pharmacological studies of malaria in pregnancy,...
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Pharmacologicalstudiesofmalariainpregnancy,infancyandchildhoodinPapuaNewGuinea
Sam Salman MBBS (Hons)
This thesis is presented for the degree of Doctor of Philosophy of the University of Western Australia as a component of a MBBS/PhD combined degree
School of Medicine and Pharmacology 2012
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Dedicated to the Baha’i youth in Iran, who continue to be denied access to tertiary education
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Declaration
This thesis contains the details of five published pharmacokinetic studies performed in Papua
New Guinea. The majority of the work in four of the five studies (Chapters 2, 3, 4 and 5) was
performed by Sam Salman, while his contribution to the fifth (Chapter 6) was substantial. This
contribution is indicated by first authorship on the first four works P
1‐4P and second authorship of
the final P
5P. The contribution of authors to each of the studies is detailed in section i (page VII)
and an acknowledgement to all those involved in the work relating to the thesis is provided in
section ii (page XIII).The co‐authors of these papers have given permission for them to be
included in this thesis.
This thesis is presented for the Doctor of Philosophy component of a combined Bachelor of
Medicine and Bachelor of Surgery/ Doctor of Philosophy (MBBS/PhD) degree at the University
of Western Australia in the School of Medicine and Pharmacology. This degree comprised two
full‐time research years in 2007 and 2008 and then part‐time research combined with the last
three years of the full‐time MBBS course. Academic supervision for this work was provided by
Winthrop Professor Timothy M. E. Davis and Emeritus Professor Kenneth F. Ilett as
coordinating and secondary supervisors, respectively. No part of this thesis has been
presented for a degree at the University of Western Australia or any other university.
In addition to the publications included as a part of this thesis, the candidate also contributed
to other published works during the time of his candidature listed in section ii page (XVIII).
These were not included in this thesis as either the contribution (primarily population
pharmacokinetic analysis) was not as significant as those included in the thesis or the
publications were not related to malaria, the pharmacology of which is the topic of this thesis.
________________________________ __________
Candidate Signature Date
Dr Sam Salman
_________________________________ __________
Coordinating supervisor Signature Date
W/Prof Timothy M E Davis
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i 139B139BAuthorcontributionsandethicalapprovalsandfunding
i.a 151B151BPharmacokineticPropertiesofAzithromycininPregnancy
Percentage Author name Nature of contribution
50.0% Sam Salman Responsible for study concept
Responsible for study design
Responsible for ethical approval
Supervised and performed field work
Developed the assay for azithromycin
Performed the azithromycin assays
Developed and interpreted the pharmacokinetic model
Analysed and interpreted the clinical data
Prepared the first draft of the manuscript
4.0% Stephen J Rogerson Responsible for study concept
3.0% Kay Kose Performed field work
3.0% Susan Griffin Performed field work
3.0% Servina Gomorai Performed field work
3.0% Francesca Baiwog Performed field work
3.0% Josephine Winmai Performed field work
3.0% Josin Kandai Performed field work
3.0% Harin A Karunajeewa Responsible for study concept
Assisted with the writing of the manuscript
4.0% Sean J O’Halloran Developed the assay for azithromycin
Assisted with the writing of the manuscript
3.0% Peter Siba Responsible of translation of study findings in to policy in Papua New Guinea
6.0% Kenneth F Ilett
(Secondary PhD supervisor)
Responsible for study concept
Assisted with the pharmacokinetic model
Assisted with the writing of the manuscript
4.0% Ivo Mueller Responsible for study concept
Assisted with the writing of the manuscript
5.0% Timothy M E Davis
(Coordinating PhD supervisor)
Responsible for study concept
Responsible for study design
Responsible for ethical approval
Assisted with the writing of the manuscript
Funding sources: National Health and Medical Research Council (NHMRC) of Australia (grant
458555) and was supported and endorsed by the MiP consortium, which is funded through a
grant from the Bill and Melinda Gates Foundation to the Liverpool School of Tropical Medicne.
Ethical approvals: Medical Research Advisory Committee of Papua New Guinea (reference
07.24) and Human Ethics Research Committee at the University of Western Australia
(reference RA/4/1/1871).
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i.b 152B152BPharmacokineticPropertiesofConventionalandDouble‐Dose
Sulfadoxine‐PyrimethamineGivenasIntermittentPreventive
TreatmentinInfancy
Percentage Author name Nature of contribution
55.0% Sam Salman Responsible for study concept
Responsible for study design
Responsible for ethical approval
Supervised and performed field work
Adapted previously published assays for pyrimethamine, sulfadoxine and N‐acetyl‐sulfadoxine
Performed the pyrimethamine, sulfadoxine and N‐acetyl‐sulfadoxine assays
Developed and interpreted the pharmacokinetic model
Analysed and interpreted the clinical data
Prepared the first draft of the manuscript
3.0% Susan Griffin Performed field work
3.0% Kay Kose Performed field work
3.0% Nolene Pitus Performed field work
3.0% Josephine Winmai Performed field work
5.0% Brioni Moore Supervised and performed field work
3.0% Peter Siba Responsible for translation of study findings in to policy in Papua New Guinea
8.0% Kenneth F Ilett
(Secondary PhD supervisor)
Responsible for study concept
Responsible for study design
Assisted with the pharmacokinetic model
Assisted with the writing of the manuscript
5.0% Ivo Mueller Responsible for study concept
Assisted with the writing of the manuscript
12.0% Timothy M E Davis
(Coordinating PhD supervisor)
Responsible for study concept
Responsible for study design
Responsible for ethical approval
Assisted with the writing of the manuscript
Funding: IPTi Consortium and facilities utilised were developed with support from the National
Health and Medical Research Council (NHMRC) of Australia (grant 458555).
Ethical approval: Medical Research Advisory Committee of Papua New Guinea (reference
08.04).
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i.c 153B153BPopulationPharmacokineticsofArtemether,Lumefantrine,and
TheirRespectiveMetabolitesinPapuaNewGuineanChildrenwith
UncomplicatedMalaria
Percentage Author name Nature of contribution
55.0% Sam Salman Responsible for study concept
Responsible for study design
Responsible for ethical approval
Supervised and performed field work
Adapted previously published assay for lumefantrine
Developed the assay for desbutyl‐lumefantrine
Performed the lumefantrine and desbutyl‐lumefantrine assays
Developed and interpreted the pharmacokinetic model
Analysed and interpreted the clinical data
Prepared the first draft of the manuscript
7.0% Madhu Page‐Sharp Adapted previously published assay for artemether and dihydroartemisinin
Performed the artemether and dihydroartemisinin assays
Assisted with the writing of the manuscript
3.0% Susan Griffin Performed field work
3.0% Kay Kose Performed field work
3.0% Peter Siba Responsible of translation of study findings in to policy in Papua New Guinea
10.0% Kenneth F Ilett
(Secondary PhD supervisor)
Responsible for study concept
Responsible for study design
Assisted with the pharmacokinetic model
Assisted with the writing of the manuscript
5.0% Ivo Mueller Responsible for study concept
Assisted with the writing of the manuscript
14.0% Timothy M E Davis
(Coordinating PhD supervisor)
Responsible for study concept
Responsible for study design
Responsible for ethical approval
Assisted with the writing of the manuscript
Funding: The National Health and Medical Research Council (NHMRC) of Australia (grant
634343).
Ethical approval: Medical Research Advisory Committee of Papua New Guinea (reference
05.02).
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i.d 154B154BApharmacokineticcomparisonoftwopiperaquine‐containing
artemisinincombinationtherapiesinPapuaNewGuineanchildren
withuncomplicatedmalaria
Percentage Author name Nature of contribution
50.0% Sam Salman Responsible for study concept
Responsible for study design
Responsible for ethical approval
Supervised and performed field work
Performed the more recent piperaquine assays
Developed and interpreted the pharmacokinetic model
Analysed and interpreted the clinical data
Prepared the first draft of the manuscript
10.0% Madhu Page‐Sharp Developed the original assay for piperaquine
Performed the original group piperaquine assays
Developed the assay for artemisinin
Performed the artemisinin assays
Assisted with the writing of the manuscript
4.0% Kevin T Batty Assisted with assay development for artemisinin
Assisted with the writing of the manuscript
3.0% Kay Kose Performed field work
3.0% Susan Griffin Performed field work
3.0% Peter Siba Responsible of translation of study findings in to policy in Papua New Guinea
10.0% Kenneth F Ilett
(Secondary PhD supervisor)
Responsible for study concept
Assisted with the pharmacokinetic model
Assisted with the writing of the manuscript
5.0% Ivo Mueller Responsible for study concept
Assisted with the writing of the manuscript
12.0% Timothy M E Davis
(Coordinating PhD supervisor)
Responsible for study concept
Responsible for study design
Responsible for ethical approval
Assisted with the writing of the manuscript
Funding: The National Health and Medical Research Council (NHMRC) of Australia (grant
634343).
Ethical approval: Medical Research Advisory Committee of Papua New Guinea (reference
05.02).
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i.e 155B155BArtemisinin‐naphthoquinecombinationtherapyforuncomplicated
paediatricmalaria:Apharmacokineticstudy
Percentage Author name Nature of contribution
35.0% Kevin T Batty Responsible for study concept
Assisted with assay development for artemisinin and naphthoquine
Developed and interpreted the original non‐compartmental pharmacokinetic model
Prepared the first draft of the manuscript
30.0% Sam Salman Responsible for study design
Supervised and performed field work
Developed and interpreted the population pharmacokinetic model
Prepared the first draft of the manuscript
4.5% Brioni R Moore Responsible for ethical approval
Supervised and performed field work
Assisted with the writing of the manuscript
3.0% John Benjamin Supervised and performed field work
Assisted with the writing of the manuscript
4.5% Sook Ting Lee Developed the original naphthoquine assay
Performed the original naphthoquine assays
Supervised and performed field work
4.5% Madhu Page‐Sharp Developed the artemisinin and naphthoquine assay
Performed the artemisinin and naphthoquine assays
Assisted with the writing of the manuscript
2.0% Nolene Pitus Performed field work
3.0% Kenneth F Ilett
(Secondary PhD supervisor)
Assisted with the population pharmacokinetic model
Assisted with the writing of the manuscript
2.0% Ivo Mueller Responsible for study concept
2.0% Francis W Hombhanje Responsible for study concept
2.0% Peter Siba Responsible of translation of study findings in to policy in Papua New Guinea
7.5% Timothy M E Davis
(Coordinating PhD supervisor)
Responsible for study concept
Responsible for study design
Responsible for ethical approval
Assisted with the writing of the manuscript
Funding: The National Health and Medical Research Council (NHMRC) of Australia (grant
634343).
Ethical approval: Medical Research Advisory Committee of Papua New Guinea (reference
05.02).
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ii 140B140BAcknowledgements
First and foremost I would like to thank my wife, Lila, who when I embarked on this
combined degree was a friend and has been there for me on every stage of this
journey. Thank you for your patience, support and love, without it I couldn’t have
finished this work.
I would also like to acknowledge my mother and my sister who have always been there
for me and my father, who taught me to always search for new knowledge. A big thank
you to my grandmother, Iran Rezvani, the matriarch of my family, your strength flows
down to us all.
To my extended family and my friends, thank you for your encouragement and well
wishes.
Clinical studies such as those presented in this thesis are not possible with the
contribution of a range of people; I wish to thank them below:
My supervisors Winthrop Professor Timothy M. E. Davis and Emeritus Professor
Kenneth F. Ilett, for your guidance, wisdom and mentorship and for allowing
me to be a part of your research team, it has been a privilege and a pleasure. I
hope that there will be many more years of friendship and work together.
The field team at Alexishafen Health Centre in Papua New Guinea who did the
difficult job of data collection, Susan Griffin, Kay Kose, Servina Gomorai, Nolene
Pitus, Josephine Winmai, Francesca Baiwog, Josin Kandai [nurses], Christine
Kalopo [microscopist] and Bernard (“Ben”) Maamu [driver] for your hard work,
commitment to the work and acceptance into your team.
Dr Madhu Page‐Sharp, research fellow at UWA and Curtin University, for your
guidance with HPLC, assay development and for your generosity in performing
some of the drug assays required for this thesis.
Dr Laurens Manning for your onsite clinical supervision, mentorship with my
medical degree and also for your friendship. You invited me into your home
and made me feel like a part of your own family.
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Dr Harin Karunajeewa for your guidance with respect to the field work and for
your role in co‐ordinating the original piperaquine study.
Dr Sean O’Halloran, Laboratory Manager at PathWest ‐ Clinical Pharmacology
and Toxicology Laboratory, for your guidance with LC‐MS/MS and for allowing
me to perform my assays around the already busy schedule for routine clinical
samples on the LC‐MS/MS equipment.
Dr Moses Laman and Dr Michele Senn for your onsite clinical supervision.
Dr Brioni Moore, Medical Scientist at Curtin University, for your help in
completing recruitment in the infant trial and for co‐ordinating the latter
naphthoquine trial.
Dr Sook Ting Lee for your work in the original Naphthoquine trial and for being
a lab buddy in the first year of the research.
Professor Stephen Rogerson, University of Melbourne, for your expert advice in
the azithromycin trial.
Associate Professor Kevin Batty, Curtin University, for supervising the use of the
LC‐MS at Curtin University School of Pharmacy.
Mary Anne Townsend, Senior Medical Scientist at PathWest ‐ Biochemistry
Department, for your assistance in biochemical assays for the study in infants.
Dr Ivo Muller, the then head of Vector Borne Diseases at Papua New Guinea
Institute of Medical Research, Dr Peter Siba, director of the Papua New Guinea
Institute of Medical Research, and to John Taime, Yagaum site manager for
Papua New Guinea Institute of Medical Research, for welcoming me into your
country, your assistance in providing local resources and support in my time in
Papua New Guinea.
The microscopy and data entry teams at the Yagaum branch of the Papua New
Guinea Institute of Medical Research for your work in providing and managing
data essential to the studies.
Sr Valsi Kurian and the staff of Alexishafen Health Centre for your kind co‐
operation and allowing us to use your facilities for the studies.
The Faculty of Medicine, Dentistry and Health Sciences (in particular Dr Jan
Dunphy) for establishing an A & A Saw Scholarship to assist me in the combined
years of the degree and in conjunction with the Raine Medical Research
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Foundation for awarding me with a MBBS/PhD scholarship in the full time years
of the research.
And finally a big thank you to participants of the studies, their families and their
communities for agreeing to be a part of this research, without whom there
would be nothing to report.
My thoughts are with the family of Servina Gomorai, dedicated research nurse, who
died of cancer during the course of this thesis.
My thesis is dedicated to the Baha’i youth in Iran, who continue to be denied access to
tertiary education. Were it not for the courage of my parents to escape and find refuge
in Australia, I would be numbered with them and would not have had this wonderful
adventure.
Some members the field team in the study clinic in Alexishafen. (L‐R) Sitting: Christine Kalopo, Josephine Winmai, Susan Griffin, Kay Kose, Servina Gomorai. Standing: Sam Salman, Nolene Pitus, Ben Maamu.
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iii 141B141BPublications,presentationsandprizes
iii.a 156B156BPublicationsrelatedtothesis
Salman, S., S. J. Rogerson, K. Kose, S. Griffin, S. Gomorai, F. Baiwog, J. Winmai, J. Kandai, H. A.
Karunajeewa, S. J. O'Halloran, P. Siba, K. F. Ilett, I. Mueller, and T. M. Davis. 2010.
Pharmacokinetic properties of azithromycin in pregnancy. Antimicrob Agents Chemother
54:360‐6. (Chapter 2)
Salman, S., S. Griffin, K. Kose, N. Pitus, J. Winmai, B. Moore, P. Siba, K. F. Ilett, I. Mueller, and T.
M. Davis. 2011. The pharmacokinetic properties of conventional and double‐dose sulfadoxine‐
pyrimethamine given as intermittent preventive treatment in infancy. Antimicrob Agents
Chemother. 55:1693‐700 (Chapter 3)
Salman, S., M. Page‐Sharp, S. Griffin, K. Kose, P. M. Siba, K. F. Ilett, I. Mueller, and T. M. Davis.
2011. Population pharmacokinetics of artemether, lumefantrine, and their respective
metabolites in Papua New Guinean children with uncomplicated malaria. Antimicrob Agents
Chemother 55:5306‐13. (Chapter 4)
Salman, S., M. Page‐Sharp, K. T. Batty, K. Kose, S. Griffin, P. Siba, K. F. Ilett, I. Mueller, and T. M.
E. Davis. 2012. A pharmacokinetic comparison of two piperaquine‐containing artemisinin
combination therapies in Papua New Guinean children with uncomplicated malaria.
Antimicrob Agents Chemother. 56:3288‐97 (Chapter 5)
Batty, K. T., S. Salman, B. R. Moore, J. Benjamin, S. T. Lee, M. Page‐Sharp, N. Pitus, K. F. Ilett, I.
Mueller, F. W. Hombhanje, P. Siba, and T. M. Davis. 2012. Artemisinin‐naphthoquine
combination therapy for uncomplicated pediatric malaria: A pharmacokinetic study.
Antimicrob Agents Chemother. 56:2472‐84 (Chapter 6)
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iii.b 157B157BOtherpublicationsduringcandidature
Salman, S. *, B. Hullet*, S. J. O'Halloran, D. Peirce, K. Davies, and K. F. Ilett. 2012. Development
of a Population Pharmacokinetic Model for Parecoxib and Its Active Metabolite Valdecoxib
After Parenteral Parecoxib Administration in Children. Anesthesiology 116. (*equal first
authorship)
Salman, S., S. K. Sy, K. F. Ilett, M. Page‐Sharp, and M. J. Paech. 2011. Population
pharmacokinetic modelling of tramadol and its O‐desmethyl metabolite in plasma and breast
milk. Eur J Clin Pharmacol.
Paech, M. J., S. Salman, K. F. Ilett, S. J. O'Halloran, and N. A. Muchatuta. 2012. Transfer of
Parecoxib and Its Primary Active Metabolite Valdecoxib via Transitional Breastmilk Following
Intravenous Parecoxib Use After Cesarean Delivery: A Comparison of Naive Pooled Data
Analysis and Nonlinear Mixed‐Effects Modeling. Anesth Analg 114:837‐44.
Karunajeewa, H. A., S. Salman, I. Mueller, F. Baiwog, S. Gomorrai, I. Law, M. Page‐Sharp, S.
Rogerson, P. Siba, K. F. Ilett, and T. M. Davis. 2009. Pharmacokinetic properties of sulfadoxine‐
pyrimethamine in pregnant women. Antimicrob Agents Chemother 53:4368‐76.
Karunajeewa, H. A., S. Salman, I. Mueller, F. Baiwog, S. Gomorrai, I. Law, M. Page‐Sharp, S.
Rogerson, P. Siba, K. F. Ilett, and T. M. Davis. 2010. Pharmacokinetics of chloroquine and
monodesethylchloroquine in pregnancy. Antimicrob Agents Chemother 54:1186‐92.
Wong, R. P., S. Salman, K. F. Ilett, P. M. Siba, I. Mueller, and T. M. Davis. 2011. Desbutyl‐
lumefantrine is a metabolite of lumefantrine with potent in vitro antimalarial activity that may
influence artemether‐lumefantrine treatment outcome. Antimicrob Agents Chemother
Benjamin, J., B. Moore, S. T. Lee, M. Senn, S. Griffin, D. Lautu, S. Salman, P. Siba, I. Mueller, and
T. M. Davis. 2012. Artemisinin‐naphthoquine combination therapy for uncomplicated
paediatric malaria: A tolerability, safety and preliminary efficacy study. Antimicrob Agents
Chemother. 56:2465‐71
Manning, L., M. Laman, M. Page‐Sharp, S. Salman, I. Hwaiwhanje, N. Morep, P. Siba, I. Mueller,
H. A. Karunajeewa, and T. M. Davis. 2011. Meningeal inflammation increases artemether
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concentrations in cerebrospinal fluid in Papua New Guinean children treated with
intramuscular artemether. Antimicrob Agents Chemother 55:5027‐33.
Karunajeewa, H. A., I. Mueller, M. Senn, E. Lin, I. Law, P. S. Gomorrai, O. Oa, S. Griffin, K. Kotab,
P. Suano, N. Tarongka, A. Ura, D. Lautu, M. Page‐Sharp, R. Wong, S. Salman, P. Siba, K. F. Ilett,
and T. M. Davis. 2008. A trial of combination antimalarial therapies in children from Papua
New Guinea. N Engl J Med 359:2545‐57.
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iii.c 158B158BPosterpresentations
Salman, S., M. Page‐Sharp, S. Griffin, K. Kose, I. Mueller, K. F. Ilett, and T. M. E. Davis. 2011.
Population Pharmacokinetics of artemether, lumefantrine and their respective metabolites in
Papua New Guinean children with uncomplicated malaria, Australasian Society of Clinical and
Experimental Pharmacologists and Toxicologists (ASCEPT), Perth.
Salman, S., M. Page‐Sharp, S. Griffin, K. Kose, N. Pitus, J. Winmai, B. Moore, P. Siba, K. F. Ilett, I.
Mueller, and T. M. Davis. 2011. The pharmacokinetic properties of standard and double dose
sulfadoxine‐pyrimethamine(Fansidar®) in infants, Students in Health and Medical Research
Conference (SHMRC), Perth.
Salman, S., M. Page‐Sharp, S. Griffin, K. Kose, N. Pitus, J. Winmai, B. Moore, P. Siba, K. F. Ilett, I.
Mueller, and T. M. Davis. 2011. The pharmacokinetic properties of standard and double dose
sulfadoxine‐pyrimethamine(Fansidar®) in infants, UWA School of Medicine and Pharmacology
Annual Research Symposium, Perth.
Salman, S., H. Karunajeewa, I. Law, I. Muller, T. M. E. Davis, and K. F. Ilett. 2009.
Pharmacokinetics of Chloroquine in Pregnant and Non‐pregnant Women in Papua New
Guinea, Population Approach Group in Australia and New Zealand (PAGANZ), Newcastle.
Salman, S., S. Rogerson, S. J. O’Halloran, I. Muller, T. M. E. Davis, and K. F. Ilett. 2009.
Pharmacokinetic properties of azithromycin in pregnancy, Students in Health and Medical
Research Conference (SHMRC), Perth.
Salman, S., S. Rogerson, S. J. O’Halloran, I. Muller, T. M. E. Davis, and K. F. Ilett. 2009.
Pharmacokinetic properties of azithromycin in pregnancy, UWA School of Medicine and
Pharmacology Annual Research Symposium, Perth.
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iii.d 159B159BPrizes
Finalist for Neville Percy poster prize at Australasian Society of Clinical and Experimental
Pharmacologists and Toxicologists (ASCEPT 2011) for poster: “Population Pharmacokinetics of
artemether, lumefantrine and their respective metabolites in Papua New Guinean children
with uncomplicated malaria”
Best Poster Presentation by a Postgraduate Student/Post doctorate at UWA School of
Medicine and Pharmacology Annual Research Symposium 2011 for poster: “Population
pharmacokinetics of artemether, lumefantrine and their respective metabolites in Papua New
Guinean children with uncomplicated malaria”
Special commendation in Higher Degree by Research Achievements awards (Clinical Medicine
and Dentistry discipline) at UWA for publication: “Pharmacokinetic properties of azithromycin
in pregnancy”
Best Clinical Research Poster Presentation by a Postgraduate Student/Post doctorate at UWA
School of Medicine and Pharmacology Annual Research Symposium 2009 for poster:
“Pharmacokinetic properties of azithromycin in pregnancy”
Best Methodology and Study Design at Students in Health and Medical Research Conference
2009 for poster: “Pharmacokinetic properties of azithromycin in pregnancy”
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iv 142B142BAbstract
With half the world’s population still at risk of malaria, it remains one of the most
important global health concerns. In highly endemic areas such as coastal Papua New
Guinea (PNG), adults develop immunity to symptomatic infection, while pregnant
women, infants and children bear the burden of clinical disease. Antimalarial drugs still
play an important role in the treatment and prevention of malaria. In pregnancy and
infancy prevention of disease is afforded, to some extent, by the use of Intermittent
Preventive Treatment (IPT). In childhood, effective treatments are required to prevent
recrudescence and early re‐infection. To enable optimal dosing of pharmacological
therapy, studies performed in these specific at‐risk groups are required.
This primary aims of this thesis were, in samples of at‐risk populations, to describe the
pharmacokinetic (PK) properties of a number of antimalarial drugs using a population
approach and to provide preliminary information regarding their efficacy, safety and
tolerability. These studies were intended to guide future large clinical trials and assist
in determining health policies.
The first of these studies evaluated the PK of azithromycin (AZI) in pregnant and non‐
pregnant women. AZI is one of the few antimalarial drugs known to be safe in
pregnancy and it is conventionally given with chloroquine or sulfadoxine‐
pyrimethamine (SP). The effect of pregnancy on the PK parameters of AZI was not
large enough to justify a dose adjustment. A timed single blood sample that could be
used as a surrogate for overall exposure was identified. The preliminary tolerability
and efficacy data from this study were used in developing the drug regimen for a large
study of IPT in pregnancy currently underway in PNG.
Infants were the participants in the second study in which the PK of conventional and
double dose SP were investigated. Hepatic and renal maturation were incorporated
into the PK model for pyrimethamine, sulfadoxine and N‐acetylsulfadoxine (a
metabolite of sulfadoxine). Exposure was significantly higher in the double dose group
despite a slight reduction in the relative bioavailability of sulfadoxine. Preliminary data
on the safety of a double dose were obtained. The findings support the evaluation of
the efficacy of a double dose regimen for IPT in infants.
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The last three studies were performed in children and evaluated the use of several
artemisinin combination therapies (ACTs) namely, artemether/lumefantrine (AL),
artemisinin/piperaquine base (ART/PQ)and artemisinin/naphthoquine (ART/NQ).
AL was the first ACT recommended by the World Health Organisation (WHO). The
study found that a subset of children may be under‐dosed as higher mg/kg doses were
required to produce the same exposure to lumefantrine, artemether and
dihydroartemisinin (DHA, an active metabolite of artemether) when compared with
adult doses. It was the first study to describe the population PK of desbutyl‐
lumefantrine, an active metabolite of lumefantrine. The results from this study were
taken into consideration when AL was chosen as first‐line in the treatment of
uncomplicated malaria in PNG.
The next study in this set assessed ART/PQ, an ACT not yet recommended by the WHO
but available in the private sector. The PK of PQ in this combination were compared to
those of a historical study of DHA/PQ phosphate, which had been performed at the
same location. Although there were no clinically significant differences in PQ PK
between formulations, the low ART dose and the reduced ART exposure with
successive doses raise concerns regarding the use of this combination. The results of
this study suggest that an extended dose regimen should be investigated.
The final study was a PK evaluation of ART/NQ, another ACT commercially available
but not yet recommended by the WHO. Three distinct dose regimens were tested in
similar samples of children, and the PK differences between them were analysed. This
study provided the first information of NQ disposition in children as well as providing
additional data on the PK of multiple doses of ART. ART/NQ dosing in a large scale
efficacy trial that is currently being carried out in PNG was based on the PK data from
this study.
In summary, this thesis describes studies in samples of at‐risk individuals in PNG that
add vital information to an often sparse literature on the pharmacology of these
important antimalarial drugs. Future studies of these treatments will be enhanced as a
result.
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v 143B143BTableofcontents
8Ti8T 8TAuthor contributions and ethical approvals and funding8T .................................................................. VII
8Tii8T 8TAcknowledgements8T .......................................................................................................................... XIII
8Tiii8T 8TPublications, presentations and prizes8T ............................................................................................ XVII
8Tiv8T 8TAbstract8T........................................................................................................................................... XXIII
8Tv8T 8TTable of contents8T ........................................................................................................................... XXVII
8Tvi8T 8TAbbreviations8T .................................................................................................................................. XXXI
8Tvii8T 8TAntimalarial drugs and combinations used in this thesis8T .............................................................. XXXV
8Tviii8T 8TList of Tables8T ................................................................................................................................ XXXVII
8Tix8T 8TList of Figures8T ................................................................................................................................ XXXIX
8Tx8T 8TPreface8T ............................................................................................................................................. XLIII
8T18T 8TGeneral Introduction8T ........................................................................................ 1
8T1.18T 8TMalaria 8T ................................................................................................................................... 1
8T1.1.18T 8TGlobal snapshot 8T .................................................................................................................. 1
8T1.1.28T 8TParasitology8T ........................................................................................................................ 1
8T1.1.38T 8TMalaria in Papua New Guinea 8T ............................................................................................ 3
8T1.1.48T 8TPrevention of Malaria in Pregnancy8T.................................................................................... 5
8T1.1.58T 8TPrevention of Malaria in infancy8T ......................................................................................... 7
8T1.1.68T 8TTreatment of Malaria in childhood 8T ..................................................................................... 9
8T1.28T 8TAntimalarial drugs8T ................................................................................................................ 15
8T1.2.18T 8TArtemisinin and artemisinin derivatives8T ........................................................................... 15
8T1.2.28T 8TArylamino alcohols8T ............................................................................................................ 22
8T1.2.38T 8T4‐aminoquinilones 8T ............................................................................................................ 27
8T1.2.48T 8TAntifolate drugs8T ................................................................................................................ 35
8T1.2.58T 8TAntibiotics8T ......................................................................................................................... 38
8T1.38T 8TPharmacokinetics8T ................................................................................................................. 43
8T1.3.18T 8TIntroduction8T ...................................................................................................................... 43
8T1.3.28T 8TPopulation pharmacokinetics8T ........................................................................................... 49
8T1.3.38T 8TNONMEM8T .......................................................................................................................... 53
8T1.3.48T 8TPharmacokinetic considerations in specific populations8T .................................................. 59
8T1.48T 8TThesis outline8T ....................................................................................................................... 65
8TPREVENTIONOFMALARIAINPREGNANTWOMEN 8T.................................................69
8T28T 8TPharmacokinetic Properties of Azithromycin in Pregnancy 8T ............................ 71
8T2.18T 8TBackground8T ........................................................................................................................... 71
8T2.28T 8TPublication8T ............................................................................................................................ 73
8T2.2.18T 8TAbstract8T ............................................................................................................................. 73
8T2.2.28T 8TIntroduction8T ...................................................................................................................... 74
XXVIII
8T2.2.38T 8TPatients and methods8T ....................................................................................................... 75
8T2.2.48T 8TResults8T ............................................................................................................................... 79
8T2.2.58T 8TDiscussion8T .......................................................................................................................... 84
8T2.2.68T 8TAcknowledgements8T ........................................................................................................... 87
8TPREVENTIONOFMALARIAININFANTS 8T.......................................................................89
8T38T 8TPharmacokinetic Properties of Conventional and Double‐Dose Sulfadoxine‐
Pyrimethamine Given as Intermittent Preventive Treatment in Infancy8T ................... 91
8T3.18T 8TBackground8T ........................................................................................................................... 91
8T3.28T 8TPublication8T ............................................................................................................................ 93
8T3.2.18T 8TAbstract8T ............................................................................................................................. 93
8T3.2.28T 8TIntroduction8T ....................................................................................................................... 94
8T3.2.38T 8TPatients and methods8T ....................................................................................................... 95
8T3.2.48T 8TResults8T ............................................................................................................................. 100
8T3.2.58T 8TDiscussion8T ........................................................................................................................ 108
8T3.2.68T 8TAcknowledgements8T ......................................................................................................... 112
8TTREATMENTOFUNCOMPLICATEDMALARIAINCHILDREN8T.............................115
8T48T 8TPopulation Pharmacokinetics of Artemether, Lumefantrine, and Their
Respective Metabolites in Papua New Guinean Children with Uncomplicated Malaria8T117
8T4.18T 8TBackground8T ......................................................................................................................... 117
8T4.28T 8TPublication8T .......................................................................................................................... 119
8T4.2.18T 8TAbstract8T ........................................................................................................................... 119
8T4.2.28T 8TIntroduction8T ..................................................................................................................... 120
8T4.2.38T 8TPatients and methods8T ..................................................................................................... 121
8T4.2.48T 8TResults8T ............................................................................................................................. 127
8T4.2.58T 8TDiscussion8T ........................................................................................................................ 135
8T4.2.68T 8TAcknowledgements8T ......................................................................................................... 140
8T58T 8TA Pharmacokinetic Comparison of Two Piperaquine‐Containing Artemisinin
Combination Therapies in Papua New Guinean Children with Uncomplicated Malaria8T143
8T5.18T 8TBackground8T ......................................................................................................................... 143
8T5.28T 8TPublication8T .......................................................................................................................... 145
8T5.2.18T 8TAbstract8T ........................................................................................................................... 145
8T5.2.28T 8TIntroduction8T ..................................................................................................................... 146
8T5.2.38T 8TPatients and methods8T ..................................................................................................... 147
8T5.2.48T 8TResults8T ............................................................................................................................. 154
XXIX
8T5.2.58T 8TDiscussion8T ....................................................................................................................... 162
8T5.2.68T 8TAcknowledgements8T ........................................................................................................ 167
8T68T 8TArtemisinin‐Naphthoquine Combination Therapy for Uncomplicated
Paediatric Malaria: A Pharmacokinetic Study 8T ......................................................... 169
8T6.18T 8TBackground8T ......................................................................................................................... 169
8T6.28T 8TPublication8T .......................................................................................................................... 171
8T6.2.18T 8TAbstract8T ........................................................................................................................... 171
8T6.2.28T 8TIntroduction8T .................................................................................................................... 173
8T6.2.38T 8TPatients and methods8T ..................................................................................................... 175
8T6.2.48T 8TResults8T ............................................................................................................................. 183
8T6.2.58T 8TDiscussion8T ....................................................................................................................... 194
8T6.2.68T 8TAcknowledgements8T ........................................................................................................ 199
8T6.2.78T 8TConflict of interest statement8T ......................................................................................... 199
8T78T 8TGeneral Discussion8T ....................................................................................... 201
8T7.18T 8TSignificance of findings 8T ....................................................................................................... 202
8T7.1.18T 8TPrevention of malaria in pregnancy8T ................................................................................ 202
8T7.1.28T 8TPrevention of malaria in infancy8T ..................................................................................... 203
8T7.1.38T 8TTreatment of uncomplicated malaria in children8T ........................................................... 204
8T7.28T 8TImprovements and future directions8T ................................................................................. 207
8T7.2.18T 8TPrevention of malaria in pregnancy8T ................................................................................ 208
8T7.2.18T 8TPrevention of malaria in infancy8T ..................................................................................... 209
8T7.2.28T 8TTreatment of uncomplicated malaria in children8T ........................................................... 210
8Tx8T 8TReferences8T ....................................................................................................................................... 213
8Txi8T 8TAppendix8T .......................................................................................................................................... 239
XXX
XXXI
vi 144B144BAbbreviations
µg ............... microgram(s)
µl ................ microliter(s)
ACT ............. Artemisinin Combination Therapy
AL ............... artemether/lumefantrine
AQ .............. amodiaquine
ARM ............ artemether
ARTS ........... artesunate
ART ............. artemisinin
AUC ............ area under the curve
AZI .............. azithromycin
BLQ ............. below the limit of quantification
BOV ............ between occasion variability
BSA ............. body surface area
BSV ............. between subject varibility
CI ................ confidence interval(s)
CL ................ clearance
CLRHR .............. hepatic clearance
CLRRR .............. renal clearance
CLRTR .............. total clearance
CQ ............... chloroquine
CWRES ........ conditional wieghted residuals
CysC ........... Cystatin C
DBL ............. desbutyl‐lumefantrine
DHA ............ dihydroartemisinin
DHFR .......... dihydrofolate reductase
DHPS ........... dihydropterate synthase
dl ................ decilitre(s)
ECR50R ............ half maximal effective concentration
FP ................ first‐pass
g .................. gram(s)
GFR ............. glomerular filtration rate
GOF ............ goodness‐of‐fit
h ................. hour(s)
Hb ............... haemoglobin
HPLC ........... high‐performance liquid chromatography
ICR50R ............. half maximal inhibitory concentration
XXXII
IIV ................ inter‐individual variability
IOV .............. inter‐occasion variavility
IPT ............... Intermittent Preventive Treatment
IPTi .............. Intermittent Preventive Treatment in infancy
IPTp ............. Intermittent Preventive Treatment in pregnancy
IQR .............. inter‐quartile range
kRaR................. aborption rate constant
kg ................ kilogram(s)
kRtrR ................ transit compartment rate
l ................... litre(s)
L .................. likelihood
LC‐MS .......... liquid chromatography mass spectrometry
LC‐MS/MS ... liquid chromatography‐tandem mass spectrometry
LRT .............. likelihood ratio test
LUM ............ lumefantrine
M ................. moles per litre
mg ............... milligram(s)
ml ................ millilitre(s)
MQ .............. mefloquine
MTT ............. mean transit time
ng ................ nanogram(s)
NN ............... number of transit compartments
NPC ............. numerical predictive check
NPD ............. naive pooled data
NQ ............... naphthoquine
NSX ............. NR4R‐acetylsulfadoxine
OFV ............. objective function value
PETIA ........... particle enhanced immunoturbidimetry
PCR .............. polymerase chain reaction
pcVPC .......... prediction corrected visual predictive check
PI ................. prediction interval(s)
PK ................ pharmacokinetic(s)
PNA ............. postnatal age
PNG ............. Papua New Guinea
PQ ............... piperaquine
PYR .............. pyrimethamine
QC ............... quality control
XXXIII
QN .............. quinine
R ................. correlation coefficient
RBC ............. red blood cell
RSD ............. relative standard deviation
RSE ............. relative standard error
RUV ............ residual unexplained variability
SD ............... standard deviation
SDX ............. sulfadoxine
SP ................ sulfadoxine/pyrimethamine
STS .............. standard two stage
tR½R ............... half‐life
UPLC ........... ultra high‐performance liquid chromatography
UV ............... ultraviolet
V ................. volume of distribution
VPC ............. visual predictive check
vs. ............... versus
VRssR ............... volume of distribution at steady state
WHO ........... World Health Organisation
WRES .......... weighted residuals
WT .............. body weight
XXXIV
XXXV
vii 145B145BAntimalarialdrugsandcombinationsusedinthisthesis
19TTrade name 19TGeneric name(s) of component(s) 19TAmount per tablet
19TZithromax® 19Tazithromycin 19T500 mg
19TFansidar® sulfadoxine/pyrimethamine 19T500/25 mg
19TCoartem® artemether/lumefantrine 19T20/120 mg
19TDuo‐cotecxin® 19Tdihydroartemisinin/piperaquine phosphate 19T40/320 mg
19TArtequick® 19Tartemisinin/piperaquine base 19T24/144 mgP
a
19TArco® 19Tartemisinin/naphthoquine 19T125/50 mg
P
aPSachets were used.
XXXVI
XXXVII
viii 146B146BListofTables
Table 1‐1 List of some commonly used artemisinin combination therapies .................................... 11
Table 1‐2 Change in relative bioavailability of artemisinin with consecutive dosing calculated from reported AUC values. All comparisons are with day 1 AUC. Artemisinin was given alone unless otherwise specified. ............................................................................................................... 18
Table 1‐3 Fractional difference in tR½R and AUC of artemether and dihydroartemisinin when drugs are coadministed with artemether/lumefantrine .................................................................. 21
Table 1‐4 Manufacturer’s recommended dosing of artemether/lumefantrine in children. Each tablet consists of 20mg of artemether and 120mg of lumefantrine. ............................................... 24
Table 1‐5 Fractional difference in tR½R and AUC of lumefantrine when drugs are coadministed with artemether/lumefantrine ......................................................................................................... 26
Table 1‐6 PCR‐adjusted efficacy of dihydroartemisinin/piperaquine at day 42 or afterwards in various studies. ................................................................................................................................. 29
Table 1‐7 Summary of findings of the pharmacokinetics of naphthoquine in healthy volunteers in Qu et al. P
220P ..................................................................................................................................... 33
Table 1‐8 Changes of enzymes involved in metabolism during pregnancy, adapted from Anderson 2005 P
307P. ............................................................................................................................. 60
Table 2‐1 Baseline characteristics of the study participants by pregnancy status and treatment allocation. Data are mean ± SD, median [IQR] or number (%). ........................................................ 79
Table 2‐2 Side‐effects reported during the first week after initiation of treatment. Data are numbers of patients and (%)............................................................................................................. 80
Table 2‐3 Model building, final parameter estimates and bootstrap results from the AZI population pharmacokinetic modelling. ........................................................................................... 81
Table 2‐4 Secondary pharmacokinetic parameters derived from post hoc Bayesian estimates for pregnant and non‐pregnant study participants (median [IQR]). ................................................ 83
Table 3‐1 Dosing guide for conventional and double‐dose groups with the SDX/PYR doses in mg given in parentheses. ........................................................................................................................ 95
Table 3‐2 Baseline characteristics of study participants. Data are number (%), mean±SD or median [IQR]. .................................................................................................................................. 100
Table 3‐3 Final population PK parameters and bootstrap results for PYR. .................................... 101
Table 3‐4 Post hoc Bayesian predicted PK parameters for PYR for PNG infants given conventional and double doses of SDX/PYR (median [IQR]). ......................................................... 102
Table 3‐5 Final population PK parameters and bootstrap results for SDX and NSX. Parameters for NSX modelling obtained after fixing model parameters for SDX are highlighted in grey. ........ 104
Table 3‐6 Post hoc Bayesian predicted PK parameters for SDX and NSX in PNG infants given conventional and double dosing of SDX/PYR (median [IQR]). ........................................................ 106
XXXVIII
Table 4‐1 Baseline characteristics of study participants. Data are number (%), mean ± SD or median and [inter‐quartile range]. ................................................................................................. 127
Table 4‐2 Final population pharmacokinetic estimates and bootstrap results for lumefantrine and desbutyl‐lumefantrine. ............................................................................................................ 129
Table 4‐3 Final population pharmacokinetic estimates and bootstrap results for ARM and DHA. 131
Table 4‐4 Secondary pharmacokinetic parameters derived from post hoc Bayesian estimates for study participants. Data are median [inter‐quartile range]. .................................................... 133
Table 4‐5 Summary of studies reporting area under the plasma concentration‐time curve (AUC) for lumefantrine. ............................................................................................................................ 136
Table 5‐1 Baseline characteristics of study participants. Data are number (%), mean ± SD or median [IQR]. .................................................................................................................................. 154
Table 5‐2 Final population pharmacokinetic estimates and bootstrap results for piperaquine. ... 156
Table 5‐3 Secondary pharmacokinetic parameters of piperaquine derived from post hoc Bayesian estimates for study participants, and day 7 plasma piperaquine concentrations. Data are median [inter‐quartile range]. .................................................................................................. 158
Table 5‐4 Final population pharmacokinetic estimates and bootstrap results for artemisinin (n=12). ............................................................................................................................................ 159
Table 5‐5 Secondary pharmacokinetic parameters for artemisinin derived from post hoc Bayesian estimates for study participants. Data are median [inter‐quartile range]. ..................... 160
Table 6‐1 Demographic data for children given artemisinin‐naphthoquine for the treatment of uncomplicated falciparum malaria. Data are mean ± SD unless otherwise indicated. .................. 183
Table 6‐2 Population pharmacokinetic parameters and bootstrap results for NQ in children with uncomplicated falciparum malaria. ........................................................................................ 185
Table 6‐3 Post hoc Bayesian parameter estimates and derived secondary pharmacokinetic parameters for NQ in children with uncomplicated falciparum malaria. Data are median [IQR]. 188
Table 6‐4 Population pharmacokinetic parameters and bootstrap results for ART in children with uncomplicated falciparum malaria. ........................................................................................ 191
Table 6‐5 Post hoc Bayesian parameter estimates and derived secondary pharmacokinetic parameters for artemisinin in children with uncomplicated falciparum malaria. Data are median [IQR]. All between‐group comparisons were statistically non‐significant. ....................... 193
XXXIX
ix 147B147BListofFigures
8TUFigure 1‐1 life cycle of Plasmodium in humans and mosquitoes. From http://www.malariasite.com/malaria/LifeCycle.htm.U8T ........................................................................ 2
8TUFigure 1‐2 Map of South Pacific region showing location of PNG. From http://www.wpro.who.int/internet/files/eha/toolkit/web2009/Country%20Profiles/Maps/fiji%20melanesia%20country%20map.jpg. U8T ................................................................................................ 3
8TUFigure 1‐3 Map of Papua New Guinea showing Madang. From: http://geology.com/world/papua‐new‐guinea‐map.gif U8T ..................................................................... 4
8TUFigure 1‐4 Population indices of immunity in an endemic area of P. falciparum transmission (from Langhorne et al 2008 UPU
26UPU). Change over time of various indices of malaria in a population
living in an endemic area of P. falciparum transmission: asymptomatic infection (pink), mild disease (febrile episodes caused by malaria; blue) and severe or life‐threatening disease (green). The data are normalized and are presented as the per cent of maximum cases for each population index. U8T .............................................................................................................................. 10
8TUFigure 1‐5 Artemisinin and its derivatives showing endoperoxide bridge in blue. The different functional groups at the 2‐keto position in red namely oxo for artemisinin, hydroxyl for dihydroartemesisinin, methoxy for artemether, hemisuccinate for artesunate and ethoxy for artemotil. U8T .......................................................................................................................................... 15
8TUFigure 1‐6 Regression line for artemisinin saliva and unbound venous plasma concentrations (ng/ml) in 18 male Vietnamese patients 1‐8 h after the first oral dose of 100 mg or 500 mg artemisinin. Figure 3A in Gordi et al. 2000 UPU
120UP.8T .................................................................................. 17
8TUFigure 1‐7 Measured artemether (■) and dihydroartemisinin (○) concentrations and the model‐fitted curves in a patient who received 80 mg artemether orally at 0, 8, 24 and 48 h demonstrating the time dependant changes seen in artemether and dihydroartemisinin disposition. From van Agtmael et al. UPU
139UP8T ............................................................................................ 20
8TUFigure 1‐8 Arylamino alcohols showing the similarity in structure of halofantrine and lumefantrine in blue. U8T ........................................................................................................................ 22
8TUFigure 1‐9 Some 4‐aminoquinilones antimalarials showing the common 4‐aminoquinilone group in chloroquine, amodiaquine, naphthoquine and piperaquine (a dimer).U8T ............................ 27
8TUFigure 1‐10 Antifolate antimalarial drugs.U8T ........................................................................................ 35
8TUFigure 1‐11 Antibiotics with activity against Plasmodium species.U8T .................................................. 38
8TUFigure 1‐12 One compartment model with a single output rate.U8T..................................................... 45
8TUFigure 1‐13 Two compartment open model with oral dosing and elimination from the central compartment. U8T ................................................................................................................................... 46
8TUFigure 1‐14 Two compartment open model with parenteral dosing and elimination from the central compartment parameterized in terms of clearance and volume parameters.U8T .................... 47
8TUFigure 1‐15 Example of a fitted concentration versus time profile. Observed concentrations over time (red crosses) have been fitting using a curve that is the combination of positive and negative exponentials (black line) that represent absorption and elimination processes respectively.U8T ...................................................................................................................................... 48
XL
8TUFigure 1‐16 A typical Ω matrix with variance terms, the diagonals, in blue (ω1,1 is the variance for η1), covariance terms, the off‐diagonals, in red and black (ω2,1 is the covariance between η1and η2, ω2,1 is the same as ω1,2). U8T ............................................................................................. 55
8TUFigure 1‐17 An example of the changes expected in volume, clearance and half‐life over weight and age using average weight for age data UPU
312UPU. The solid black line represents changes when
only considering allometry while the dashed red line considers both the effects of size and age.U8T 63
8TUFigure 2‐1 Structural model used in the final pharmacokinetic analysis of plasma azithromycin concentrations in the central compartment versus time.U8T ............................................................... 82
8TUFigure 2‐2(A) Population (○) and individual (●) predicted versus observed plasma azithromycin concentrations (µg/l on log10 scale) for the final model. The line of identity is also shown. (B) Weighted residuals vs. time (log scale) for azithromycin final model. U8T ............................................ 82
8TUFigure 2‐3 Visual predicted check plots showing simulated 10 UPU
thUPU (short dashed line), 50 UPU
thUPU (dotted
line) and 90 UPU
thUPU (solid line) percentile concentrations and observed concentration (log scale) data
(grey open circles) versus time (log scale) for non‐pregnant (A) and pregnant (B) participants. U8T .... 84
8TUFigure 3‐1 (A) Population (○) and individual (●) predicted versus observed plasma pyrimethamine concentrations (µg/l on log10 scale) for the final model. The line of identity is also shown. (B) Conditional weighted residuals vs. time for pyrimethamine final model. U8T ........... 103
8TUFigure 3‐2 Visual predicted check plots for PYR showing simulated 10 UPU
thUPU (short dashed line), 50 UPU
thUPU
(dotted line) and 90 UPU
thUPU (solid line) percentile concentrations and observed concentration (log
scale) data (grey open circles) versus time (log scale) for conventional dose (A) and double‐dose (B) participants. U8T ...................................................................................................................... 103
8TUFigure 3‐3 (A) Population (○) and individual (●) predicted versus observed plasma sulfadoxine concentrations (µg/l on log10 scale) for the final model. The line of identity is also shown. (B) Conditional weighted residuals vs. time (log scale) for sulfadoxine final model. U8T .......................... 105
8TUFigure 3‐4 Visual predicted check plots for SDX showing simulated 10 UPU
thUPU (short dashed line), 50 UPU
thUPU
(dotted line) and 90 UPU
thUPU (solid line) percentile concentrations and observed concentration (log
scale) data (grey open circles) versus time (log scale) for conventional dose (A) and double‐dose (B) participants. U8T ...................................................................................................................... 105
8TUFigure 3‐5 Maturation as a fraction of adult clearance for PYR (solid line) and SDX (dashed line) predicted from the PK model plotted against PMA. A box plot of the PMA in the recruited subjects is included to show its distribution in relation to maturation of clearance.U8T .................... 107
8TUFigure 4‐1 Time‐concentration plots showing LUM (○) and DBL () in μg/l on log10 scale. Curves of the median concentration for LUM (solid black line) and DBL (dashed black line) are also shown. U8T ..................................................................................................................................... 128
8TUFigure 4‐2 Population (○) and individual predicted (●) versus observed data for LUM (A) and DBL (B) concentrations (µg/l) for the final model. The lines of identity are also shown. U8T ............. 130
8TUFigure 4‐3 Visual predictive check showing observed 50th (●), 10th () and 90th (○) percen les with the simulated 95% CI for the 50th (solid black line), 10th (grey dotted lines) and 90th (dashed grey lines) percentiles for LUM (A) and DBL (B) concentrations (μg/l on log10 scale) from the final model. U8T ...................................................................................................................... 130
XLI
8TUFigure 4‐4 Population (○) and individual predicted (●) versus observed data for ARM (A) and DHA (B) concentrations (µg/l) for the final model. The lines of identity are also shown. The grey dashed line represents the LOQ of ARM in (A) and DHA in (B). U8T ............................................. 132
8TUFigure 4‐5 Visual predictive check showing observed 50th (●), 10th () and 90th (○) percen les with the simulated 95% CI for the 50th (solid black line), 10th (grey dotted lines) and 90th (dashed grey lines) percentiles for ARM (A) and DHA (B) concentrations (μg/l on log10 scale) from the final model. The fraction of BLQ observations from the data (○ connected with a dotted black line) with the simulated 95% prediction interval are also shown for both ARM and DHA. U8T ................................................................................................................................................ 132
8TUFigure 4‐6 The doses of lumefantrine and artemether in mg/kg given to children 5‐35 kg under current (solid black line) and suggested (dashed grey line) dosing regimens. The horizontal dotted black line represents the dose in mg/kg recommended for a 50 kg adult. U8T ........................ 137
8TUFigure 5‐1 (A) Population predicted (○) and individual (●) predicted versus observed plasma piperaquine concentrations (µg/l on log URU10URU scale) for the final model. The line of identity is also shown. (B) Conditional weighted residuals vs. time (log scale) for piperaquine final model. U8T ....... 157
8TUFigure 5‐2 Visual predictive check showing observed 50 UPU
thUPU (●), 10 UPU
thUPU () and 90 UPU
thUPU (○) percen les
with the simulated 95% CI for the 50 UPU
thUPU (solid black line), 10 UPU
thUPU (grey dotted lines) and 90 UPU
thUPU
(dashed grey lines) percentiles for plasma piperaquine concentrations (µg/l on log URU10URU scale) vs. time (h) for Artequick (A) and Duo‐cotecxin (B) from the final model. The observed data are superimposed as grey crosses. The insert shows data for the first 96 h. U8T ....................................... 158
8TUFigure 5‐3 (A) Population (○) and individual (●) predicted versus observed plasma artemisinin concentrations (µg/l on log URU10URU scale) for the final model. The line of identity is also shown. (B) Conditional weighted residuals vs. time for artemisinin final model. U8T ............................................ 160
8TUFigure 5‐4 Visual predictive check showing observed 50 UPU
thUPU (●), 10 UPU
thUPU () and 90 UPU
thUPU (○) percen les
with the simulated 95% CI for the 50 UPU
thUPU (solid black line), 10 UPU
thUPU (grey dotted lines) and 90 UPU
thUPU
(dashed grey lines) percentiles for plasma artemisinin concentrations (µg/l on log URU10URU scale) vs. time (h) from the final model. The observed data are superimposed as grey crosses.U8T ................. 161
8TUFigure 6‐1 HPLC‐UV (222 nm) chromatograms showing naphthoquine (N; tURRRU = 9.4 min) and the internal standard, tramadol (T; tURRRU = 6.8 min). Panel A is spiked plasma used in the calibration curve (20 µg/l naphthoquine); Panel B is a patient’s pre‐dose blank sample (with IS) showing no endogenous interference; Panel C is a typical sample (25 µg/l naphthoquine). U8T ....................... 177
8TUFigure 6‐2 LC‐MS chromatograms showing artemisinin (ART; tURRRU = 4.3 min) and the internal standard, artemether (IS; tURRRU = 7.9 min). Panel A is spiked plasma used in the calibration curve (200 µg/l artemisinin); Panel B is a patient’s pre‐dose blank sample (with IS) showing no endogenous interference; Panel C is a typical sample (136 µg/l artemisinin). U8T .............................. 178
8TUFigure 6‐3 Time‐concentration plots of NQ for Group 1 (Panel A), Group 2 (Panel B; milk) and Group 3 (Panel C; water and double‐dose) patients. Inset shows plasma concentration‐time data from 0‐100 h after the dose. U8T .................................................................................................. 184
8TUFigure 6‐4 (A) Population predicted (○) and individual predicted (●) versus observed NQ plasma concentration (µg/l; log scale) for the final model. The line of identity is shown. (B) Conditional weight residuals vs. time (log scale) for NQ final model. U8T ............................................................... 186
8TUFigure 6‐5 Prediction corrected VPC plots for NQ in children with uncomplicated falciparum malaria, showing the observed 50 UPU
thUPU (●), 10 UPU
thUPU and 90 UPU
thUPU (○) percen les with the simulated 95% CI
XLII
for the 50 UPU
thUPU (solid black line), 10 UPU
thUPU and 90 UPU
thUPU (dashed grey lines) percentiles. Inset shows plasma
concentration‐time data from 0‐100 h after the dose. U8T .................................................................. 189
8TUFigure 6‐6 Time‐concentration plots of ART for Group 2 (Panel A; milk) and Group 3 (Panel B; water and double‐dose) patients. U8T .................................................................................................. 190
8TUFigure 6‐7 (A) Population predicted (○) and individual predicted (●) versus observed ART plasma concentration (µg/l; log scale) for the final model. The line of identity is shown. (B) Conditional weight residuals vs. time (log scale) for ART final model. U8T .......................................... 192
8TUFigure 6‐8 Prediction corrected VPC plots for ART in children with uncomplicated falciparum malaria, showing the observed 50 UPU
thUPU (●), 10 UPU
thUPU and 90 UPU
thUPU (○) percen les with the simulated 95% CI
for the 50 UPU
thUPU (solid black line), 10 UPU
thUPU and 90 UPU
thUPU (dashed grey lines) percentiles.U8T ................................. 192
XLIII
x 148B148BPreface
The studies in this thesis arose from collaboration between the University of Western Australia
and the Papua New Guinea Institute of Medical Research. They were carried out at Alexishafen
Health Centre just north of Madang on the north coast of Papua New Guinea between
February 2007 and October 2010. Participants were from surrounding villages including Biges,
Haven, Kananam, Maiwara, Malmal, Pau, Rempi and Vidar. The candidate spent nine months
on four separate trips in 2007 and 2008 to co‐ordinate the studies and participate in the bulk
of the data collection. The resultant publications provide the chapters of this thesis that
contain original data. The contribution of the candidate and co‐authors to these articles is
listed in section i.
Drug assays were performed at School of Medicine and Pharmacology, University of Western
Australia, QEII Medical Centre (HPLC‐UV), School of Pharmacy, Curtin University, Bentley
campus (HPLC‐UV and LC‐MS) and Department of Clinical Pharmacology and Toxicology,
PathWest Laboratory Medicine, QEII Medical Centre (UPLC‐LC‐MS/MS). Biochemical analyses
for Chapter 3 were performed at Department of Biochemistry, PathWest Laboratory Medicine,
Fremantle Hospital.
Abbreviations are used throughout the thesis and appear in full when first used. A list is
provided in section vi.
All studies were approved by the Medical Research Advisory Committee of the PNG
Department of Health and the Institutional Review Board of the PNG Institute of Medical
Research. The Medical Research Advisory Committee carries the responsibility of providing
ethical approval for all studies of performed in Papua New Guinea including those involving
international parties. Human research carried out in PNG requires the approval of this body.
The exact nature of, and ethical issues surrounding, each study were presented to these
bodies as is required for studies in humans, particularly vulnerable populations such as young
children and pregnant women.
The following is included as a postscript to this preface:
During the middle of the data collection phase for the infant study six members of the field
team (Susan Griffin, Kay Kose, Servina Gomorai, Nolene Pitus, Christine Kalopo and Bernard
Maamu), a study infant and mother, and myself were involved in a serious car accident.
Although several members of the team were seriously injured there were no fatalities and the
XLIV
study mother and infant received only minor bruising and cuts. I suffered only cuts, bruises
and a dislocated thumb. I am most grateful to the staff of Madang Hospital who provided us
with medical care, and am in awe of the field team most of whom returned to work at the
Papua New Guinea Institute of Medical Research. I am indebted to Dr Laurens Manning and his
wife, Kate, for taking care of me in the days after the accident. It took me a month to regain
full physical and mental capacity, and it had no significant impact on the completion of this
thesis.
1
1 0B0BGeneralIntroduction
1.1 7B7BMalaria
1.1.1 23B23BGlobalsnapshot
Malaria continues to be a serious global health concern with approximately half the
world’s population still at risk.P
6P According to the World Health Organisation (WHO)
there were 107 countries where malaria was still endemic in 2010, eight of which have
interrupted transmission and are in the ‘prevention of reintroduction phase’.P
6P Despite
a reduction from 2005 when there were between 350‐500 million cases a year and
over 1 million deaths, P
7P there are still an estimated 216 million cases and 655 000
deaths attributable to malaria.P
6P In children living in Africa who make up a large
proportion of these deaths, malaria is often complicated with nutritional deficiency.P
8P
Globally there has been a 17% reduction in the incidence of malaria and a 25%
reduction in malaria specific mortality between 2000 and 2010.P
6P
1.1.2 24B24BParasitology
Malaria is an infection due to protozoan parasites from the genus Plasmodium that
target red blood cells (RBCs). There are five known species of Plasmodium that infect
humans. Most of the morbidity and mortality of malaria is attributable to P.
falciparum, which is also the most common globally. P. vivax, P. ovale and P. malariae
are also human malaria parasites. Recently, P. knowlesi, a monkey malaria parasite,
has been found to be an important cause of disease in humans in certain areas of
Asia.P
9P
Malaria is a vector‐borne disease transmitted by the female anopheline mosquito of
which there are more than 30 species. The life cycle of Plasmodium in humans and
mosquitoes is shown in Figure 1‐1. The disease in humans begins after a bite from an
mosquito. During the blood meal, less than 100 sporozoites residing in the mosquito’s
salivary gland PP
10, 11 are injected into the subcutaneous tissues (less frequently into the
blood stream) which then, after a short delay, travel to the liver.12 In the liver,
sporozoites pass through a Kupffer cell and several hepatocytes before beginning to
develop into merozoites inside a hepatocyte.13 In P. vivax, a number of these will enter
2
a dormant stage, and these hypnozoites can reactivate months or years later.14 P.
ovale may also have dormant hypnozoite stage although this hypothesis has recently
been brought into question.15 Each sporozoite will undergo asexual reproduction over
5‐15 days to develop into tens of thousands of merozoites forming a hepatic schizont.
The schizont then ruptures and each merozoite is capable of invading a RBC through a
sequence of receptor interactions, reorientations, vacuole formation and cell entry by
endocytosis.12 Once inside the RBC, the merozoite grows into a trophozoite which
eventually becomes a schizont comprising between 16‐32 merozoites. After 48‐72 h,
the schizont ruptures and each merozoite infects another RBC. Subsequently, another
round of the intra‐erythoctic cycle begins. Instead of undergoing asexual reproduction,
some merozoites in infected RBCs undergo sexual differentiation into gametocytes.
In P. falciparum infections, the surface of the RBC is altered such that asexual parasites
can bind to endothelium and to the placenta, while gametocytes can also adhere to
the endothelium.12 This sequestration of infected RBCs is responsible for cerebral
Figure 1‐1 life cycle of Plasmodium in humans and mosquitoes. From http://www.malariasite.com/malaria/LifeCycle.htm.
3
malaria, other manifestations of organ failure, and pregnancy complications including
maternal anaemia, low birth weight and premature delivery.12, 16 There is growing
evidence that P. vivax is also able to sequester,17 and this may be responsible for the
respiratory and pregnancy‐related complications associated with vivax malaria.17, 18
Once differentiated into micro‐ and macro‐gametocytes, these forms are taken up by
the mosquito during a blood meal. In the mosquito, a zygote is formed, which leads to
the formation of sporozoites. These migrate to the salivary gland within the mosquito
and are then able to infect a human host with the next blood meal of the mosquito.
1.1.3 25B25BMalariainPapuaNewGuinea
Papua New Guinea (PNG) is situated in the southwest of the Pacific Ocean and
occupies the eastern half of the island of New Guinea as well as numerous small
islands (Figure 1‐2). Four human malaria parasites are found in PNG; P. falciparum, P.
vivax, P. ovale and P. malariae. Of these P. falciparum represents 80% of all infections,
while P. vivax is the next most common.6 PNG accounted for 36% of all malaria cases in
the Western Pacific region in 2010.6
Temperature, and therefore altitude, is the main determinant of malaria prevalence in
PNG. The warmer coastal areas are holoendemic and the cooler highlands have low
rates of infections.19 Ninety‐four per cent of the population live in areas of high
Figure 1‐2 Map of South Pacific region showing location of PNG. From http://www.wpro.who.int/internet/files/eha/toolkit/web2009/Country%20Profiles/Maps/fiji%20melanesia%20country%20map.jpg.
4
transmission (more than 1 case per 1,000 population per year), while the remaining six
percent live in an area of low transmission (0‐1 cases per 1,000 population per year).6
Although many countries have seen a decrease in the number of cases from 2000 to
2010, the numbers in PNG have remained stable.6 Malaria is one of the most common
outpatient diagnoses and causes of hospital admission.19
The studies in this thesis were carried out at a small Catholic health centre in
Alexishafen, just north of Madang on the north coast of PNG (Figure 1‐3). There is a
very high rate of malaria transmission in this area, more than 100 cases per 1,000
population each year.6 The local distribution of Plasmodium species is representative
of the country as a whole. P. falciparum infections are the most common, followed by
P. vivax and occasional cases of P. ovale and P. malariae.20
Before DDT spraying programs (1957‐1970), P. vivax was the most common malaria
species in PNG. There was an increase in P. falciparum immediately after DDT spraying
was abandoned.19 For some time after this, the use of antimalarial drugs, particularly
chloroquine (CQ), was central to controlling malaria.19 The diagnosis of malaria is
largely a clinical diagnosis in PNG with an annual blood smear examination rate of <
5%.6 Subsequently, there was extensive, and potentially inappropriate, use of CQ,
Figure 1‐3 Map of Papua New Guinea showing Madang. From: http://geology.com/world/papua‐new‐guinea‐map.gif
5
which led to resistant strains first being reported in 1976.19 By the early 1990s, the
effectiveness of CQ and similar compounds such as amodiaquine (AQ) was greatly
reduced. It was not until 2000 that sulfadoxine/pyrimethamine (SP) was added to CQ
as first‐line for the treatment of uncomplicated malaria.19 The effectiveness of CQ/SP
also declined. In 2008, a large efficacy trial demonstrated that
artemether/lumefantrine (AL), an artemisinin combination therapy (ACT), provided
better treatment outcomes. This treatment is currently being implemented as first‐line
therapy.21 In addition to antimalarial drugs, distribution of insecticide treated bednets
(2000), indoor residual spraying (2010) and intermittent preventive treatment in
pregnancy (ITPp, 1981) have been implemented. Intermittent preventive treatment in
infancy (IPTi) is being evaluated,22 with the efficacy results from the trial pending.23
In areas of high transmission, such as is the case in the lowlands of PNG, it is pregnant
women, infants and children that bear the burden of disease.24, 25 For this reason
greatest attention is given to the control of malaria in these sub‐groups in PNG.
1.1.4 26B26BPreventionofMalariainPregnancy
Adults living in highly endemicity areas develop a degree of immunity to malaria such
that, although infections still occur, they are usually asymptomatic.25, 26 However, all
pregnant women, regardless of the intensity of malaria transmission in the area, are at
risk of a number of complications. These include maternal anaemia and the effect of
placental accumulation of parasites, including low birth weight from prematurity and
intrauterine growth restriction.24 There is also a growing body of evidence that
maternal infection itself increases infant mortality.27 Prior naturally acquired immunity
becomes compromised in pregnancy, particularly during the first and second
pregnancies.25 The effects of malaria during pregnancy are primarily facilitated by the
sequestration of parasites in the maternal placental vascular bed, and are therefore
more common with P. falciparum infections (see 1.1.2 above).24
Antimalarial drug treatment of infected pregnant women is acknowledged to protect
mothers from anaemia and their infants from low‐birth weight and death.28 Treatment
strategies for malaria in pregnancy include ensuring prompt treatment of symptomatic
infections. In endemic areas, the majority of infections are likely to be asymptomatic.
6
Nevertheless, they are still potentially harmful to both mother and infant.29 Another
management strategy is to provide continuous prophylaxis throughout pregnancy.
However, ensuring compliance is difficult.
For these reasons, a novel approach has been developed whereby treatment courses
of antimalarial drugs are administered at regular times during pregnancy regardless of
whether or not the mother is parasitaemic.30, 31 This is called IPTp. Doses are designed
to correspond with usual ante‐natal care, which is often has good compliance in
developing countries.32
Initial studies of IPTp used pyrimethamine (PYR) combined with dapsone. These
studies recorded benefits including a lower parasite density, increased maternal
haemoglobin (Hb) and higher birth weight.33 Currently, SP is drug of choice in many
IPTp programs. Its use is supported by a strong evidence base,28 and it is the only drug
currently recommended by the WHO for this purpose.34 In PNG, a combination of IPT
and chemoprophylaxis is employed. A single treatment dose of CQ with SP (IPT) is
given followed by weekly doses of CQ (chemoprophylaxis).35
Recent findings bring the use of SP for IPTp into question despite a strong evidence
base for the benefits of SP IPTp. In addition to the potential detrimental effects of
antifolate drugs in pregnancy, a major concern relates to the increase in parasite
resistance to SP. In an area of very high SP resistance (68% treatment failure by day 14
in children) there was no benefit of SP IPTp with the suggestion that it may have a
detrimental effect on infant anaemia.36 Another study reported no significant benefit
of SP IPTp in an area where it was previously beneficial, potentially due to a rise in
parasite resistance to SP.37 In contrast to these results, SP IPTp was still efficacious in
an area with high day 14 SP treatment failure in children (8‐39%),38 suggesting that
parasite resistance to SP affects treatment and prophylactic efficacies differentially.
The WHO recommends the use of SP IPTp to continue until the day 14 treatment
efficacy of SP in children falls below 50%.34 The use of SP outside IPTp has decreased,
thereby reducing drug pressure. There is some evidence that drug resistant strains are
decreasing in areas with reduced drug pressure.39, 40 However, no report has
7
demonstrated a positive effect of such reduced drug pressure on treatment or
prophylactic efficacy.
As the efficacy of SP, the only recommended form of IPTp, is under threat, there is a
great need for alternatives.29 However, few antimalarial drugs have proved safe in
pregnancy.41 Additionally, pregnant women are generally excluded from
pharmacokinetic (PK) studies of antimalarial drugs due to fears of adverse maternal
and foetal outcomes.42 Therefore information regarding appropriate dose
adjustments is often lacking. Potential candidates include piperaquine (PQ),
mefloquine (MQ) and azithromycin (AZI).29 The latter is the subject of the publication
in Chapter 2 and its pharmacology is discussed in more detail in section 1.2.5.1 (page
39).
1.1.5 27B27BPreventionofMalariaininfancy
For children born in an endemic area, factors during foetal development influence
their risk of developing malaria in first year of life. For example, maternal malaria
increases the rate of malaria morbidity in infancy,27, 43‐45 an effect that may be greatest
during the later stages of pregnancy.27 The malaria parasite is also capable of crossing
the placenta in utero (or at birth) to cause congenital malaria. 46 This occurs more
commonly in infants of non‐immune mothers, while infants of immune mothers with
congenital malaria are often asymptomatic and can clear the infection
spontaneously.47, 48
During early infancy, there is relative protection against falciparum malaria in endemic
areas, as evidenced by lower than expected rates of infection and low parasitaemia
when malarial infections do occur.49‐51 One explanation for this observation is the
passive transfer of immunity from mother to infant, mediated by maternal
immunoglobulin G passage through the placenta.52 The presence of foetal Hb in infants
could also have a role, with some evidence that it can slow the maturation of malaria
parasites in RBCs.53 Recently, this effect of foetal Hb has been questioned.54 One
report has suggested a combined effect of maternal immunoglobin G and foetal Hb to
impair the cytoadherance of malaria infected RBCs.54 This mechanism of protection
has also been proposed to explain the protective effects of sickle Hb and Hb C.55
8
Although young infants have relative protection against infections with P. falciparum,
no protection has been noted against P. vivax.56
Once this stage of relative protection ends at approximately 3‐6 months of life, the
rate and severity of P. falciparum infections increase.51 The processes of innate
immunity are still developing during this time.57 Peak malaria infection rate occurs at a
younger age in areas of higher transmission, where the acquisition of immunity is
faster.57 Hence, severe falciparum malaria is more common in younger children in
areas of high transmission, and is a significant contributor to the overall morbidity and
mortality in infants.58 In Madang and its surrounds, including Alexishafen where the
studies in this thesis were preformed, the rate of infection in those less than one year
old was approximately 15% for P. falciparum and 5% for P. vivax in the 1980s.20 A more
recent study in a neighbouring province has shown similar results, with a rate of
approximately 14% and 8% for P. falciparum and P. vivax infections, respectively.59 This
study also found the risk of severe malaria to be significantly higher in children below
the age of two compared with older children (odds ratio of 2.2, 95% confidence
interval, CI, 1.8‐2.7).59
Malaria also contributes to the complex interplay of factors, including nutritional
deficiencies, underweight status, and co‐infections, that result in a high infant
mortality rate in developing countries like PNG.8 The effect of malaria on infant
mortality is mediated through a variety of complications, including anaemia, organ
dysfunction and metabolic disturbances.58 In 2008, for children between 1 month and
5 years of life, malaria was the third leading cause of death worldwide after
pneumonia and diarrhoeal diseases, and the second leading cause of death in PNG
after pneumonia.60
Although a number of approaches have been used to deal with the high burden of
malarial disease in infants, IPTi is a relatively new strategy with promising results.61 In
ITPi, infants in endemic areas receive treatment doses of antimalarial drugs at routine
vaccination visits, regardless of clinical and/or parasitological features. SP has been
used as a first‐line agent in evaluating IPTi programs due to its availability, tolerability
9
and relatively low cost. It is also a single dose regimen which facilitates direct
observation of the full treatment.
A recent review of the safety and efficacy data from six trials of SP IPTi in Africa
reported 30% protective efficacy against clinical malaria, 23% protective efficacy
against all‐cause hospital admissions, 31% protective efficacy against malaria‐related
hospital admissions, and 21% protective efficacy against anaemia in the first year of
life.62 This review came to the conclusion that SP IPTi was a valuable tool in the control
of malaria in endemic areas of Africa, despite the emergence of molecular markers of
parasite resistance to SP.62 Results from a similar IPTi trial performed in PNG are
imminent. The results of this trial may differ from those performed in Africa due to the
different host and parasite genetics, species prevalence, and coexisting health
problems.
An additional benefit of IPTi over and above its effect on malaria related morbidity and
mortality may be an increase in immunisation rates. In Mali, immunisation rates
increased from 37% to 54% one year after an IPTi program began.63 Results of a pilot
study of SP IPTi in six African countries, where half a million SP doses were given, are in
preparation.23 Despite the extensive data of the benefits of IPTi, no country currently
has a IPTi policy in place.6
As will be discussed in detail below (section 1.2.4.1, page 36), despite the extensive
use of SP in infants, PK studies are limited. There is some suggestion that an increased
dose is required to ensure adequate exposure. This is the subject of the publication in
Chapter 3.
1.1.6 28B28BTreatmentofMalariainchildhood
Children living in malaria endemic areas continue to have symptomatic infections after
their first year of life. The development of acquired immunity is slow, occurring over
many years, and sterile immunity is yet to be demonstrated.26 Immunity to malaria
occurs in stages. Initially protection against severe malaria is developed followed by
immunity to symptomatic disease, and finally, yet incompletely, to asymptomatic
parasitaemia (Figure 1‐4).26 Acquisition of immunity to non‐cerebral severe infections
10
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11
artemisinin compound is partnered with another drug with a longer half‐life (tR½R) , are
either tablet combinations of individual pre‐existing drugs or co‐formulations that
include some partner drugs not commonly used (Table 1‐1).
In order to guide policy makers and health professionals regarding appropriate choices
of antimalarials, the WHO published guidelines for the treatment of malaria in 2006,68
with a follow up second edition in 2010.69 The first of these guidelines recommended
ACTs as the first line treatment of uncomplicated falciparum malaria.68 The four
recommended ACTs were AL, artesunate (ARTS)+AQ, ARTS+MQ and ARTS+SP. The first
of these, AL, is available as a co‐formulation, known as Riamet® or Coartem®. More
detail of the two components of AL is presented in sections 1.2.1.1 (page 16) and
1.2.2.1 (page 23). At the time of the study in Chapter 4, AL was still under investigation
for use PNG, where it is now the first line treatment.
The second edition of these guidelines added dihydroartemisinin (DHA)/PQ, another
co‐formulated ACT, to the list of recommended combinations.69 Around the same time
a Cochrane review of ACTs for the treatment of uncomplicated malaria found DHA/PQ
was at least as effective as the previously recommended ACTs.70 Both the WHO
guidelines and the Cochrane review noted the likely superiority of DHA/PQ to AL in the
Table 1‐1 List of some commonly used artemisinin combination therapies
Artemisinin component
Partner drug Co‐formulated WHO
prequalified WHO
recommended
artesunate amodiaquine Yes Yes
(Coarsucam) Yesa
artesunate mefloquine Yes No Yes
artesunate sulfadoxine‐
pyrimethamine No No Yesa
artesunate chloroquine No No No
artemether lumefantrine Yes Yes (Coartem) Yes
dihydroartemisinin piperaquine phosphate
Yes No Yes
artemisinin piperaquine base Yes No No
artemisinin naphthoquine Yes No No
artesunate pyronaridine Yes No No a Not for multidrug resistant areas
12
treatment of uncomplicated vivax malaria.69, 70 The superiority of DHA/PQ over a
number of other ACTs was also noted in a more recent Cochrane review, which
specifically examined the used of ACTs for vivax malaria.71 A recent efficacy trial in PNG
found a higher efficacy of DHA/PQ compared to AL and ARTS+SP for children with
uncomplicated vivax infection.21 A similar ACT, artemisinin (ART)/PQ base, is also
commercially available and is the subject of the publication in Chapter 5. The
components of these two PQ‐containing ACTs are discussed in more detail in sections
1.2.1.1 (page 16), 1.2.1.2 (page 19) and 1.2.3.1 (page 28).
There is evidence that the newly deployed partner drugs lumefantrine (LUM) and PQ
are vulnerable to the development of parasite resistance.72‐74 Currently there has not
been a need to change drug therapy in response to this. However, there is a need for
more ACTs that are effective and approved by the WHO. Two potential alternative
combinations are ARTS/pyronaridine and ART/naphthoquine (NQ). The former is
backed by the Medicines for Malaria Venture and has been shown to effective against
P. falciparum and P. vivax in Africa and Asia.75 The latter was developed as a single
dose therapy by the manufacturer, in contrast to the WHO recommendations for at
least 3 days of the artemisinin component in combination therapies.69 There are few
data relating to ART/NQ, particularly PK data. This combination is the subject of the
publication in Chapter 6 and NQ is discussed in more detail in section 1.2.3.2 (page 31).
In determining the efficacy of treatment for uncomplicated malaria, the WHO usually
recommends a 28 day post treatment follow‐up.69 For drugs with a longer tR½R such as
PQ this is extended to 42 days.69 Recurrent parasitaemia after treatment can either be
due to re‐infection or recrudescence. For the latter there is persistent erythrocytic
infection after inadequate treatment that becomes apparent either clinically or
parasitologically. The time between treatment and recrudescence is dependent on a
number of factors. Later recrudescence is seen for drugs with longer half‐lives,
including PQ. For P. vivax, the activation of dormant hypnozoites, known as a relapse,
is also a possible mechanism of recurrent parasitaemia. A relapse is unlikely to occur
less than 16 days from the initial infection.14 For falciparum infections, it is possible to
differentiate a re‐infection from a recrudescence using polymerase chain reaction
13
(PCR) technology. In vivax malaria, however, a recrudescence cannot be differentiated
reliably from a relapse, as both originate from the same initial infection.
In providing guidance regarding the choice of first‐line treatment for malaria, the WHO
uses PCR‐adjusted cure rates. These rates only represent those subjects who remained
free from recrudescence and relapse, while not considering re‐infections. The WHO
recommends changing a first‐line treatment with a PCR‐adjusted cure rate of < 90% to
one with a PCR‐adjusted cure rate of > 95%.69 Although a high PCR‐adjusted efficacy is
beneficial in reducing the rise of resistance, a better marker for the impact of
treatment on the individual patient may be the rate of recurrent parasitaemia (non‐
PCR‐adjusted cure rate). Regardless of the aetiology of the infection, the negative
health implications for the patient are similar. The non‐PCR‐adjusted cure rate is
particularly important in areas of high transmission where new infections during
follow‐up are common.21 In these areas, the period of post‐treatment prophylaxis
afforded by the partner drugs in ACTs becomes important.76 This period of prophylaxis
increases with the terminal elimination tR½R of the drug and is shortened with increased
parasite resistance against the drug.76 A recent efficacy trial of four different
antimalarial treatments in PNG found that, although there was a significant difference
in the PCR‐adjusted cure rates, there was no difference in the non‐PCR‐adjusted cure
rate.21 The post treatment prophylaxis efficacy should be considered when two
treatments have similar PCR‐adjusted cure rates.
14
15
1.2 8B8BAntimalarialdrugs
1.2.1 29B29BArtemisininandartemisininderivatives
ART (qinghaosu) is an extract of the leaves of the herb Artemisia annua (sweet
wormwood) and has been used in the treatment of fever in China for many
centuries.77 Although traditional preparations of this compound do produce clinical
effects, such preparations are ineffective when considering the doses required for
adequate treatment outcome.77 A number of derivatives of ART have been produced
by altering the function group at the 2‐keto position (Figure 1‐5). These are DHA,
artemether (ARM), artemotil and ARTS. The first report of ART appeared In Western
literature in 197978 and, since then the use of artemisinin compounds has flourished.
Figure 1‐5 Artemisinin and its derivatives showing endoperoxide bridge in blue. The different functional groups at the 2‐keto position in red namely oxo for artemisinin, hydroxyl for dihydroartemesisinin, methoxy for artemether, hemisuccinate for artesunate and ethoxy for artemotil.
Despite a number of theories and the endoperoxide bridge being essential, the exact
mechanism of antimalarial action of artemisinin compounds remains unknown.79
These compounds are extremely potent as they are able to reduce the parasite load by
10,000 per 2‐day erythrocytic cycle.80 They act on late trophozoite stages as well as
early trophozoites and gametocytes.81 The gametocidal effect has important
16
implications for preventing the transmission of malaria. Despite their short tR½R, there is
growing evidence for the emergence of resistance to these compounds.82
All compounds within this class are generally safe and very well tolerated.83, 84 The only
potentially serious side effects reported have been type I hypersensitivity reactions,
estimated to occur in 1 in every 3,000 patients.85 High doses of ARTS monotherapy for
7 d have been associated with transient neutropaenia.86 From results of in vitro and
animal studies, neurotoxicity has been associated with exposure to artemisinin
compounds.83, 87‐93 The relevance of these finding in humans is uncertain. A number of
case reports,94‐98 case‐control studies of hearing loss,99, 100 and reports of delayed
coma recovery,101, 102 have suggested potential neurotoxicity in human. These reports,
however, essentially represent observational data with no proof of causality.
Prospective studies have been unable to corroborate these findings.103‐107 The extent
of ART that crosses the intact blood brain barrier into CSF is small, only 2% of plasma
concentrations.108 Animal studies have involved allometrically higher doses of slow
release formulations given for longer periods than recommended for the treatment of
human malaria.89, 90, 109 This difference in dosing is thought to be primary reason that
no definitive evidence of neurotoxicity has been found in humans treated with
conventional doses of artemisinin compounds.109
1.2.1.1 75B75BArtemisinin
Despite being the basis for all artemisinin compounds, ART is less commonly used due
to its lower relative potency.110 Both oral and rectal formulations of ART are available.
Initially it was used as a monotherapy where oral dosing for 7 days was required to
avoid recrudescence. At the recommendation of the WHO, the practice of ART
monotherapy has largely ceased.111 Currently combinations with PQ base (Artequick®)
and NQ (ARCO®) are commercially available. The efficacies of these combinations are
discussed in sections 1.2.3.1 (page 28) and 1.2.3.2 (page 31).
The PK of oral ART have been extensively evaluated in healthy adults112‐117 and adults
with malaria,118‐124 while only one study has included children with malaria.125 These
studies used either non‐compartmental analysis112, 113, 117‐121, 123, 124 or compartmental
analysis,114‐116, 122, 125 and reported an elimination tR1/2R between 1.4 hours (h) and 4.8 h
17
in plasma.
minutes in
the first 10
the last re
115 In a nu
eliminatio
and the el
of these st
Although o
concentra
variability
Additiona
to be high
studies wi
sampling (
Figure 1‐6 ReVietnamese p2000120.
. Absorption
n plasma.114
0 h post do
ecordable d
mber of the
on tR1/2R. Ther
limination m
tudies emp
one study r
ations,120 th
between th
lly the auth
her than exp
ith saliva sa
(1.4 h and 4
egression line fopatients 1‐8 h a
n of ART is r
4, 125 The sam
se. In two s
rug concent
ese studies,
refore, aspe
may not hav
loyed saliva
reported a c
e correlatio
he two was
ors noted a
pected.120 T
mpling (0.9
4.8 h).113‐115
or artemisinin safter the first o
rapid with a
mpling dura
studies with
trations wa
, the sampli
ects of the d
ve been acc
a sampling i
correlation
on co‐efficie
s explained
a tendency f
This may exp
9‐2.2 h),116, 1
5, 118, 119, 123, 1
saliva and unboral dose of 100
an absorptio
ation in mo
h longer sam
s much ear
ing period w
distribution
curately est
in the place
between sa
ent was 0.77
by a linear
for concent
plain the tre
121, 124 when
125
ound venous p0 mg or 500 mg
on tR1/2R of ap
st studies h
mpling durat
lier at 8 and
was only tw
phase may
imated. Add
e of venous
aliva and un
7, indicating
relationship
trations in e
end for a low
n compared
plasma concentg artemisinin. F
pproximate
has been res
tions, 24 h
d 12 h respe
wice the rep
y have been
ditionally, a
samples.121
nbound ven
g that only
p (Figure 1‐
earlier saliva
wer tR1/2R rep
d to those w
trations (ng/mlFigure 3A in Go
ely 20
stricted to
and 48 h,
ectively.114,
orted
missed,
a number
1, 122, 124
ous
59% of the
6).
a samples
ported in
with plasma
l) in 18 male rdi et al.
18
In the only study of oral ART PK that included children, 23 children aged 2‐12 years
were given five days of ART dosed according to body weight (WT, approximately 10
milligram/kilogram [mg/kg]) and 31 adults received 500 mg ART daily for 5 days.125
Children were found to have a higher relative clearance (CL) than adults in a model
that used age group as a categorical variable covariate.125 Although the sparse
sampling was probably adequate for developing the population PK model, the last
sample in adults was at 10 h whereas it was 8 h in children.125 An unbalanced study
design is not ideal for comparisons between two groups.
The auto‐induction of ART metabolism has been well characterized, with a primary
effect on the bioavailability of subsequent doses rather than on systemic CL.116 This
reduction in relative bioavailability after multiple dosing has been noted in numerous
studies.113, 117, 119, 121, 123‐125 The results of these studies are summarized in Table 1‐2,
using reported areas under the curve (AUCs) of different doses to determine the
relative bioavailability between doses. The auto‐induction effect is rapid and
associated with a reduction in the exposure of the second dose by 77%.124 A semi‐
physiological model was used to estimate a mean auto‐induction time of 1.9 h.116
Table 1‐2 Change in relative bioavailability of artemisinin with consecutive dosing calculated from reported AUC values. All comparisons are with day 1 AUC. Artemisinin was given alone unless otherwise specified.
Study Day of comparison change in relative bioavailability
Hassan Alin et al. 1996123 six ‐ 83%
Ashton et al. 1998119 five ‐ 75%
Ashton et al. 1998113 four ‐ 66%
seven ‐ 76%
Sidhu et al. 1998125 five ‐ 86%
Svensson et al. 1998117 sevena ‐ 82%
Gordi et al. 2002121 five ‐ 87%
five ‐ 72%
Svensson et al. 2002124 two + 25%b
three ‐ 55%
twoc ‐ 77%
threec ‐ 75% a omeprazole 20 mg was given on days 1 and 7, b non‐significant increase, c mefloquine was given on day 1
19
1.2.1.2 76B76BDihydroartemisinin
DHA is an active metabolite of a number artemisinin compounds, including ARM, ARTS
and artemotil. It also used as an antimalarial compound in its own right. It has an in
vitro activity similar to other artemisinin derivatives, and higher than that of ART.110, 126
Is it available in both tablet and suppository forms. Currently, it is commonly co‐
formulated with PQ tetraphosphate, a combination recently added to the list of WHO
recommended ACTs. The efficacy of this combination is discussed in section 1.2.3.1
(page 28).
When given alone orally, DHA has a rapid absorption with an absorption tR½R of 0.58 ‐
0.83 h.127‐129 It reaches peak plasma concentrations between 1.5‐2 h in adults with or
without malaria.127‐129 In the same samples, the elimination tR½R was found to be
between 0.83 and 1.97 h.127‐129 Following oral administration of ARTS, a similar tR½R is
found for DHA as a metabolite.128‐131 In contrast, the non‐compartmental elimination
tR½R of DHA is longer after the administration of oral ART (1.5‐5.1 h).132‐134 This suggests
formation rate‐limited elimination of DHA after oral administration of ART. No PK
interactions have been reported for DHA.
1.2.1.3 77B77BArtemether
ARM is an artemisinin derivative available for oral dosing as well as intramuscular
injection. Such parental formulations of ARM are important in the treatment of severe
malaria, when oral intake may not be tolerated. ARM is the artemisinin component in
the first approved co‐formulated ACT, AL (Coartem®, Riamet®). The efficacy of this
combination is reviewed in section 1.2.2.1 (page 23).
The PK of ARM and DHA after oral dosing of AL have been extensively investigated.
After oral dosing, peak ARM concentrations are reached after 0.75‐2 h, with DHA
concentrations peaking shortly afterwards.133‐139 This is in contrast to a markedly
slower and variable absorption phase after intramuscular injection.108, 140 Compared to
a fasted state, the AUC of ARM and DHA were increased 2.4 and 1.9 times respectively,
after a standard high‐fat breakfast.134 When simultaneous compartmental analysis of
ARM and DHA has been performed, models using one or two compartments for ARM
linked with one compartment for DHA have adequately described the plasma
20
concentrat
of ARM wit
population
in the CL o
1.5 and 3.6
A number
with LUM,
later time
non‐signifi
potentially
increase in
DHA, due t
DHA AUC (
given with
differences
Figure 1‐7 Mepatient who rseen in artem
tion‐time co
th each dos
n PK analysi
f ARM with
6 h.133, 135‐13
of potentia
with which
compared t
cant decrea
y due to an
n the AUC of
to co‐admin
(see above)
and withou
s in PK. Wh
easured artemeeceived 80 mg ether and dihy
ourse.141, 142
se, associate
s, this phen
h each succe
9, 143, 144
l PK interac
h it is comm
to when it w
ase (0.72 90
increase in
f DHA.134 An
nistration of
were not a
ut MQ136 an
en administ
ether (■) and dartemether or
ydroartemisinin
2 Multiple d
ed with an i
nomenon w
essive dose.
ctions of AR
monly co‐for
was adminis
0% CI 0.41 ‐
first‐pass (F
nother stud
f LUM.139 Th
altered with
nd quinine (
tered with l
ihydroartemisirally at 0, 8, 24 n disposition. F
dosing of AR
increasing A
was adequat
. 142 The elim
RM have als
rmulated, pe
stered alon
‐1.27) in the
FP) metabo
dy found litt
he time dep
h co‐adminis
(QN),137 the
lopinavir/rit
inin (○) concenand 48 h demoFrom van Agtm
RM resulted
AUC of DHA
ely describe
mination tR½
o been asse
eak concen
e.134 One st
e total bioav
lism as ther
tle change i
pendent cha
stered LUM
re were no
tonavir the
trations and thonstrating the tael et al.139
d in a decrea
A.133, 134, 136, 1
ed by a 57%
R of ARM is
essed. Whe
trations occ
tudy also no
vailability of
re was an as
n the AUC o
anges in AR
M.134, 139 Whe
significant
re was a no
he model‐fittedtime dependan
asing AUC
139 In a
% increase
between
n given
curred at a
oted a
f ART,
ssociated
of ART and
RT and
en AL was
on‐
d curves in a nt changes
21
significant decrease in ART AUC (34%), and a significant decrease in DHA AUC (45%).135
As the tR½Rs were similar between the two groups, this effect was likely mediated
through a change in the bioavailability ART rather than induction of metabolism. The
lack of significant results in the above studies may be a consequence of the high
variability in ART and DHA PK parameters with coefficients of variation between 32
and 103%, combined with modest difference in PK (Table 1‐3). Finally, when
ketoconazole, a potent CYP3A4 inhibitor, was co‐administered with a single dose of
ART/LUM, the AUC of ART and DHA increased by 2.39 and 1.66 fold, respectively, and
tR½R of ART and DHA increased by 1.42 and 1.69 fold, respectively. These results
emphasise the role of CYP3A4 in ART disposition.138
Table 1‐3 Fractional difference in tR½R and AUC of artemether and dihydroartemisinin when drugs are coadministed with artemether/lumefantrine
Study Coadministered drug artemether dihydroartemisinin
tR1/2 AUC tR1/2 AUC
Lefevre et al. 2000136 mefloquine ‐ first dose 1.21 1.37 0.95 1.19
last dose N/A 1.04 1.80 1.28
Lefevre et al. 2002138 ketoconazole 1.42 a 2.39 a 1.69a 1.66
Lefevre et al. 2002137 quinine 0.83 0.54b 0.94 0.63b
German et al. 2009135 lopinavir/ritonavir 0.94 0.65 N/A 0.55a a P<0.05. b significant difference (P<0.05) however quinine was administered after the last dose of artemether/lumefantrine when a difference already existed between the groups. The authors cannot therefore be certain that this difference was due to the administration of quinine.
22
1.2.2 30B30BArylaminoalcohols
QN is the oldest member of this group of antimalarial drugs. It was an active
component in powdered bark from the cinchona tree, the first medicine to be used
against malaria in the Western world. In an attempt to synthesize QN in 1856, the first
synthetic textile dye, mauve, was inadvertently produced ‐ later to become the origin
of the 4‐aminoquinilone drugs (page 27). The synthesis of QN was later achieved in
1944. Other arylamino alcohols include MQ, halofantrine and LUM (Figure 1‐8). These
compounds act on asexual blood stages of the parasite. While the mechanism of action
is not clear, there is evidence that they interrupt the handling of heme in the food
vacuole, through a different pathway to the CQ‐like drugs.145 They are not as potent as
the artemisinin compounds, only reducing the parasite load by approximately 100‐
1,000 times with each erythocytic cycle.
Figure 1‐8 Arylamino alcohols showing the similarity in structure of halofantrine and lumefantrine in blue.
The in vitro sensitivities of the drugs in this group are higher in CQ resistant strains
than CQ sensitive strains,146 and are also closely related to each other.147 Therefore,
resistance against LUM, a relatively new drug, may be expected in areas of high
resistance to MQ. Indeed, in these areas, treatment response to LUM has been related
to the degree of this resistance.148 The use of ACTs has seen the return of sensitivity to
MQ in Thailand,149 a positive sign for the future of these compounds.
23
There are a number of concerns with regard to the toxicity of drugs within this group.
QN has many known side‐effects, the more serious of which are ototoxicity, tinnitus,
hypoglycaemia and cardiotoxicity.83 MQ is known to produce neuropsychiatric
symptoms from dizziness and anxiety to convulsions, psychosis and encephalopathy.83
Halofantrine prolongs the electrocardiographic QT interval, increasing the risk of
tachyarrhythmias and death.150 Given the structural similarity between LUM and
halofantrine (Figure 1‐8), it may be expected that LUM would have similar effects on
the heart. In vitro data suggest LUM is safe in this respect, with a wide therapeutic
index. The half maximal effective concentration (ECR50R) for effect on heart muscle is
8,100 nM, many folds higher than half maximal inhibitory concentration (ICR50R) value
for malaria, at 40 nM.151, 152 Additionally, clinical studies have not found a significant
effect of therapeutic doses of LUM on QT interval in either children or adults.153‐158
1.2.2.1 78B78BLumefantrine
LUM, originally known as benflumetol, was developed in the 1970s by the Academy of
Military Sciences in China. It is only available in a combination with ARM, first
registered in China in 1992. After further development by Novartis Pharmaceuticals,
this combination was released to the world in 2001. In vitro, LUM demonstrates
synergy with ARM and DHA against Plasmodium falciparum.151, 159 Desbutyl‐
lumefantrine (DBL), an active metabolite of LUM, has been shown to have a higher
activity than LUM in vitro (between 2 and 7 times)160‐163 and has mild synergy with DHA
in vitro.163
The currently recommended treatment regimen of AL consists of 6 doses taken with
fat over 3 days (at 0, 8, 24, 36, 48, 60 h). Adults receive 4 tables per dose and children
are dosed according to WT (Table 1‐4). This represents an improvement over a
previous less efficacious regimen of 4 doses over two days. A more efficacious
approach of 6 doses over 5 days exists.164 However, it would likely result in poorer
compliance and, therefore, worse outcomes outside the research setting. A simpler
once daily treatment regimen, with doubled doses taken once daily for three days,
resulted in lower LUM AUC and worse efficacy, when compared to the recommended
24
treatment regimen.165 The study was underpowered (n=43) and the two groups were
not well matched in terms of baseline parasitaemia. The median parasitaemia was
1,981/μl in the standard regimen group, compared to 15,549/μl in the once daily
group.165 As a higher baseline parasitaemia can reduce bioavailability,166 the effect of a
simpler dosing schedule on AUC may have been overestimated. A similar once daily
regimen is the recommended regime for a paediatric suspension of the
combination.167 This formulation has promising initial efficacy findings in the treatment
of uncomplicated falciparum malaria in children.168, 169 However, it was not superior to
tablets given as six doses over three days.170 To conclusively accept or reject a
simplified dosing schedule, a larger non‐inferiority trial is required. The available
evidence suggests that, due to the low and variable bioavailability of LUM, multiple
doses of AL are required to allow LUM to reach concentrations that ensure treatment
efficacy.
A recent pooled analysis of seven studies supported by the manufacturer
demonstrated adequate day 28 PCR‐adjusted parasitological cure rates (>97%)
amongst both children and adults with falciparum malaria171. In children, this was
97.3% in the evaluable population and 93.4% in the modified intention to treat
analysis. A Cochrane review reported that, in comparison with other ACTs, AL is at
least as effective in the treatment of falciparum malaria.70 Neither of these reviews
reported the rates of re‐infection, a particular concern with LUM in areas of high
endemicity. LUM has a relatively short tR½R when compared other ACT partner drugs, at
68 ‐ 275 h135‐138, 164 versus 224 ‐ 667 h for PQ.172‐179 Therefore, a shorter period of post‐
treatment prophylaxis would be expected after AL. In PNG, this combination was
found to be superior to DHA/PQ phosphate, ARTS/SP and CQ/SP in treating the P.
falciparum infection in young children with a PCR‐corrected day 42 efficacy of 95.2%.21
Table 1‐4 Manufacturer’s recommended dosing of artemether/lumefantrine in children. Each tablet contains 20 mg of artemether and 120 mg of lumefantrine.
Weight Artemether/lumefantrine dose
5 ‐ <15 kg 1 tablet
15 ‐ <25 kg 2 tablets
25 ‐ <35 kg 3 tablets
>35kg 4 tablets
25
When the rate of recurrent parasitaemia was considered, there was no significant
difference between the various treatment arms in the study.21
The high efficacy of this combination does not extend to the treatment of vivax
malaria. A recent Cochrane review reported AL is probably inferior to DHA/PQ in
preventing recurrent parasitaemia before day 28 after the treatment of vivax
malaria.71 Results from a PNG trial also suggest AL should not be the first choice in the
treatment of uncomplicated vivax malaria.21 In this trial, there was no significant
difference between the 42 day PCR‐adjusted efficacy of AL and CQ+SP, 30.3% and
13.0% respectively (P=0.06).21 These results may be attributed to the relatively short tR½R
of LUM.
The PK of LUM has been evaluated in a number of populations. In all cases, a highly
variable bioavailability has been noted. The bioavailability was found to increase up to
16‐fold when given with food compared to the fasted state.180 Only 1.2g of fat, given
as 36 millilitres (ml) soya milk, was required to reach 90% of maximal absorption in a
study of healthy volunteers.181 In patients with malaria, there is evidence that
absorption improves as they recover from the acute infection.141, 164 In fact,
bioavailability is inversely proportional to both initial parasite load and temperature at
presentation,164 further suggesting there is an independent disease effect on
bioavailability. After a lag‐time of approximately 2 h, absorption occurs relatively
slowly. Maximum concentrations are reached between 4‐6 h after oral dosing,134, 135,
138 and absorption tR½R is between 4‐5 h.141, 164 In compartmental analyses, the PK of
LUM is best described through a two compartment model.141, 143, 164, 182 The exception
is one study in which, due to an inadequate sampling schedule, only a one
compartment model could be supported.142 The terminal elimination tR½R in adults has
been reported between 68 and 275 h.135‐138, 164
Several PK evaluations of LUM in children have been reported.142, 144, 183 However,
methodological issues complicate comparisons with adult data. None of these studies
performed adequate sampling for accurate calculation of the terminal elimination tR½R.
The authors of one of these studies suggested that children may be under‐dosed, as
they found a lower AUC in comparison with healthy adults.144 Due to limitations from
26
their study design, direct comparisons with data from adults with malaria were not
possible.
With regards to the PK of DBL, little has been reported.143, 184 In the two available
studies, the ratio between DBL and LUM was low at between 0.33% and 5.2%,143, 184
and the terminal elimination tR½R of DBL, only reported in one study, was longer than
that of LUM at 137 h vs. 68 h.143
As with ARM, a number of PK interactions for LUM have been investigated (Table 1‐3).
No significant differences in LUM PK were seen when QN was co‐adminstered with
AL.137 MQ decreased the exposure to LUM without affecting its tR½R, potentially through
its effect on reducing bile production and therefore LUM absorption.136 In contrast,
lopinavir/ritonavir increased the AUC of LUM more than two‐fold and this was
associated with a non‐significant increase in the elimination tR½R. This may represent
CYP3A4 inhibition, although the same study found the exposure to ARM, also a CYP3A4
substrate, was decreased.135 Ketoconazole, a potent CYP3A4 inhibitor, also increased
the AUC of LUM. As the elimination tR½R was not significantly altered with ketoconazole
administration, it is likely that increased bioavailability through inhibition of FP
metabolism was the cause of the increased AUC.138
Table 1‐5 Fractional difference in tR½R and AUC of lumefantrine when drugs are coadministed with artemether/lumefantrine
Study Coadministered drug lumefantrine
tR1/2 AUC
Lefevre et al. 2000136 mefloquine 0.74 0.56a
Lefevre et al. 2002138 ketoconazole 0.93 1.65a
Lefevre et al. 2002137 quinine 1.13 1.02
German et al. 2009135 lopinavir/ritonavir 1.14 2.35a a P<0.05.
27
1.2.3 31B31B4‐aminoquinilones
The development of the 4‐aminoquinilone antimalarials began in the textile industry
where methylene blue, one of the original man‐made textile dyes, was used as the first
synthetic drug against malaria. In 1934, modifications of the structure of methylene
blue led to the development of CQ, the first 4‐aminoquinilone. From this time, further
modifications were performed to yield a number of derivatives. These include AQ and
NQ, with an alteration in the side chain, and PQ, the result of dimerising two 4‐
aminoquinilone moieties (Figure 1‐9). They have antimalarial action by interrupting the
process of detoxifying heme in the parasite, a side‐product in the digestion of Hb. This
results in increased oxidative stress and, ultimately, death of the parasite.185
Figure 1‐9 Some 4‐aminoquinilones antimalarials showing the common 4‐aminoquinilone group in chloroquine, amodiaquine, naphthoquine and piperaquine (a dimer).
CQ is considered safe in the doses typically used in the treatment of malaria. When
taken in overdose, CQ can lead to death primarily through cardiotoxicity.83 In usual
doses, pruritus is a particular feature and affects compliance to CQ.186 More serious
side effects are related to long term use of CQ and include neuropathy and
retinopathy.187 Idiosyncratic reactions including rash and bone‐marrow toxicity, have
been reported.187 The toxicity of PQ has not been characterized as well as that of CQ. A
review of more than 2,600 adults and children indicated that it was safe and well
tolerated.188 Less information regarding the toxicity of NQ exists in the literature. A
28
review of 952 adults reported a side‐effect profile similar to that of other antimalarial
drugs.189 This review included data from the manufacturer not available elsewhere in
the literature, with one of the two authors being an employee of the manufacturer.
The same review included a number of studies of NQ have also been performed in
children, with no significant safety concerns thus far.189
1.2.3.1 79B79BPiperaquine
Of the dimeric 4‐aminoquinilones (bisquinilones), PQ is the most commonly used and
well‐researched. It was developed in the 1960s in China, where it was used
extensively. It was initially used as a monotherapy in China, Cambodia and Vietnam. Its
uptake in other parts of the world only began recently. It is available as two forms in
ACT combinations. Its tetraphosphate salt is co‐formulated with DHA (e.g.
Duocotexcin) and its base is co‐formulated with ART (e.g. Artequick). The former has
been extensively researched, and recently added to the list of WHO recommended
ACTs.69 There are few data for the PQ base containing combinations. In vitro, PQ has
activity against P. falciparum similar to that of CQ against CQ sensitive strains.190 A
number of studies have reported a correlation between the ICR50R of CQ and PQ.21, 190‐192
There is also some evidence that the in vitro activity of PQ is independent of resistance
to CQ as summarised in a recent review by Gargano et al.193 This review was written by
an employee of Sigma‐tau, a manufacturer of a formulation of DHA/PQ
tetraphosphate, raising a potential conflict of interest. A number of reports have
shown antagonism in vitro between PQ and DHA.191, 194, 195 The clinical significance of
this observation has not been determined, and is likely to be small.
Gargano et al193 also summarised the results of a number of efficacy studies involving
DHA/PQ, and reported that it is highly effective against both P. vivax and P.
falciparum.193 This review included studies from Asia and Africa that included both
children and adults. Many of the studies reported the 28 day efficacy, rather than the
42 day efficacy. For PQ, given its long tR½R, a longer 42 day efficacy is recommended by
the WHO.69 Table 1‐6 summarises the PCR‐adjusted efficacy of DHA/PQ in studies that
reported efficacy at 42 days or later. Two Cochrane reviews also concluded that
DHA/PQ tetraphosphate was efficacious against both falciparum and vivax malaria.70, 71
29
In contrast to these reports, an efficacy trial in PNG children with malaria found the
combination performed poorly against falciparum malaria, despite a reasonable
efficacy against vivax malaria. In this study, the PCR‐adjusted 42 day efficacy was 88%,
Table 1‐6 PCR‐adjusted efficacy of dihydroartemisinin/piperaquine at day 42 or afterwards in various studies.
Study Country (site) n
Pf d42 efficacy
(PCR‐adjusted) (%)
Denis et al. 2002196 Cambodia (Anlong Veng) 34 94.1a
Cambodia (Snoul) 55 96.4a
Hien et al. 2004197 Vietnam (Ho Chi Minh) 166 98.7a
Ashley et al. 2004198 Thai‐Myanmar border (Mae La, Mawker Thai, Muruchai)
170 100
Ashley et al. 2005199 Thailand (Mae Sot) 179 98.3
Smithuis et al. 2006200 Burma (Dabhine, Mingan) 152 99.3
Mayxay et al. 2006201 Laos (Phalanxay) 105 100
Ratcliff et al. 2007202 Papua (Timika) 289 95.9
Kamya et al. 2007 144 Uganda (Apac) 211 93.1
Janssens et al. 2007 203 Cambodia (Kvav, Anlong Veng) 162 97.5b
Hasugian et al. 2007 202 Papua (Timika) 114 95.2
Zongo et al. 2007204 Burkina Faso (Bobo‐Dioulasso) 172 97.8
Grande et al. 2007205 Peru (Iquitos) 230 98.3
Yeka et al. 2008 206 Uganda (Kanungu) 215 98.0
Karunajeewa et al. 2008 21 Papua New Guinea (Alexishafen, Kunjingini)
100 88.0
Arinaitwe et al. 2009207 Uganda (Tororo) 351 95.8b
Thanh et al. 2009 172 Veitnam (Ninh Thuan) 49 100
Bassat et al. 2009 208 Burkina Faso (Nanoro) 198 90.4
Kenya (Kilifi) 133 89.5
Mozambique (Manhica) 274 90.9
Uganda (Mbarara) 164 93.9
Zambia (Ndola) 192 92.7
total 879 91.5
Valecha et al. 2010 209 Thailand, Laos, India (various) 668 99.3
Mayxay et al. 2010 209 Laos (Phalanxay, Xepon) 197 99.5 a efficacy at day 56, b efficacy at day 63.
30
lower than the average 92.4% and 99% in multicentre studies in Africa and Asia
reported in the review by Gargano et al.193 The results of this trial, and the hypotheses
presented for the lower than expected treatment efficacy against falciparum malaria
including a relationship between the in vitro efficacy of PQ and CQ,192 were brought
into question by Gargano et al.193 No alternative explanation for the result is presented
in the review. The results from the PNG trial are not the lowest reported efficacy of the
combination. At one of the sites of an efficacy trial in Rwanda, Rukara, the 28 day PCR‐
adjusted efficacy was only 87%,210 even lower than the comparable 90.1% 28 day PCR‐
adjusted efficacy in the PNG study. The potential cross‐resistance of PQ with CQ was
also proposed as a potential cause for the low efficacy, as Rukara was in an area well
known for its high CQ resistance.210 In the original analysis of the multicentre study in
Africa, the average 42 day efficacy was only 91.5% (extended per‐protocol),208 as
opposed to the 92.4% stated in the review by Gargano et al.193 Lower rates were found
at individual sites, specifically 89.5%, 90.4% and 90.9% in Kenya, Burkina Faso and
Mozambique, respectively.208 In fact, when considering the results in Kenya and
Mosambique alone, AL compared to DHA/PQ tetraphosphate was significantly more
efficacious at 28 and 42 days as evidenced by a lower PCR corrected failure rate
(P<0.05, Fisher’s exact test, two‐tailed level of significance).208 The studies that have
reported lower efficacies consisted primarily of paediatric subjects. Children have
lower plasma concentrations of PQ on day 7 (see below) and this may, in part, explain
the differences in efficacy between studies. Although an adequate explanation for the
low efficacy of PQ in some areas is yet to be established, the results of the PNG trial
are not as unique as suggested by Gargano et al.193
ART/PQ base is another ACT where PQ is used as the partner drug, albeit in a different
form. This combination is currently marketed as a two day regimen. It therefore is not
in line with current WHO recommendations for at least three days of a artemisinin
compound.69 When three days of an artemisinin compound are given, at least two
erythrocytic cycles are exposed to the drug. A lower number of parasites are therefore
exposed to the partner drug, reducing the risk of parasite resistance. Limited data exist
regarding the efficacy of this combination, with mixed results compared to DHA/PQ
tetraphosphate.211, 212 These two studies in adults demonstrated a 28‐day efficacy of
94 and 100% for the two day regimen. A three day regimen has also been proposed in
31
the literature, and was found to be more efficacious than the two day regimen.213
These early studies, performed in a small number of subjects, represent preliminary
data only and further evaluation is required to assess the absolute and comparative
efficacy of this combination.
The PK of PQ has been primarily investigated after oral administration of PQ
tetraphosphate. The absorption of PQ is relatively slow, with maximum plasma
concentrations reached 2.5 ‐ 4 h post dose172, 176, 179, 214 and an absorption tR½R of 7.7 ‐
9.2 h.174, 177 A number of studies have also demonstrated multiple concentration peaks
following each dose, presumably corresponding to enterohepatic recycling of the
lipophilic drug.177, 215 Discrepancies exists regarding the effect of fat intake on the
bioavailability of PQ tetraphosphate.173, 176, 214, 215 One study of patients with malaria
that used a standard local meal, as opposed to healthy volunteers consuming a high fat
meal,215 found no increase in PQ exposure associated with fat intake.214 The use of
compartmental analysis has often found a two‐compartment model to be suitable.174,
175, 178 One study found that a three‐compartment model better described the time‐
concentration profile. However, there were insufficient data to support it over a two‐
compartment model.177 The disposition of PQ may be more complex and the ability to
detect lower concentrations suggested a triphasic elimination in one study.216 Related
to this finding, the elimination tR½,R of PQ, reported to be between 224 and 667 h,172‐179
is positively associated with the duration of sampling.216 No PK evaluation of PQ base
has been reported.
A limited number of PQ PK studies have been performed in children. In comparison to
adults, children have been found to have lower day 7 concentrations,202 higher relative
CL,174 and lower PQ exposure at critical times.178 These findings suggest that current
dosing in children may be inadequate.
1.2.3.2 80B80BNaphthoquine
NQ, as its phosphate salt, was registered as a medicine in China in 1993 where it was
initially tested as a monotherapy.217 It is structurally similar to AQ, with a large
aromatic side chain on the 4‐aminoquinilone moiety (Figure 1‐9). Currently, it is
available as a co‐formulation with ART, although there is sparse clinical data regarding
32
this combination in the literature. In vitro, NQ has potency similar to PQ. Its in vitro
activity may be correlated with that of CQ,192 although only one report has
investigated its in vitro interactions with other drugs.194 As with PQ (see above),
antagonism between DHA and NQ has been noted.194 In the mouse model, synergism
of NQ with ART against P. berghei was demonstrated.217
The efficacy of ART/NQ has been summarised by Hombhanje et al.,189 who had access
to archival data from the manufacturer, the majority of which were not available
elsewhere in the literature. It should be noted that one of the two authors of this
review is an employee of the manufacturer. The combination is effective against
falciparum malaria in adults and children in Africa and Asia, including PNG, and is
comparable to other ACT combinations including ARM/LUM and DHA/PQ
tetraphosphate.189 One study of adults in this review, however, reported a 28 day
efficacy of 92%, lower than the 95% recommended by the WHO for a new first‐line
treatment. The remainder of the eleven studies reported a 28‐day efficacy of > 96%,
with three of the studies in paediatric populations and two others including children.
The tR½R of NQ is comparable to that of PQ (see below). Therefore, given the WHO
guidance, a 42 day efficacy would also be the recommended end point for efficacy
studies of NQ. Most studies of NQ to date have only performed 28 day follow up.189 No
significant difference in cure rates with a single dose or two doses over 24 h has been
found.189 The recommendation by the WHO to have at least three days of the
artemisinin compound is in relation to reducing the risk of resistance to the partner
drug in ACTs (as discussed above). Therefore, the risk of resistance to NQ is increased
by using a single dose regimen. In vitro data have demonstrated the potential for
parasite resistance against NQ.218
Few data exist regarding the efficacy of this combination against P. vivax. Initial reports
are mixed, ranging from 64% (28 days, non‐PCR‐adjusted)219 to 90% (56 day, non‐PCR‐
adjusted)217 and 99% (42 day, PCR‐adjusted).189 Further data are required to better
define its efficacy against vivax malaria.
The first description of the PK of NQ in Western literature appeared in the conclusion
of a paper in 2004 without reference to the original source of the data.217 After oral
33
administration of NQ alone to 14 healthy volunteers, peak plasma concentration were
achieved 2‐4 h post dose and the elimination tR½R was reported to be between 41 and 57
h.217 Little other PK information was given, other than a sampling duration of 168 h. A
more detailed examination, also in healthy volunteers, was later published.220 The time
to peak plasma concentration was similar to that of the initial report, between 2.5‐3.5
h. Some results were inconsistent within different sections of this study (Table 1‐7).
The range of reported elimination tR½RRsR had significant variability in different sections,
from 156‐299 h.220 For example, when comparing the fed and fasted states in the same
participants, the tR½R was reported to be 156 and 276 h respectively.220 This would
suggest an increased CL over many days caused by the ingestion of a single meal, an
implausible consequence of food intake.220 A more likely cause for this finding would
be problematic study design. Blood samples were only taken out to 216 h, about the
same as the reported tR½R for NQ. Therefore, the study design was not adequate for
determining the elimination tR½R. The results from these two studies would suggest that
NQ has a long tR½R, and a longer sampling duration would be required to characterise it
adequately.
The same study reports that the combination with ART results in a doubled exposure
of NQ, represented by an AUC from 0‐216 h, compared to when the drug is given
alone.220 There was little change in elimination tR½R associated with this change, from
299 h when given alone to 276 h in combination.220 This suggests that NQ has a higher
bioavailability in the combination compared to when administered alone. Different
subjects were used to make this comparison and no patient characteristics were given
to assess if the two groups were comparable. An explanation for this finding was not
provided, and it is not easily explained given the current knowledge of these two
drugs.
Table 1‐7 Summary of findings of the pharmacokinetics of naphthoquine in healthy volunteers in Qu et al.220
Dose Combined/Alone Fasted/Fed Elimination tR½R (h) AUCR0‐216hR (g h l‐1) 200 mg Combined Fasted 256 480
400 mg Combined Fed 156 446
400 mg Alone Fasted 299 424
400 mg Combined Fasted 276 955
600 mg Combined Fasted 233 1875
34
An unusual relationship with coadministed food was also noted. Using a crossover
design, the exposure to NQ was approximately halved compared to a fasted state.220
This finding is not expected for the lipophilic drug, and is in contrast to findings in
PQ,173, 176, 214, 215 and LUM180, 181 where the relative bioavailability either remained
constant or increased with the administration of fat. The washout period used, 168 h,
is too short for a drug with a tR½R of at least 200 h. However, as the fed stage of the
study followed the fasted stage, the short washout period would not explain this
unusual finding. No other studies have evaluated the effect of food on the PK of NQ.
No studies of the PK of NQ in patients with malaria, or children, exist in the literature.
35
1.2.4 32B32BAntifolatedrugs
Antifolate drugs were first used against malaria in the 1930s when sulfachrysoidine, an
antibacterial agent, was used in a trial against malaria (Figure 1‐10). Development was
stagnant until the 1950s when sulfadoxine (SDX), a sulfonamide, was developed and
partnered with PYR, a compound with which sulfonamides were found to have a
synergistic effect.221 Broadly, there are two groups of antifolate drugs categorised with
respect to their targets in the folate synthesis pathway. These are dihydrofolate
reductase (DHFR) inhibitors, such as sulfachrysoidine and SDX, and dihydropterate
synthase (DHPS) inhibitors, such as PYR.145 The mechanism of action of these drugs is
through preventing folate synthesis, and thereby interrupting DNA synthesis in the
malaria parasite. The combination of SP is the most commonly used antimalarial
antifolate treatment today, and contains both a DHFR and a DHPS inhibitor.
Figure 1‐10 Antifolate antimalarial drugs.
These drugs have been used extensively and their safety profiles are well known.
Although humans lack DHPS, DHFR inhibitors do interrupt folic acid synthesis in
humans. Drugs like PYR are known to exacerbate subclinical folate deficiency in
vulnerable subjects. However, in other subjects the antifolate effects are only seen
after long‐term administration.222 SDX, like other sulfonamides, can cause cutaneous
hypersensitivity reactions including the potentially fatal Stevens Johnson Syndrome. In
combination with PYR, this serious side effect is predominately only found when used
36
for long‐term prophylaxis of malaria which is no longer a recommended practice.83 This
side effect is rare when SP is used for the treatment of malaria.83 Other rare
hypersensitivity reactions include vasculitis, myocarditis, acute glomerulonephritis and
pulmonary reactions.83 Haematological toxicity caused by SP, besides that cause by
folate depletion, is thought to be primarily caused by SDX and includes
agranulocytosis, aplastic anaemia and thrombocytopaenia.223 SP has been found to be
safe in the treatment of malaria, with serious side effects being a rare event.
1.2.4.1 81B81BSulfadoxine/pyrimethamine
For antimalarial treatment, SDX and PYR are always used together. Initially, this
combination represented a viable alternative to CQ to which parasite resistance was
growing. After enjoying a brief period of high efficacy, resistance against this
combination occurred quickly, within five years in some areas.224 After this, SP was
used in combination with CQ or ARTS with some success. The most significant role of
SP as an antimalarial has been its use in IPTi and IPTp, as discussed above.
The use of SP in infants older than 2 months has been extensive but it is
contraindicated in younger infants due to the immaturity of their metabolising enzyme
systems.225 There have been few PK evaluations that have included infants to allow
adequate assessment of the exposure to SDX and PYR in this age group. A study of
patients with malaria aged from one year old to adulthood has highlighted the
importance of assessing the PK in the young.226 This study found that children aged 2‐5
years had a double risk of treatment failure in association with a lower exposure to
both SDX and PYR.226 A doubled dose for this age group was, therefore, suggested as
an appropriate alteration to dosing. Although children aged 1‐2 years were also
included in this study, no dose adjustment was found to be necessary.226 These
younger children already received a higher mg per kg dose relative to the other age
groups, and only 11 children were in this age group.226 A population PK study in
children, including infants with congenital toxoplasmosis, found that the elimination tR½R
of both drugs was related to WT.227 A shorter elimination tR½R was found in lighter, and
thus younger, children227 as would be expected from allometry.228 The potential effect
of maturation of elimination processes on the PK of SP in infants has yet to been
established.
37
There is also some evidence that the concentrations of SDX and PYR are lower in
children with malaria than in healthy adults, signifying a potential disease effect of
malarial on PK.229, 230 As these comparisons were made between age groups in
different populations, and very few infants have been included these studies, it is
difficult to draw conclusions regarding the potential impact of malaria on the PK of SDX
and PYR in infants. In IPTi, infants would be in one of three disease states. Some
infants would be afebrile and aparasitaemic, others would be afebrile with a positive
blood smear and the last group would be febrile and slide positive for malaria.
Potentially, PK differences may exist between these three groups. These potential
differences have not yet been investigated.
38
1.2.5 33B33BAntibiotics
Some antibiotics have significant activity against Plasmodium species, although the
parasite is not a prokaryote like bacteria. This antimalarial action is facilitated by the
presence of two organelles in the malaria parasite, the mitochondrion and the
apicoplast. These organelles have their own DNA and have similar processes of protein
production to bacteria.231 The apicoplast is a relic plastid of red algae lineage, devoid of
photosynthetic activity. Apicoplasts are also found in Toxoplasma species.231 It is the
site of antimalarial activity of most antibiotics that have action against Plasmodium
species.231 Tetracyclines, such as doxycycline, also act on the mitochondrion.232 It has
recently been shown that the only essential function of the apicoplast in Plasmodia is
the production of isoprenoid precursors.231 These precursors are involved in a number
of cellular processes including energy production in the mitochondia.231 For all
antibiotics, a ‘delayed death’ phenomenon is seen where the exposed parasites are
able to develop normally while the apicoplast in the next generation of merozoites
cannot divide, and the resultant parasites lack this essential organelle and die.231
Therefore, in the assessment of in vitro antimalarial activity of antibiotic agents, it is
important to allow for longer culture times so that their full effect can be assessed.233
Figure 1‐11 Antibiotics with activity against Plasmodium species.
The toxicity of these compounds has been reported primarily from their use as
antibiotic agents. For example, doxycycline and related tetracyclines are
contraindicated in pregnancy because of their incorporation into teeth and bone234 as
39
well as a small teratogenic risk.235 Unlike doxycycline, AZI is safe to use in pregnancy.236
The Centers for Disease Control and Prevention recommends 1 gram (g) of AZI as first
line treatment of Chylamidia trachomatis in pregnant women. This recommendation is
based on studies confirming its effectiveness, as well as its safety, in pregnancy.237 The
most common side effects of AZI are gastrointestinal, including nausea, diarrhoea and
abdominal pain, but not psudomembranous colitis.238 These side effects are thought to
result from the interaction of AZI with the motilin receptor in the gut wall.239 Some
transient increases in liver enzymes have also been noted.238 No deaths have been
attributed to AZI. Studies of AZI in the setting of malaria have found similar results with
respect to tolerability and safety.240‐242
1.2.5.1 82B82BAzithromycin
AZI is derived from erythromycin, and is a semi‐synthetic azalide antibiotic, a subclass
of the macrolides. It is commonly used in the treatment of respiratory and sexually
transmistted diseases and is available in oral and intravenous formulations.243, 244 In
vitro it has activity against P. falciparum comparable to other compounds in the
antibiotic class of antimalarials245, 246 and is more potent than erythromycin.247 It is
more potent against CQ‐resistant than CQ‐sensitive strains.246
A number of trials have evaluated its use in the treatment of malaria. A Cochrane
review reported that it performed poorly in the treatment of falciparum and vivax
malaria, both as a monotherapy and in combination with CQ or ARTS.248 One trial in
pregnant women found that compared to three days of ARTS with SP and SP alone (the
standard treatment), two days of 1 g AZI with SP resulted in fewer recrudescent
episodes in the treatment of falciparum malaria.249 Therefore, AZI is unlikely to have a
role in the treatment of uncomplicated malaria outside of pregnancy.
The benefits of AZI for IPTp extend past its antimalarial effect. In developing countries,
sexually transmitted infections and, to a lesser extent, pneumococcal infections
contribute to morbidity in pregnancy.250 A large trial of IPTp in pregnant Malawian
women compared three regimens given during pregnancy; i) two single doses of SP
(control group) ii) monthly single doses of SP, and iii) monthly single doses of SP
combined with two single 1g doses of AZI.251 When compared to the control group, the
40
AZI group had a significantly lower rate of preterm delivery, higher birthweight and
fewer postpartum Trichomonas infections.251 The only significant benefit over the SP
monthly group was a decrease in the rate of very preterm birth (<35 weeks
gestation).251 In the AZI group, there was a trend for lower maternal and placental
blood parasitaemia at delivery compared to the other two groups.251 This did not reach
statistical significance, probably due to the small number of events.251 Neonates of the
mothers in the AZI group had significantly lower rates of hospitalization when
compared to the other groups.251 In contrast to these results, a similar IPTp trial found
that there was no additional benefit of 1 g AZI given twice in pregnancy over two doses
of SP in preventing preterm birth, low birthweight, maternal anaemia or maternal
parasitaemia.252 These differences may in part be explained by fewer doses of SP in the
trial without significant results (two doses compared with monthly dosing), a higher
percentage of first pregnancies as well as a greater burden of malaria and syphilis.
These factors could influence the effect of AZI on pregnancy. In addition to the
combination of AZI with SP, a combination of AZI with CQ is also being investigated for
IPTp with results pending.253
The 1g single dose of AZI used in the studies mentioned above is the suggested dose
for sexually transmitted diseases, which may not be suitable in the treatment of
malaria (see below). Neither of these studies assessed the impact of AZI on the
histological appearance of the placenta.251, 252 Histologically, pathological features in
placental malaria can be used to distinguish between active, active‐chronic and past‐
chronic infections,254 each of which is associated with particular clinical outcomes.255
An efficacy trial of SP with AZI against falciparum malaria in pregnancy was the only
study to evaluate this outcome, and was underpowered to assess differences in
placental features.249
Few studies have been published that evaluate the PK of AZI, and no PK data exist for
malaria‐infected patients. Absorption of AZI has been described by both zero256, 257 and
first258, 259258, 259 order kinetics. Both two256, 257, 259 and three compartment models258,
260 have been reported, with an elimination tR½R of 27‐79 h.256‐258, 260‐262 Only one PK
evaluation has been performed in pregnant women,263 but it has significant limitations.
Twenty women were given 1g of AZI at set times before a planned Caesarean section,
41
during which samples for AZI assay were taken.263 Each subject, therefore, only
contributed one sample to the analysis. An elimination tR½R of 12 h was reported,263
significantly shorter than that reported in non‐pregnant adults. Due to the small cohort
and cross‐sectional nature of this study, it is difficult to interpret the significance of this
result. The PK of AZI in earlier stages of pregnancy is likely to differ to those in term‐
gravid women and has not been determined to date. One PK study has shown no
interaction between AZI and CQ;264 but no such data exist for SP and AZI.
In bacterial infections, a single larger dose of AZI has been shown to be as effective as
multiple smaller daily doses.256, 265 This effect of ‘front loading’ is likely due to the
accumulation of AZI in white blood cells to concentrations 100‐1,000 fold higher than
in plasma.250, 252 These high concentrations persist over time and carry the AZI to the
site of infection.256, 258 In contrast, the concentration of AZI in RBCs is essentially
negligible after oral dosing.258 Given the need for relatively prolonged parasite
exposure to therapeutic concentrations in vitro, it is likely that prolonged high
concentrations of AZI in plasma, where the parasites encounter the drug, are required.
Therefore, in the treatment and prevention of malaria, two to three day regimens of
AZI would theoretically be preferable to a single dose.
42
43
1.3 9B9BPharmacokinetics
1.3.1 34B34BIntroduction
PK, put simply, is what the body does with a drug. More formally it is, “the study and
characterization of the time course of drug absorption, distribution, metabolism and
excretion”266 The term was first used in German literature in 1953266 but did not
appear in English literature until 1959.267 PK is a relatively new science and is
considered to have begun in 1937 when Teorell published a pair of papers entitled
“Kinetics of distribution of substances administered to the body”.262, 263 In these, he
attempted to describe the distribution throughout the body through the use of an
approximate system of differential equations.268, 269 Since then, the science (and art) of
PK has grown significantly.
One of the aims of PK is to find the dose at which a drug is therapeutic but not toxic.
This concept existed long before the time course of the concentration of a drug in the
body was characterized. Paracelsus, a German‐Swiss physician (as well as a botanist,
alchemist and astrologer) in the early 1500s wrote, “What is there that is not poison?
All things are poison and nothing (is) without poison. Solely the dose determines that a
thing is not a poison.”270 In the case of antimalarials, there is often a lack of
information to guide the dosing in the most vulnerable populations, namely infants,
children, and pregnant women. Therefore, the study of the PK of antimalarials in these
populations is of paramount importance to ensure that they are both effective and
safe.
In very general terms there are two approaches to PK analysis, non‐compartmental
and compartmental, and these are described briefly below. Essentially, they are both
mathematical tools that help describe the concentration versus time course of a drug
in a bodily fluid.
44
1.3.1.1 83B83BNon‐compartmentalAnalysis
Although some of the equations used in non‐compartmental analysis appeared in
1931, it was not until the 1980s that this method was widely used.266 It uses statistical
moment theory to determine the area under the curve (AUC), area under the moment
curve (AUMC) and the variation of residence times.271 These three properties
correspond to the area under the zeroth, first, and second moment curves
respectively.271 These areas are determined using the trapeozid method with
extrapolation to enable their calculation from time zero to infinity. Other parameters
can then be derived, including the mean residence time, mean transit time (MTT), CL,
volume of distribution (V), rate constants and half‐lives. Additionally, simple
information such as the maximum concentration (CRmaxR) and the time to maximum
concentration (TRmaxR) are determined from simple observation of the concentration
versus time plot.
Although non‐compartmental analyses has sometimes been referred to as ‘model‐
independent’, it is based on a model with a far more restricted structure than
compartmental analysis.272 Although a model is not explicitly stated, there must be at
least one compartment in which the observations being modelled exist.272 An inherit
assumption is that drugs modelled with non‐compartment methods display linear
kinetics.272 These restrictions make these models easier to conceptualize, as only the
compartment in which the observations are made (the central compartment) needs to
be considered.272 Any potential peripheral compartment(s) do not need to be
identified or characterised.272
Non‐compartmental analysis incorporates less input, and therefore less bias, by the
modeller and is able to describe simple PK parameters adequately. However, as the
time course of the drug in the measured fluid is not considered, it has limited use in
predicting future drug concentrations, or in explaining physiological processes.
1.3.1.2 84B84BCompartmentalmethods
Compartmental methods were first utilized by Teorell in 1937, where he used a four
compartment model described by a set of differential equations.273 In these models,
there are a number of defined compartments through which the drug passes according
45
to some d
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The simple
Figure 1‐12 O
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46
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Figure 1‐13 Tw
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47
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her than am
and . T
the is th
al and perip
model in figu
om the central
is the abso
eripheral vo
ompartment
ted, includin
ge of the
Equation 1‐4
Equation 1‐5
Equation 1‐6
ounts,
he of a
he rate
heral
ure 1‐13
compartment
orption
olume of
tal
ng the AUC
48
In a compartmental model, the concentration of the drug in each of the compartments
over time can be linked with a multi‐exponential equation. The concentration is the
integral of the rate of change of the concentration of the drug. For example, the rate
of change in concentration, and its integral (the concentration over time) for a one
compartment model with oral dosing would be:
Equation 1‐7
Equation 1‐8
where is the concentration of the drug at a given time, is the dose of the drug, and
is the bioavailability. To determine the true value of , the AUC from an oral dose
needs to be compared to that of an intravenous dose. When this comparison is not
available, as often is the case, and are relative to , and are known as the
apparent clearance ( / ) and apparent volume of distribution ( / ).
Compartmental analysis, therefore, can be considered as a form of curve fitting. An
appropriate curve (model) is chosen that best represents the observed concentration
versus time data. Figure 1‐15 demonstrates an example of this principle.
Time (h)
Dru
g c
on
cen
trat
ion
(g
/l)
Figure 1‐15 Example of a fitted concentration versus time profile. Observed concentrations over time (red crosses) have been fitting using a curve that is the combination of positive and negative exponentials (black line) that represent absorption and elimination processes respectively.
49
1.3.2 35B35BPopulationpharmacokinetics
Determining the PK parameters in an individual can be relatively easily accomplished
using the aforementioned methods. However, providing information on the PK of a
drug in a population is far more valuable, and requires extension of these methods.
The principles of population PK presented here can readily be found in the
literature.275‐281
Broadly speaking there are two types of PK characteristics that need to be determined
in a population, fixed effects and random effects. Fixed effects refer to the population
average parameters and covariate relationships, while random effects refer to the
variation between individuals, between occasions and within individuals.276 Fixed
effects can be expressed as follows:
Equation 1‐9
where represents an individual, is the clearance of individual , is the
population average of the not related to the covariate, is the value of the
covariate for individual , and is the average proportionality constant
relating to .
The first type of random effect is the variability that exists between individuals within a
population, as no two individuals are perfectly identical. The inter‐individual or
between subject variability (IIV or BSV) can be added to Equation 1‐9 to give:
Equation 1‐10
where expresses the difference between the population expected value of for
individual and . For the population, each is normally distributed with a mean of
0 and a variance of . As two PK parameters within a population may be correlated,
covariance terms also exist between s. This information makes up a matrix Ω, which
has diagonal elements equal to the variance of different , while the other elements
describe the covariance between different s. If multiple occasions exist for each
50
individual, then inter‐occasion or between‐occasion variability (IOV or BOV) also exists
in the population.282 This can be added to Equation 1‐10 to give:
Equation 1‐11
where represents a particular occasion, and represents the difference between
the expected individual clearance ( ) and the clearance for that occasion ( ). Each
is normally distributed with a mean of zero and a variance of . The final random
effect that exists in a population is the variability within an individual, also known as
the error or the residual unexplained variability (RUV). As no biological assay method is
perfect and no study is performed perfectly, there is always a component of error in
each observation that cannot be explained using the other random effects mentioned
above. RUV applies to each observation:
∗ Equation 1‐12
where is the final predicted concentration in patient on occasion at time , ∗
is the model predicted concentration based on all the , , and values of the
population, and , the error, is the difference between and ∗ .
1.3.2.1 85B85BNaïvepooleddatamethod
The simplest method for analysing the concentration versus time curves obtained from
many individuals within a population is the naïve pooled data (NPD) approach. As the
name suggests, this method pools all the observations and them analyses then as if
they had come from a single individual. Using this method, only one random effect,
, is estimated:
∗ Equation 1‐13
has similar properties to the aforementioned RUV, and accounts for all variability
from , and . Therefore, this method assumes that there are no covariate fixed
effects and cannot determine IIV or IOV.
51
Although the NPD approach allows for easy analysis of data with few observations for
each individual, even estimates of can be biased and non‐informative.279
1.3.2.2 86B86BStandardtwostagemethod
The standard two stage (STS) method provides benefits over NPD as it is able to
provide some estimate of IIV. As the name suggests, the analysis is performed in two
steps. In the first, individual estimates of the individual parameters ( for example)
are obtained by analysing the curves of each individual alone. Although it is not used
further, the RUV for each individual is also obtained. The individual parameters are
then used to calculate the population average values ( ), as well as provide an
estimate of IIV ( ). There is potential for covariate relationships to be identified and
IOV ( ) to be estimated at this stage.
Although IIV can be estimated using STS it is often overestimated.283 A limitation of
using this method is that the PK profile of each individual must be able to be analysed
individually.281 Individuals with few observations, or missing vital observations, cannot
be included in the analysis. The data from these individuals, as well as the time and
effort in collecting them, is lost when using this method of analysis. A more complex
shortcoming of STS is that individual estimates for parameter do not all have the same
precision, although they are treated equally when calculating population
parameters.279 This becomes another potential source of bias.279
More complex methods of two stage analysis exist, including global two stage and
iterative two stage methods. These have shown improved performance when
compared to STS.283‐285
1.3.2.3 87B87BNon‐linearmixedeffectsmethod
Non‐linear mixed effects modelling for PK was first described in 1972 and was
proposed as a method for analysing routine patient data.286 It avoids the estimation of
individual parameters, instead s (average population parameters) are estimated
directly. All the fixed effects and random effects expected in a population are
estimated simultaneously, hence the name mixed effects modelling. Using this
methodology, individuals with few data points do not need to be discarded. These
52
individuals can be included in the analysis, and unlike NPD, they maintain their
individuality. The output from this method therefore describes the population as a
whole, rather than the individuals within it.
As sparse data can be analysed, this method is ideal for providing PK information in
patient groups where obtaining full rich sample sets for each individual is difficult
either logistically or ethically.277 Additionally, it is capable of handling complexities in
the model structure, such as transit compartments,287 and challenges in the dataset,
such as observations below the limit of quantification (BLQ).288 For these reasons it is
well suited to the analysis of the antimalarials in the study samples presented in this
thesis, where either sampling was sparse (Chapter 3), data sets were incomplete
(Chapters 2, 4and 6), complexities existed in the structural model (Chapter 5), or
challenges were present in the dataset (Chapter 4).
53
1.3.3 36B36BNONMEM
NONMEM is a computer package that was designed by Beal and Shiener to perform
NONlinear Mixed Effects Modeling.289 It is widely used in analysis of population PK
data and is referred to as the ‘gold standard’ in analyses of this type.
NONMEM employs a likelihood ( ) function, which it maximizes with respect to the
fixed and random effects, and the dataset (a maximum method). To determine for
all the data, however, the marginal for each data point must first be calculated. As
there is considerable difficulty in calculating the marginal exactly, different
approximations are used. These approximations correspond to the various estimation
methods within NONMEM (a detailed derivation of each approximation is provided by
Wang 2007290). A note should be made that, although NONMEM utilises a maximum
method (using approximations), it actually minimizes the value of 2 log , called
the objective function. Therefore, the objective function value (OFV) is used to arrive
at the most likely estimates of PK parameters.
Three estimation method will be discussed here, Laplacian, first‐order conditional
estimates (FOCE) and first order. The Laplacian (first level of approximation) and FOCE
(second level of approximation) methods both use individual parameter estimates to
calculate the OFV at each step. They are therefore ‘conditional’ estimation methods, as
they utilize the random inter‐individual effects in the approximation of the .290, 291
They are more time consuming than the FO (third level of approximation) method,
which only uses the population parameter estimates in the calculation of the OFV.290,
291 Additionally, both the Laplacian and FOCE method can be used with , interaction
(INTERACTION option), where that the effect of on will be considered. This
becomes important when a proportional, or other heteroscedastic, error model is
used. In these error models the size of the effect of on ∗, the model predicted
concentration, will depend on the value(s) of the (s) for the individual, as these effect
the size of ∗.
One important role of the OFV is to allow hypothesis testing of nested models, using
the likelihood ratio test (LRT).292 Two models are nested where the only difference
between them is that one (or more) of the parameters has been fixed (usually to 0) in
54
one model, and is estimated in the other. This applies to covariates (if parameterised
appropriately), and also to structural parameters. The difference between the OFV of
nested models is approximately distributed chi squared ( ), with the degrees of
freedom equal to the number of parameters fixed in the reduced model.291 This is
known as the LRT. For accurate hypothesis testing to take place, FOCE‐I (FOCE with ,
interaction) or Laplace‐I (Laplacian with , interaction) should be used, with a
preference towards Laplace‐I in models with greater non‐linearity.291
1.3.3.1 88B88BModelbuilding
A process of model building needs to take place to arrive at a final PK model,
preferably with the use of hypothesis testing.293 First the data must be checked for
errors of data entry such as impossible or unrealistic times or covariate values. Then,
the structural model is established, with the nature of absorption and elimination
processes as well as the number of compartments determined. In other words the
population parameters, or θ, of the model are established in this step. Alongside this,
the most appropriate model for RUV (ε) should be established.
In general, three types of RUV models exist: additive, proportional and combined.293 Of
these, the combined method is preferred as it takes into account that the size of the
RUV is likely to increase with increasing observations, and that the RUV can never be
zero. If the data is log transformed, an additive RUV model approximates the
proportional RUV model for untransformed data. The main difference is that the
estimated RUV can never be zero when an additive model is used for log transformed
data. The RUV can also be a marker for model selection. A reduction in the RUV
suggests that more of the variability in data is explained.
The IIV and IOV ( and respectively) can then be added to the model, and tested to
assess if they can be estimated (diagonals of the Ω matrix, Figure 1‐16). This depends
on the number of individuals in the population and the number and distribution of the
observations. The correlation between s and s should also be estimated (off‐
diagonals of the Ω matrix, Figure 1‐16), particularly if the model is to be used for
simulation purposes.
55
, , ,
, , ,
, , ,
Figure 1‐16 A typical matrix with variance terms, the diagonals, in blue ( , is the variance for ), covariance
terms, the off‐diagonals, in red and black ( , is the covariance between and , , is the same as , ).
Covariate relationships can then be assessed and tested. Considerations for inclusion
of a covariate relationship include the change in OFV, the biological plausibility of the
relationship, and a decrease in the IIV of the parameter in the relationship. In this way,
part of the IIV can become explained while the other part remains random. One
method for incorporating categorical covariates is as follows:
1 Equation 1‐14
where is the fractional effect of 1 on , and is value of the
covariate for individual . The value of the covariate is usually either 1 or 0, although
covariates with more categories are possible. Potential categorical covariates include
co‐administered substances that induce/inhibit metabolising enzymes or compete at
transporter sites, genetic factors influencing metabolism, disease states (such as
malaria), sex and pregnancy. The latter covariate is discussed in more detail in section
1.3.4.1.
For continuous variables, a number of functions can be tested, such as a centred linear
model:
1 Equation 1‐15
where becomes the fractional effect of a unit increase in on , and is
the median of in the population. A power function is also possible:
Equation 1‐16
56
where now represents the power relationship between and . Given the
flexibility of NONMEM, any covariate relationship can be defined. Nevertheless, the
biological plausibility should be considered before the implementation of any covariate
relationship. Two important types of continuous covariates are those that describe size
and age (discussed further in section 1.3.4.2). Other potential continuous covariates
include gestational age (instead of the categorical pregnancy covariate), parasitaemia,
glomerular filtration rate (GFR) and biochemical indices for an individual.
Some additional aspects of model building and specific covariates particular to parts of
this thesis are discussed below.
1.3.3.1.1 135B135BAbsorptionmodels
The absorption phase of drugs can be complicated, particularly when considering that
some drugs with poor solubility first need to become available for absorption.294
Absorption is often suitably described with first‐order kinetics, while zero‐order
kinetics can also be appropriate in some cases. A lag time between the ingestion of the
drug and its appearance is often required, and can improve the models of datasets
that demonstrate this phenomenon.287 More complex models that include either
combined or sequential zero‐ and first‐order kinetics have also been used.294 The
wrong absorption model had been found to impact the estimation of other parameters
such as and .294 Recently, a model utilizing a mathematical approximation for
transit compartments has been developed. This model allows the estimation of the
number of transit compartments (NN) and the transit compartment rate (kRtrR) as
continuous variables ( s), with their own and .287 This transit compartment model
has been found to be superior in describing the delayed appearance of a number of
drugs after oral administration when compared to those using lag time.287
1.3.3.1.2 136B136BBelowthelimitofquantificationdata
A commonly encountered problem in the analysis of drugs within a biological matrix is
samples with unmeasurable, or even undetectable, concentrations of the drug.288
These BLQ data have been shown to significantly impact the estimates of fixed and
random parameters, resulting in significant bias, even if they only represent 5% of the
entire dataset.295‐297 In 2001, seven ways of dealing with these data in NONMEM were
57
presented. Some of these methods were omission of the BLQ data, setting the BLQ
data to a particular set value, and more complex likelihood‐based methods.288 A
slightly altered version of one of these methods, M3 with F_FLAG,296 has been shown
to consistently perform well in various situations, including those with the percentage
of BLQ data as high as 40%.295‐297 The implementation of this method has been
simplified from version 6.2 of NONMEM onwards and is described in Ahn et al.296
1.3.3.2 89B89BModelevaluation
An important step in the development of a population PK model is the evaluation
phase. Generally speaking, there are internal validation and external validation
techniques.298 The latter is only possible when there is another similar dataset
available to test the final model against. Although a portion of the original data can be
set aside for this purpose, often there are limited numbers of subjects available
initially, which makes this strategy impractical.
1.3.3.2.1 137B137BBasicinternalevaluation
Basic internal validation includes the assessment of goodness‐of‐fit (GOF) plots. GOF
plots include population and individual predictions (PRED and IPRED respectively)
versus observations (OBS), overlaid PRED on OBS versus time, and weighted residuals
(WRES) versus time.293 Recently a new diagnostic, the conditional weighted residual
(CWRES), has been described.299 For models using FOCE estimation, examination of
WRES can suggest an inadequate model is adequate, or that an adequate model is mis‐
specified.299 This is because WRES are calculated using the FO approximation, and
therefore CWRES, calculated based on the FOCE approximation, are more appropriate
when using FOCE estimation.299
1.3.3.2.2 138B138BAdvancedinternalevaluation
One method for obtaining the confidence intervals (CI) of the model estimates is the
non‐parametric bootstrap.300 By sampling with replacement from the study
population, new datasets are obtained which are then analysed with the final model
obtained in the model building stage. The 2.5th and 97.5th percentiles from the
resulting estimates for each of the fixed and random effects can be used to obtain the
empirical 95% CI. Although some reports include the rates of success of these
58
bootstrap results, there is no evidence that these rates indicate model stability or any
other diagnostic.301, 302
The visual predictive check (VPC) originates from the posterior predictive check and is
a simulation based form of evaluation.303 A number of concentrations are simulated
from the final model for each time point in the dataset. The simulated concentrations
are then compared to the observed concentrations to determine the internal
predictive performance of model.303 An extension of this method allows evaluation of
data with BLQ, one of the few tools available in this setting. The 95% CI of simulated
fraction of BLQ data are compared with the actual fraction of BLQ data at set time
periods.296 More recently an improved method for VPCs, known as prediction‐
corrected VPCs (pcVPC), has been developed.304 This method corrects for the
differences in the size of the observations and, therefore, presents a more accurate
visual representation of the predictive performance of the model. 304
A similar method is known as the NPC. NPC use the same simulated data that is
produced with a VPC, yet it presents them in a different way. The number of actual
data below and above different prediction intervals (PI) is compared with 95% CI,
obtained from the simulated data. Unlike a VPC, there is no independent variable,
usually time, in a NPC.
Both VPCs and NPCs can be stratified according a covariate in the model. This is
particularly important when there are two or more groups in the datasets. For
example, when comparing two formulations of the same drug, it is important to assess
if the predictive performance of the model is adequate for each formulation.
59
1.3.4 37B37BPharmacokineticconsiderationsinspecificpopulations
1.3.4.1 90B90BPregnancy
The effects of biological changes that occur during pregnancy which have potential
effects on PK have been reviewed in the literature.305, 306 Changes in gastric emptying
and pH, increased plasma volume with associated decreased concentrations of plasma
proteins, increased body fat, inhibition as well as induction of hepatic enzymes, and
increased glomerular filtration, all have potential implications for PK.305, 306
Pregnancy is known to delay gastric emptying and decrease the acidity in the stomach.
Despite this, the bioavailability of many drugs appears unchanged in pregnancy.306
In pregnancy, the increased plasma volume with associated decrease in drug binding
plasma proteins has a number of effects on PK. One expected change would be an
increase in the V, and therefore a decrease in the peak concentrations.307 Additionally,
due to the higher V, the elimination tR½R would be expected to increase:
½ ln 2 Equation 1‐17
As changes also occur in (see below), the final result may be a decrease, increase or
no change in tR½R.307 These effects, although at times measurable, have not yet proved
to be clinically important.307
Due to the reduced concentration of plasma binding proteins, the fraction of unbound
drug, and therefore the concentration of drug available to act at receptors or be
cleared by the organs of elimination (correlated with ), may be expected to
increase.306 This is particularly important for drugs with high plasma binding and low
extraction ratio, such as phenytoin, where a small decrease in the fraction bound can
produce a large increase in the concentration of free drug.306 Therefore, the effects of
the increased and decreased binding need to be considered simultaneously. Often,
the only solution for such drugs is to measure unbound concentrations or rely on
clinical monitoring.306
60
Changes in metabolism in pregnancy are mediated through changes in hepatic blood
flow, as well as the activity of metabolizing enzymes. Some evidence suggests that
there is an increase in hepatic blood flow in pregnancy.306 In particular, drugs with a
high extraction ratio have an increased .306 The various changes in action of
metabolising enzymes may be more significant than changes in blood flow. These
include changes in phase I, such as cytochrome P450 monooxygenase (CYP) enzymes,
and phase II metabolism, such as uridine 5′‐diphosphate glucuronosyltransferase
enzymes (UGT) and N‐acetyltransferase enzymes (NAT).306, 307 These are summarised in
Table 1‐8 which demonstrates the varied nature of these changes with either
increased, decreased or unchanged action of these enzymes.306 In general, these
changes do not appear to be consistent throughout pregnancy. In fact, there are
differences between the different stages of pregnancy. There is a period of a number
of weeks post‐partum before the activity of these enzymes returns to non‐pregnant
values.306, 307
Just as there are changes in hepatic , so too renal is altered in pregnancy. Renal
is not only determined by the GFR, but also on the active processes of tubular
secretion and reabsorption. Increased blood flow to the kidneys is thought to be
responsible for the higher GFR seen in all stages of pregnancy.306 Interestingly, in the
final six weeks of pregnancy the GFR was found to fall and was no different to non‐
pregnant values from three weeks before delivery.308 The variability seen in the
increased between renally excreted drugs is in part due to active tubular processes.
Table 1‐8 Changes of enzymes involved in metabolism during pregnancy, adapted from Anderson 2005307.
Enzyme Change in pregnancy Example drug substrates
CYP1A2 Decreased caffeine, clozapine
CYP2A6 Increased nicotine
CYP2C9 Increased phenytoin,
CYP2C19 Decreased proguanil, omeprazole
CYP2D6 Increased antidepressants, antiarrhythmics, chloroquine
CYP3A4 Increased >50% of all drugs including lumefantrine, artemether
UGT Increased lamotrigine (UGT1A4), zidovudine (UGT2B7)
NAT2 Nil isoniazid, hydralazine
61
Tubular secretion increases renal , while tubular reabsorption decreases it. These
processes may be differentially altered for different drugs in pregnancy.306
The number and complexity of the interactions between these changes makes
prediction of changes in drug disposition in pregnancy difficult.307 Ideally, a range of
women from different stages of pregnancy, including post‐partum, should be studied
to determine the changes that occur throughout pregnancy and in the weeks
afterwards. In reality, as is the case for the study presented in this thesis, it is more
common that only a small range of gestational ages are present in a study. This makes
defining a continuous relationship between model parameters and pregnancy difficult.
Often, only a categorical relationship characterising the difference between the three
stages of pregnancy with non‐pregnancy, or the postpartum period, is estimated.305, 307
1.3.4.2 91B91BChildhoodandinfancy
The main PK consideration in childhood is the large change in size over a small age
range.309, 310 Using the allometric model, parameters of and are scaled according
to WT, for example:
70
Equation 1‐18
where represents an individual, is the of individual , is the population
average of the for a 70 kg person, is the weight of the individual , and is
allometric coefficient. The effect of this scaling on CL, V and tR½R is presented as a solid
black line in Figure 1‐17.
Although the denominator (70 kg) does not influence model selection,310 a standard
value of 70 kg is chosen to allow easier comparison between studies, and helps
particularly when comparing studies performed in children with studies of adults.
Other size descriptors besides WT, such as fat free mass, ideal body weight, and body
mass index have been used to describe the PK changes associated with size.311 In a
group of obese subjects, changes in were best described using WT, while changes in
were best described using lean body mass.311 In a study population where there is
62
little difference in the body composition of the individuals, and extremes do not exist,
the choice of size descriptor is likely to be less important. Although the allometric
coefficients may be estimated (often with no significant difference with the standard
coefficients),309 the studies in this thesis were not intended, and therefore not
designed or powered, to estimate them accurately. Therefore, allometry, with
standard coefficients of 1 for volume terms and ¾ for clearance terms,309, 310 has been
used a priori in the models presented.
The second consideration after size in studies involving younger patients is that of
organ maturation.309, 310 This is particularly important in infants where the processes of
hepatic and renal maturation are still occurring to a significant extent. A few models
have been used to describe the process of this maturation.310 Of these, a sigmoid ERmaxR
model is the most plausible,309 and has been used to describe this process for a
number of drugs.310 This model allows for gradual maturation in infancy with the
achievement of ‘adult’ levels with increasing age. Using this model, the effect of
maturation on (or ) can then be combined with the effect of size to give:
70
¾
MATCL Equation 1‐19
where is the post menstrual age, is the Hill coefficient for , and
MATCL is the time to 50% maturation of . Similar maturation also takes place
with .309 An example of the changes of , and tR½R seen over a paediatric WT range
(assuming average WT for age312), and incorporating the effect of size and maturity,
are represented as a dashed red line in Figure 1‐17.
63
Weight (kg)
Vo
lum
e o
f d
istr
ibu
tio
n
Weight (kg)
Cle
ara
nc
e
Weight (kg)
Ha
lf-l
ife
Figure 1‐17 An example of the changes expected in volume, clearance and half‐life over weight and age using average weight for age data312. The solid black line represents changes when only considering allometry while the dashed red line considers both the effects of size and age.
In one study of propofol including infants and young children, 80% of the variability in
could be accounted for by size and maturation.310 This example from the literature
highlights the importance of these two covariates, size and age, in children and infants.
64
65
1.4 10B10BThesisoutline
As presented in this chapter, the literature surrounding the pharmacology of a number
of antimalarial compounds used for prevention in pregnancy and infancy and
treatment in childhood has limitations. This thesis presents a number of publications
that add to this literature. Although each chapter with original data in this thesis
relates to a discrete study, all chapters are concerned with the pharmacology of
antimalarial drugs in PNG. The thesis is therefore ordered to follow the different
groups who are at the greatest risk, from the mother who is made vulnerable by her
pregnancy to the infant who, after being affected in the womb, encounters malaria on
their own after birth, and finally the child who, while still developing their own innate
immunity to the infection, suffers from symptomatic malarial infections.
With respect to the prevention of malaria in pregnant women, a study of AZI is
presented in Chapter 2. Although better known for its use as an antibiotic, AZI shows
antimalarial activity like other antibiotics and it is also one of the few antimalarials
with proven safety in pregnancy. Prior to this work only one limited PK evaluation of
AZI in pregnancy had been published in the literature. As antimalarial activity is likely
to be related to the drug concentration in the blood, the optimal AZI dose regimen for
malaria prevention is likely to be different to that for its use as an antibiotic. In
addition, and since there may be a requirement for AZI to be used in combination for
malaria treatment, this study aimed to compare the PK of AZI in plasma of pregnant
women with non‐pregnant controls when given with CQ or SP, and provide preliminary
information on the safety and tolerability of these combinations.
A study of the prevention of malaria in infancy is presented in Chapter 3. This involved
standard and double dose SP in infants and supplements the worldwide investigation
of SP as IPTi. Given suggestions that the standard dose may not be sufficient in this
population and the consequent risk of poor performance of IPTi, there was a clear
need for a higher dose to be evaluated in this population. By employing a sparse blood
sample collection design, with only small volumes collected, this study aimed to
investigate the PK in this population in PNG and to assess the potential for the use of a
double dose with respect to safety and tolerability.
66
The next section of the thesis relates to the treatment of malaria in childhood and
contains three chapters. The methods used in all three of the studies is similar,
however the drugs investigated differ.
In Chapter 4 a publication of the pharmacology of AL, a widely used ACT, is presented.
Although widely used for the treatment of malaria in children, there are concerns
regarding the adequacy of the currently recommended dose regimen. Additionally,
few data exist regarding DBL, a potent active metabolite of LUM. The aims of this
publication were therefore to add to the current literature regarding the PK of the
components of this combination and to compare the PK in children to those in adults
in order to suggest an appropriate paediatric dose regimen.
Chapter 5 presents a publication in which two ACT combinations containing PQ are
compared. A newer combination containing PQ base (ART/PQ), not yet recommended
by the WHO, was investigated and compared with historical data of an older
combination containing PQ tetraphosphate (DHA/PQ), now recommended by the
WHO. Despite being commercially available, there is no published information on the
safety, tolerability or efficacy of this newer combination in children. This study,
therefore, not only aimed to provide a PK comparison of these two combinations but
also to provide preliminary data of the safety, tolerability and efficacy of ART/PQ in
children.
Another new ACT, ART/NQ, is the subject of the publication presented in Chapter 6.
Very few data exist for this combination, particularly in children, and therefore a study
was carried out in two parts. The first was a pilot where paediatric doses were
extrapolated from the adult doses recommended by the manufacturer, as no
recommendations for children were available at that time. The second used this
information to adjust the dose and compare different dose regimens. The aims of this
study were therefore to provide the first PK evaluation of ART/NQ in children and to
assess the impact of different dose regimens. (Safety, tolerability and efficacy data
from this study are presented separately in the literature).
67
Although a conclusion is provided for each chapter, a general conclusion is outlined in
Chapter 7. This summarises the main findings of the each of the chapters, provides
information of how some of this information has already been used, and suggests
some possible future avenues of research.
68
69
162B162BPREVENTIONOFMALARIA
INPREGNANTWOMEN
70
71
2 1B1BPharmacokineticPropertiesofAzithromycinin
Pregnancy
2.1 11B11BBackground
The primary aim of the study presented in this chapter is to compare the PK of AZI of
pregnant women with non‐pregnancy controls when given with CQ or SP. In addition it
aims to provide preliminary information on the safety and tolerability of these
combinations, particularly in pregnancy, as potential IPTp.
This study was performed at a time when an IPTp trial involving AZI was being planned
in PNG. The PNG health policy for managing malaria in pregnancy consisted of
treatment doses of CQ and SP, followed by weekly CQ. In light of increasing parasite
resistance to these drugs in PNG there was a need to investigate other potential
management options, such as AZI IPTp. Although some information existed to guide
the dose to be used in the trial, there was only sparse PK data for AZI in pregnancy and
little tolerability data for the planned regimen (2g daily for two days). Therefore this
study, based on a previous study performed at the same site,313, 314 was developed to
assist in finalising the dose regimen to be used in the AZI IPTp trial.
This study resulted in the publication3 presented in this chapter. Entitled,
“Pharmacokinetic properties of azithromycin in pregnancy” it was published in the
journal Antimicrobial Agents and Chemotherapy (2010. 54(1):p. 360‐6). The
contribution of each of the authors is outlined in section i, which also contains details
of ethical approvals and supporting funding. While the complete publication is
provided in section xi.a below, it has been reformatted to conform to thesis
requirements set by the University of Western Australia. The references have been
combined with those for the thesis as a whole and can be found in section x below.
72
73
2.2 12B12BPublication
Sam Salman,A Stephen J Rogerson,B Kay Kose,C Susan Griffin,C Servina Gomorai,C
Francesca Baiwog,C Josephine Winmai,C Josin Kandai,C Harin A Karunajeewa,A, D Sean J
O’Halloran,E Peter Siba,C Kenneth F Ilett,A,E Ivo Mueller,C Timothy M E DavisA.
ASchool of Medicine and Pharmacology, University of Western Australia, Perth,
Western Australia, Australia
BFaculty of Medicine, University of Melbourne, Melbourne, Australia
CPapua New Guinea Institute of Medical Research, Madang, Papua New Guinea
DWestern Health, Melbourne, Australia
EClinical Pharmacology and Toxicology Laboratory, Path West Laboratory Medicine,
Nedlands, Australia
2.2.1 38B38BAbstract
AZI is an azalide antibiotic with antimalarial activity that is considered safe in
pregnancy. To assess its PK properties when administered as IPTp, two 2 g doses were
given 24 h apart to 31 pregnant and age‐matched 29 non‐pregnant Papua New
Guinean women. All subjects also received single‐dose SP (1500mg/75mg) or CQ (450
mg base daily for three days). Blood samples were taken at 0, 1, 2, 3, 6, 12, 24, 32, 40,
48 and 72 h and then on days 4, 5, 7, 10 and 14 for AZI assay by ultra high‐performance
liquid chromatography‐tandem mass spectrometry (UPLC‐LC‐MS/MS). The treatments
were well tolerated. Using population PK modelling, a three‐compartment model with
a zero‐ followed by first‐order absorption and no lag time provided the best fit. The
AUCR0–∞R (28.7 and 31.8 mg∙h/l for pregnant and non‐pregnant subjects, respectively)
was consistent with results of previous studies, but the estimated terminal elimination
half‐lives (78 and 77 h, respectively) were generally longer. Among a range of potential
covariates including malarial parasitaemia, the only significant relationship identified
was for pregnancy which accounted for an 86% increase in the V of the central
compartment but there was no significant change in AUCR0‐∞R. These data suggest that
AZI can be combined with longer tR½R compounds such as SP in combination IPTp
without the need for dose adjustment.
74
2.2.2 39B39BIntroduction
AZI is a semi‐synthetic azalide antibiotic that is structurally related to erythromycin but
which has a broader spectrum of antibacterial activity and more favourable PK
profile.262, 315 It is widely used in the treatment of respiratory and sexually‐transmitted
infections, including those in HIV‐infected patients.243, 244 AZI also inhibits protein
synthesis in the plasmodial apicoplast145, 316 and thus has activity against both
Plasmodium falciparum and vivax.240, 242, 317‐322 Its greatest effect is against the progeny
of parasites that inherit a non‐functioning apicoplast after exposure, with the result
that its antimalarial effect is of slow onset and relatively weak. Therefore, AZI is best
used in combination with other antimalarial compounds as both treatment249, 318, 320
and chemoprophylaxis,240, 323 with likely additive or synergistic effects.246, 319, 321
Malaria in pregnancy can result in adverse outcomes for both mother and foetus.28
Intermittent presumptive treatment in pregnancy (IPTp) aims to reduce the burden of
malaria by administering treatment doses of antimalarial drugs at predetermined
intervals as part of routine antenatal care in endemic areas.324 Because AZI is
considered safe in pregnancy and could have activity against other clinically‐significant
pathogens,236, 325 it has been suggested as a candidate for IPTp. Although the PK of AZI
have been investigated previously,256‐262, 265, 326‐330 only one study included pregnant
women330 and most focused on its antibacterial properties. In addition, AZI is likely to
be partnered with conventional antimalarial drugs if given as IPTp, and there is
evidence that such combinations are safe and well tolerated in studies with CQ in
healthy volunteers327 and with SP in pregnant women.249 Although there does not
appear to be a clinically significant PK interaction with CQ,327 AZI interactions with
other conventional IPTp treatments are unknown. We have, therefore, investigated
the PK properties of AZI in combination with CQ or SP in pregnant and non‐pregnant
women from an area of PNG with intense transmission of both falciparum and vivax
malaria.
75
2.2.3 40B40BPatientsandmethods
2.2.3.1 92B92BStudysite,sampleandapprovals
The present study was conducted at Alexishafen Health Centre, Madang Province on
the north coast of PNG. The pregnant women were recruited at their first antenatal
clinic visit and the age‐matched non‐pregnant volunteers from the same communities
as the pregnant participants. Women were eligible if i) they had not taken any of the
study drugs in the previous 28 days, ii) they had no history of significant allergy to any
study drug, iii) there was no significant co‐morbidity or clinical evidence of severe
malaria, and v) follow‐up was possible for the duration of the study. The study was
approved by the Medical Research Advisory Committee of PNG (reference 07.24) and
the Human Ethics Research Committee at the University of Western Australia
(reference RA/4/1/1871). Written informed consent was obtained from all
participants.
2.2.3.2 93B93BClinicalprocedures
A detailed assessment was performed prior to drug administration including a side‐
effects questionnaire, point‐of‐care haemoglobin and blood glucose (HemoCue®,
Angelholm, Sweden), thick and thin blood films, and (for pregnant participants)
estimation of gestational age by fundal height. A 3 ml blood sample was taken for
subsequent antimalarial drug assay. All women received 2 g AZI (Zithromax®, Pfizer,
New York) both at enrolment and 24 h later. Subjects were also randomised to receive
single‐dose SP (1500mg/75mg; Fansidar®, Roche, Basel, Switzerland) at enrolment
(AZI‐SP arm) or CQ (Chloroquin®, Astra, Sydney, Australia) 450 mg base daily for three
days (AZI‐CQ arm) in accordance with regimens recommended for PNG.331
Administration of all doses was directly observed. The dosing schedule for AZI was
chosen as the simplest regimen that would be likely to ensure effective drug
concentrations during the first 4 days of treatment.316
Following the first dose of AZI (day 0), additional blood samples were taken at 1, 2, 3,
6, 12, 24, 32, 40, 48 and 72 h and then on days 4, 5, 7, 10 and 14 for drug assay. The
exact timing of each blood sample was recorded. All samples were centrifuged
promptly with RBCs and separated plasma stored frozen at ‐80oC. The side‐effects
76
questionnaire was re‐administered at 6 h and then at 1, 2, 3 and 7 days. Haemoglobin,
erect and supine heart rate and blood pressure, respiratory rate, temperature and
blood slides were taken on days 1, 2, 3, 7, 14, 28 and 42, and blood glucose was
measured on days 1, 2 and 3. After completion of follow‐up, pregnant patients were
returned to usual antenatal care.
2.2.3.3 94B94BLaboratoryMethods
Giemsa‐stained thick blood smears were examined independently by at least two
skilled microscopists who were blinded to pregnancy and treatment status. Each
microscopist viewed >100 fields at 1,000 x magnification before a slide was considered
negative. Any slide discrepant for positivity/negativity or speciation was referred to a
third microscopist.
AZI concentrations were measured using a validated UPLC‐LC‐MS/MS method using a
deuterated internal standard. The samples have been retained for subsequent SP
assay. AZI USP was obtained from APAC Pharmaceutical LLC (Ellicott City, MD, USA)
and dR3R‐AZI from Toronto Research Chemicals (North York, Canada). In brief, following
addition of internal standard, AZI was extracted from 5 microliters (µl) of plasma by
protein precipitation. After centrifugation, supernatant (5 µl) was injected onto a
2795/Quattro Premier XE UPLC‐ESI‐MS/MS (Waters Corp, MA, USA) using a Waters
BEH CR18R 1.7m, 2.1 x 100mm column. Gradient elution was performed using mobile
phases A (45/55 v/v comprising 1 g/l ammonium bicarbonate in 50/50 v/v
methanol:water and acetonitrile) and B (50/50 v/v methanol/acetonitrile) at 0.4
ml/min. Adduct transitions were monitored using positive electrospray ionization with
multiple reaction monitoring for AZI and dR3R‐AZI were m/z 749.6‐591.4 and m/z 752.6‐
594.4, respectively. The method was linear to 1012 nanogram (ng)/ml (r2>0.9997) with
a limit of quantification of 2.5 g/l AZI. All inter‐ and intraday coefficients of variation
were <10% and between‐patient variability was <5% when matrix effects were
investigated at three concentrations.
2.2.3.4 95B95BPopulationpharmacokineticanalysis
Concentration‐time datasets were analysed by nonlinear mixed effect modelling using
NONMEM (version 6.2.0, ICON Development Solutions, Ellicott City, MD, USA) with an
77
Intel Visual FORTRAN 10.0 compiler. Linear mammillary model subroutines within
NONMEM (ADVAN4 and 12 used with TRANS4 in the PREDPP library), FOCE with ‐
interaction, and the OFV were used to construct and compare plausible models. Unless
otherwise specified, a difference in OFV of ≥6.63 (2 distribution with 1 d.f., P<0.01)
was considered significant. The R‐based model‐building aid Xpose 6.0 was used for
graphic model diagnosis 332. Secondary PK parameters including volume of distribution
at steady‐state (VRSSR, equal to the sum of the V of all compartments), AUCR0–∞Rs and
elimination tR1/2Rs for the non‐pregnant and pregnant groups were obtained from post
hoc Bayesian prediction in NONMEM using the final model parameters. Macro
constants for the three compartment model were calculated from the modelled
parameters using previously published equations. 333
All volume terms were allometrically scaled with (*(WT/70)1.0) and all CL terms with
(*(WT/70)0.75).309 All V and CL parameters were relative to bioavailability (/F).
Two and three compartment models were compared and then zero and first order
absorption models, with and without a lag time, were assessed alone and in
combination. IIV was added to parameters for which it could be estimated reasonably
from available data. For RUV, both exponential (proportional) and combined
(exponential with additive) error models were tested. In the development of the final
models, we investigated the influence of the covariates pregnancy, dosing group,
fundal height, gestational age, malaria status, initial Hb and blood glucose on model
parameters using Xpose and the generalized additive modelling procedure function.
Relationships between these covariates and individual PK parameters were also
explored by inspection of correlation plots. Covariate relationships identified by this
procedure were evaluated within the NONMEM model. Inclusion of the covariate
required a decrease of ≥3.84 in OFV (2 d.f.=2, P<0.05) and a decrease in the IIV.
Correlations among IIV terms and WRES plots were used in model evaluation.
A bootstrap procedure using Perl speaks NONMEM (PSN) was used to sample
individuals from the original dataset with replacement and generate 1000 new
datasets that were subsequently analysed using NONMEM. The resulting parameters
were then summarized as median and 2.5th and 97.5th percentiles (95% empirical CI) to
78
facilitate validation of the final model parameter estimates. In addition, a stratified
VPC was also performed using PSN with 1000 replicate datasets simulated from the
original. The resulting 80% PI for AZI were plotted with the observed data to assess the
predictive performance of the model.
2.2.3.5 96B96BStatisticalanalysis
SigmaStat® (version 3.10, Systat Software Inc, Chicago, IL, USA) was used for statistical
analysis unless otherwise specified. Data are summarized as mean ± standard
deviation (SD) or median and inter‐quartile range (IQR) as appropriate. Student’s t‐
test or the Mann‐Whitney U test was used for two‐sample comparisons. Categorical
data were compared using either Pearson Chi squared or Fisher’s exact test, and
multiple means by repeated measures ANOVA. A two‐tailed level of significance of
0.05 was used. Drug concentrations at each time point after day 2 were compared to
AUCR0–∞R using Pearson correlation.
79
2.2.4 41B41BResults
2.2.4.1 97B97BPatientcharacteristics
A total of 31 pregnant and 29 non‐pregnant women were recruited between October
2007 and March 2008. All subjects took two AZI doses but two pregnant patients did
not receive either CQ or SP. These women were excluded from initial analyses but
were included subsequently if there was no effect of CQ or SP on AZI PK properties in
the other subjects. Baseline characteristics of the subjects by pregnancy status and
treatment allocation are shown in Table 2‐1. The groups were well matched except
that, consistent with normal physiological changes that occur in pregnancy,307, 334 the
pregnant subjects were significantly heavier and had a lower haemoglobin than the
non‐pregnant subjects for each treatment group (P<0.05). Seven of the pregnant
patients were parasitaemic at baseline compared with only one of the non‐pregnant
subjects (P=0.02).
Table 2‐1 Baseline characteristics of the study participants by pregnancy status and treatment allocation. Data are mean ± SD, median [IQR] or number (%).
Pregnant Non‐pregnant
AZI‐CQ
(n=15)
AZI‐SP
(n=14)
AZI‐CQ
(n=14)
AZI‐SP
(n=15)
Age (years) 26.9 ± 4.1 23.9 ± 5.1 25.7 ± 5.8 27 ± 6.5
Weight (kg) 53.5 ± 7.1a 56.4 ± 7.9a 51.4 ± 5.4 51.9 ± 4.9
Height (cm) 154 ± 7.4 154 ± 7.3 154 ± 6.4 154 ± 2.8
Axillary temperature (°C) 36.4 ± 0.7 36.5 ± 0.6 36.7 ± 0.3 36.4 ± 0.3
P. falciparum parasitaemia 3 (20) 3 (21) 1 (7) 0 (0)
P. vivax parasitaemia 1 (7) 0 (0) 0 (0) 1 (7)
Gestational age (weeks) 24 [22‐27] 21 [19‐24]
Gravidity 3 [2‐5] 2 [1‐4] 1 [0‐3] 2 [0‐3]
Parity 2 [1‐4] 1 [0‐2] 0 [0‐3] 1 [0‐3]
Respiratory rate (/min) 20 ± 1 22 ± 5 20 ± 2 20 ± 1
Supine pulse rate (/min) 91 ± 10 89 ± 7 82 ± 10 88 ± 7
Supine MAP (mm Hg)b 78 ± 7 81 ± 10 79 ± 9 82 ± 7
Haemoglobin (g/dl) 8.5 ± 1.6a 8.2 ± 1.2a 9.3 ± 1.9 10 ± 1.3
Blood glucose (mmol/l) 5.9 ± 1.6 5.7 ± 0.8 6.2 ± 1.1 5.6 ± 2.7 a P<0.05 vs. non‐pregnant subjects; b mean arterial pressure, calculated by adding 1/3 of the pulse pressure (systolic minus diastolic pressure) to the diastolic pressure
80
2.2.4.2 98B98BEfficacy,tolerabilityandsafety
Three of the seven falciparum and one of the two vivax cases at baseline received AZI‐
SP. There was an uncorrected adequate parasitological and clinical response (APCR) of
100% for both treatments. A further eight cases (five of whom were pregnant) became
slide‐positive for P. falciparum and three (two who were pregnant) for P. vivax late in
the 42 day follow‐up period. All received recommended antimalarial therapy.331 All
cases at baseline and during follow‐up were asymptomatic.
Both treatments were well tolerated. Table 2‐2 summarizes the self‐reported side‐
effects in the first week of follow‐up, all of which were mild (defined as not bad
enough to interfere with daily activity) and generally short‐lived (≤2 days). Six patients
reported mild symptoms before drug administration (headache, abdominal pain,
pruritus or dizziness) but these resolved subsequently. Pruritus was only reported in
the AZI‐CQ group (P=0.052). Although not formally assessed, no significant side‐effects
were volunteered at assessments beyond day 7. No patient developed hypoglycaemia
(blood glucose <2.5 mmol/l) or severe anaemia (haemoglobin <5.0 g/decilitre [dl])
Table 2‐2 Side‐effects reported during the first week after initiation of treatment. Data are numbers of patients and (%).
AZI‐CQ
(n=29)
AZI‐SP
(n=29)
Fever 2 (7) 1 (3)
Chills 2 (7) 0 (0)
Headache 6 (21) 4 (14)
Nausea 4 (14) 7 (24)
Vomiting 2 (7) 4 (14)
Diarrhoea 2 (7) 2 (7)
Abdominal pain 4 (14) 3 (10)
Rash 0 (0) 0 (0)
Pruritus 5 (17) 0 (0)
Anorexia 1 (3) 2 (7)
Insomnia 2 (7) 0 (0)
Dizziness 3 (10) 1 (3)
Bone or joint pain 1 (3) 1 (3)
Othera 5 (17) 1 (3) a Cough (2), blocked ear (1), ‘heavy head’ (1), and numbness of calf muscles (1) in the AZI‐CQ group and cough (1) in the AZI‐SP group.
81
during follow‐up. Although postural hypotension (>20 mmHg systolic or >10 mmHg
diastolic fall after standing) occurred 8 times in 7 pregnant (4 from the AZI‐CQ group)
and 7 times in 5 non‐pregnant patients (all 5 in the AZI‐CQ group), differences between
groups were not significant and there were no associated symptoms. After completion
of the study, one of the study participants had a still birth. A medical review of her
case notes by three independent physicians concluded that it was unlikely to be the
result of study medication.
2.2.4.3 99B99BPharmacokineticmodelling
A three compartment model had a lower OFV value than a two compartment model
(8700.058 vs. 8185.104; P<0.001 by 2 test, d.f.=2) and a more favourable distribution
of WRES over time. A zero‐ followed by first‐order absorption without a lag time
provided the lowest OFV and best fit for AZI absorption. The fixed model parameters
Table 2‐3 Model building, final parameter estimates and bootstrap results from the AZI population pharmacokinetic modelling.
Parameter Base model
estimate (%RSE)
Final model
estimate (%RSE)
Bootstrap (n=1000) median [95% CI]
Structural and covariate model parameters
DUR (h) 1.66 (10.4) 1.55 (3.3) 1.56 [1.21‐2.01]
kRa R(/h) 0.513 (3.2) 0.525 (14.8) 0.524 [0.451‐0.623]
VRCR/F (l) 504 (13.9) 384 (17.6) 371 [235‐554]
Pregnancy on VRCR (l) 330 (69.4) 318 [48‐604]
CL/F (l/h) 158 (3.9) 158 (6.7) 158 [145‐171]
VRP1R/FR R(l) 4080 (8.6) 4080 (12.5) 4045 [3402‐4870]
QR1R/F (l/h) 327 (5.7) 325 (12.7) 326 [288‐368]
VRP2R/FR R(l) 5070 (5.6) 5040 (7.3) 5070 [4262‐5730]
QR2R/F (l/h) 67.2 (11.5) 66.4 (12.4) 67.5 [48.5‐84.0]
Random model parameters
IIV VRcR/F 111.4 (20.9) 99.6 (35.5) 99.0 [72.6‐127.6]
IIV CL/F 28.3 (24.1) 28.3 (33.1) 27.9 [21.6‐34.5]
IIV VRP1R/F 35.8 (27.0) 35.6 (27.2) 34.8 [25.7‐45.2]
IIV DUR 73.0 (21.4) 76.9 (22) 75.5 [55.4‐95.6]
RUV (%) 31.3 (9.5) 31.2 (15.1) 30.9 [28.1‐33.8]
OFV in base model: 7999.870, OFV in final model: 7993.646, bootstrap OFV (median [95% CI]) 7974.699 [7756.673‐8201.238]
82
were DUR (the duration of the zero order absorption), kRaR, CL/F, VRCR/F, VRP1R/F, VRP2R/F,
QR1R/F and QR2R/F. The model structure is shown in Figure 2‐1. IIV could be estimated on
DUR, CL/F, VRcR/F and VRP1R/F while a proportional error model was best for RUV. After
Figure 2‐1 Structural model used in the final pharmacokinetic analysis of plasma azithromycin concentrations in the central compartment versus time.
Time (h)
1 10 100 1000
We
igh
ted
re
sid
ua
ls (
azi
thro
my
cin
)
-6
-4
-2
0
2
4
6
Predicted plasma azithromycin (g/l)
10 100 1000
Ob
se
rve
d p
las
ma
azi
thro
my
cin
(
g/l)
10
100
1000
A
B
Figure 2‐2(A) Population (○) and individual (●) predicted versus observed plasma azithromycin concentra ons (µg/l on log10 scale) for the final model. The line of identity is also shown. (B) Weighted residuals vs. time (log scale) for azithromycin final model.
83
testing the various covariates, only pregnancy on VRCR/F produced a significant decrease
in the
OFV (2 d.f.=1, P<0.05) accompanied by a decrease in the IIV of VRCR/F from 111.0 % to
99.6 %.
The results of the parameter estimates and their relative standard errors (RSE) are
summarized in Table 2‐3 and secondary parameter estimates in Table 2‐4. All drug
concentrations after day 2 were strongly correlated with the AUCR0–∞R (r>0.7; P<0.001,)
with 96 h concentrations showing the strongest association (r=0.78). The bootstrap
results (see Table 2‐3) demonstrate robust estimation of both fixed and random
parameters with bias <4% and <5%, respectively. GOF plots of observed versus
population and individual predicted concentration, and WRES versus time are shown in
Figure 2‐2.The VPC results, stratified for pregnancy status, are presented in Figure 2‐3
and show reasonable predictive performance of the model, while demonstrating some
difficulty in capturing post‐absorption plasma concentrations peaks.
Table 2‐4 Secondary pharmacokinetic parameters derived from post hoc Bayesian estimates for pregnant and non‐pregnant study participants (median [IQR]).
Parameter Pregnant (n=31) Non‐pregnant (n=29) P‐value
DUR (h) 1.65 [0.94 – 2.34] 1.75 [1.02 – 2.38] NS a
kRaR (/h) 0.525 [0.525 – 0.525] 0.525 [0.525 – 0.525] NS
VRCR/F (l) 647 [422 – 995] 249 [157 – 363] < 0.001
VRP1R/F (l) 3,620 [2,747 – 3,951] 2,909 [2,296 – 3,586] NS
VRP2R/F (l) 3,888 [3,708 – 4,104] 3,672 [3,456 – 3,888] 0.034
VRSSR/F (l) 8,355 [7,460 – 8,973] 6,875 [6,115 – 7,526] 0.002
tR½αR
b (h) 0.88 [0.57 – 1.36] 0.39 [0.24 – 0.56] < 0.001
tR½βR
b (h) 20.7 [18.3 – 22.8] 18.8 [15.3 – 21] NS
tR½γR
b (h) 78.2 [74 – 82.5] 77.1 [71.5 – 84.5] NS
AUCR0–∞R (g19T∙19Th/l) 28,713 [25,913 – 32,942] 31,781 [28,736 – 38,012] NS a NS, not significant, b tR½αR, tR½βR and tR½γR are the first distribution, second distribution and terminal elimination half‐lives respectively.
84
2.2.5 42B42BDiscussion
The present study is the first PK evaluation of AZI in pregnant and non‐pregnant
women living in a malaria‐endemic area. We found that a three compartment model
with a combined absorption process best described the disposition of AZI in our
subjects. Both two256, 257, 259 and three compartment258, 260 models have been found to
best describe AZI plasma concentration‐time profiles in other contexts. Our ability to
differentiate the tri‐exponential elimination of AZI may have been facilitated by the
relatively long sampling duration. This may also explain why our estimated terminal
elimination tR½R (78 and 77 h for pregnant and non‐pregnant participants, respectively)
was longer than in most previous studies (range 27‐79 h).256‐258, 260‐262 Overall drug
exposure (AUCR0–∞R 28.7 and 31.8 mg∙h/l for pregnant and non‐pregnant subjects,
respectively) was within the range expected from dose‐scaled results from previous
studies in other contexts (26.5‐46.4 mg∙h/l),256, 257, 259‐262, 265, 326, 327 suggesting that the
bioavailability of AZI is not dose‐dependent.
Both zero256, 257 and first258, 259 order absorption have been reported previously for AZI
but neither was appropriate for our data. A combined absorption process where the
drug enters the absorption compartment in a zero order manner and then is absorbed
according to first order kinetics provided the best model in the present study. This is
analogous to the twin processes of i) gastric emptying of the drug into the small
Time (h)
1 4 10 40 100 400
AZ
I (
g/L
)
1
10
100
1000
10000
Time (h)
1 4 10 40 100 400
AZ
I (
g/L
)
1
10
100
1000
10000A B
Figure 2‐3 Visual predicted check plots showing simulated 10th (short dashed line), 50th (dotted line) and 90th (solid line) percentile concentrations and observed concentration (log scale) data (grey open circles) versus time (log scale) for non‐pregnant (A) and pregnant (B) participants.
85
intestine (the zero order process) and ii) absorption in the small intestine proportional
to the amount present (the first order process). Despite this more complex model, AZI
absorption was still not well characterized in our final model. This has been reported
previously257 but is unlikely to be significant in the treatment of uncomplicated malaria
where exposure of the parasite to therapeutic drug concentrations over several life‐
cycles is more important than that immediately after drug administration.
Plasma AZI concentrations appeared to differ between pregnant and non‐pregnant
women only in the first 48 h after the first dose. This was confirmed by the population
PK modelling in which pregnancy, the only significant covariate relationship, accounted
for an 86% increase in VRCR/F. Despite significant differences in the secondary PK
parameters VRCR/F, VRP2R/F, VRSSR/F and tR½αR between pregnant and non‐pregnant subjects,
no difference was seen in tR½γR or AUCR0–∞R. This suggests that drug elimination and
overall exposure are similar in the two groups. A much shorter AZI tR½R (12 h) than in the
present study has been reported previously in pregnant women,330 but this study
employed a shorter sampling duration (168 vs. 336 h), included pregnant women at or
near term, and the analysis was constrained by relatively sparse sampling.
Because of the need for AZI to be combined with other therapies,317, 322 we included
conventional antimalarial drugs currently recommended as part of IPTp in PNG and
other countries.249, 250 There were no significant differences in the disposition of AZI
between the AZI‐CQ and AZI‐SP groups, consistent with a study of the interaction
between CQ and AZI in healthy volunteers.327 We conclude that AZI dose modification
is unnecessary in these combinations. In addition, the lack of an effect of malaria
status as a covariate on AZI disposition suggests that, unlike drugs such as QN,335 the
dose may not have to be adjusted when parasitaemia is present.
The most common side‐effects of AZI, especially with higher doses, are nausea and
vomiting. These symptoms are thought to relate to effect of AZI on the motilin
receptor in the upper gastrointestinal tract.239 However, with the exception of pruritus
which tended to be associated with AZI‐CQ therapy consistent with known CQ
effects,336 there were no differences in the incidence of side‐effects between the two
treatment groups and all reported adverse effects were mild. The AZI dose regimen in
86
both combination therapy groups in the present study (2.0 g daily for two days) was
associated with a similar side‐effect profile to that reported previously after a single
2.0 g dose.260 Use of the sustained release formulation of AZI should reduce side‐
effects including nausea and vomiting.337 However, this formulation has a
bioavailability of 82.8% relative to conventional AZI, suggesting that a higher dose will
be required to achieve the same drug exposure. As well as increasing the cost of AZI
treatment, this could mean that side‐effects are more frequent with higher‐dose
sustained release AZI administration.
Although the present study had limited subject numbers, it is encouraging that both
regimens achieved a 100% uncorrected APCR. The plasma concentrations of AZI
required to achieve cure are unknown as no efficacy trials have included these data.
However, the high correlation between 96 h drug concentrations and AUCR0–∞R in our
patients suggests that a day 4 plasma concentration could be an appropriate surrogate
for overall AZI exposure in efficacy trials in which serial blood sampling is problematic.
It is of interest that prolongation of the in vitro exposure of P. falciparum to 96 h
results in substantially increased potency, suggesting that either AZI renders second‐
generation parasites unable to establish a parasitophorous vacuole upon host cell
invasion or the effect on apicoplast protein synthesis inhibits successful development
of the progeny of drug‐treated parasites.316
Given the need for relatively prolonged parasite exposure to therapeutic plasma
concentrations, it is unlikely that the benefit of “front loading” of AZI used in treating
bacterial infections256, 265 will be relevant in malaria. However, experience with AZI as
an antimalarial agent is growing. A Cochrane review of its efficacy is currently
underway338 and promising results are being seen when used with SP in IPTp, such as
might be given at least twice during pregnancy.249 The present study provides a PK
foundation for the further investigation of AZI as an antimalarial agent in pregnancy,
particularly in combination IPTp. Further data from the present study should also
determine whether AZI influences the disposition of CQ and SP. Although there was a
significant increase in AZI VRCR/F in pregnant women, there was no significant change in
AUCR0‐∞R, and it is therefore likely that no dose adjustments will be required for
pregnant women when AZI is given in combination with CQ or SP.
87
2.2.6 43B43BAcknowledgements
We are most grateful to Sr Valsi Kurian and the staff of Alexishafen Health Centre for
their kind co‐operation during the study. We also thank Christine Kalopo and Bernard
(“Ben”) Maamu for clinical and/or logistic assistance. The study was funded by the
National Health and Medical Research Council (NHMRC) of Australia (grant 458555).
TMED is supported by an NHMRC Practitioner Fellowship.
88
89
163B163BPREVENTIONOFMALARIA
ININFANTS
90
91
3 2B2BPharmacokineticPropertiesofConventional
andDouble‐DoseSulfadoxine‐Pyrimethamine
GivenasIntermittentPreventiveTreatmentin
Infancy
3.1 13B13BBackground
The study in this chapter aims to investigate the PK of conventional and double dose
SP in infants in PNG as well as to assess the safety and tolerability of a double dose as a
potential IPTi.
At the time this study was conceived a trial of SP IPTi was ongoing in the same area.
There is some evidence that the PK of SDX and PYR are different in younger children
when compared to older children or adults. In particular, there was suggestion that
these younger children may be under‐dosed. The role of this PK study was to
investigate the potential use of a higher SP dose for IPTi and to aid in the
interpretation of the results of the SP IPTi efficacy trial.
This study resulted in the publication1 presented in this chapter. Entitled,
“Pharmacokinetic properties of conventional and double‐dose sulfadoxine‐
pyrimethamine given as intermittent preventive treatment in infancy” it was published
in the journal Antimicrobial Agents and Chemotherapy (2011. 55(4):p. 1693‐700). The
contribution of each of the authors is outlined in section i, which also contains details
of ethical approvals and supporting funding. While the complete publication is
provided in section xi.a below, it has been reformatted to conform to thesis
requirements set by the University of Western Australia. The references have been
combined with those for the thesis as a whole and can be found in section x below.
92
93
3.2 14B14BPublication
Sam Salman,A Susan Griffin,B Kay Kose,C Nolene Pitus,B Josephine Winmai,B Brioni
Moore,A Peter Siba,B Kenneth F Ilett,A,C Ivo Mueller,B Timothy M E DavisA.
ASchool of Medicine and Pharmacology, University of Western Australia, Perth,
Western Australia, Australia
BPapua New Guinea Institute of Medical Research, Madang, Papua New Guinea
CClinical Pharmacology and Toxicology Laboratory, Path West Laboratory Medicine,
Nedlands, Australia
3.2.1 44B44BAbstract
IPTi entails routine administration of antimalarial treatment doses at specified times in
at‐risk infants. SP is a combination that has been used as first‐line IPTi. Because of
limited PK data and suggestions that higher mg/kg paediatric doses than
recommended should be considered, we assessed SP disposition in 70 Papua New
Guinean children aged 2‐13 months randomized to conventional (25/1.25 mg/kg) or
double (50/2.5 mg/kg) dose. Blood samples were drawn at baseline, 28 days and three
time‐points randomly selected for each infant at 4‐8 h, or 2, 5, 7, 14 or 21 d. Plasma
SDX, PYR and NR4R‐acetylsulfadoxine (NSX, principal metabolite of SDX) were assayed by
high‐performance liquid chromatography (HPLC). Using population modelling
incorporating hepatic maturation and cystatin C (CysC)‐based renal function, two‐
compartment models provided best fits for PYR and SDX/NSX plasma concentration
profiles. AUCR0–∞R was greater with double vs. conventional dose for PYR (4,915 vs.
2,844 μg∙d/l) and SDX (2,434 vs. 1,460 mg∙d/l). There was a 32% reduction in SDX
relative bioavailability with double‐dose but no evidence of dose‐dependent
metabolism. Terminal elimination half‐lives (15.6 days for PYR, 9.1 days for SDX) were
longer than previously reported. Both doses were well tolerated without changes in Hb
or hepatorenal function. Five children in the conventional and three in the double‐
dose group developed malaria during follow‐up. These data support the potential use
of double‐dose SP in infancy but further studies should examine the influence of
hepatorenal maturation in very young infants.
94
3.2.2 45B45BIntroduction
IPTi is a strategy in which infants in malaria‐endemic areas are given treatment doses
of antimalarial drugs at specified times, regardless of clinical and parasitological status.
Because of its availability, tolerability and relatively low cost, SP has been used first‐
line in IPTi programs, especially in Africa. A recent review of safety and efficacy data
from six trials conducted from 1999 to 2007 revealed that, despite the emergence of
molecular markers of parasite resistance, SP IPTi reduced clinical malaria and malaria‐
related hospital admissions by about one‐third, and anaemia in the first year of life by
15%.339 The duration of effective antimalarial prophylaxis after a dose of SP is 4‐6
weeks.62, 340
There is evidence that the efficacy of SP IPTi is dose‐dependent. When given as a fixed
dose,341 efficacy declines with age as lower mg/kg doses are taken.62 In addition,
studies of older children aged 2‐5 years with falciparum malaria have found higher
CL/F and larger V/F for both SDX and PYR than those in adults.226 Consistent with these
data, a population PK study in children with congenital toxoplasmosis showed that the
elimination half‐lives for both drugs were directly related to WT, with the consequence
that younger, and thus lighter children, had more rapid elimination.227 These studies
suggest that the peak plasma concentration and AUC will be reduced in younger
children and that currently recommended doses of SP of 25 mg/kg and 1.25 mg/kg,
respectively, may be inadequate for full efficacy. Indeed, there is evidence that higher
blood PYR concentrations enhance the ability of paediatric patients to clear resistant
Plasmodium falciparum.342
In view of these data and calls for doubling of the recommended treatment dose in
children aged 2‐5 years,226 we assessed the tolerability, safety and PK properties of SP
given in recommended and double recommended doses to infants living an area of
intense malaria transmission in PNG.
95
3.2.3 46B46BPatientsandmethods
3.2.3.1 100B100BStudysite,sampleandapprovals
The present study was conducted at Alexishafen Health Centre, Madang Province on
the north coast of PNG. Infants between the ages of 2 and 13 months from the
surrounding area were eligible for recruitment provided that they i) did not have
features of severe malaria or significant non‐malarial illness, ii) had not been treated
with SDX or PYR in the previous four weeks, iii) did not have a known allergy to either
SDX or PYR, and iv) were available for assessment for the duration of follow‐up.
Written informed consent was obtained from the parents/guardians of all recruited
infants. The study was approved by the Medical Research Advisory Committee of PNG
and the Human Ethics Research Committee at the University of Western Australia.
3.2.3.2 101B101BClinicalprocedures
At enrolment, a clinical assessment was performed that included a standard baseline
symptom questionnaire completed by parents/guardians. A 500 µl finger prick capillary
blood sample was taken for preparation of blood smears for microscopy, baseline drug
assay, biochemical tests and Hb concentration (HemoCue®, Angelholm, Sweden).
Subjects were randomized to receive either the recommended dose of SP (25/1.25
mg/kg Fansidar®, Roche, Basel, Switzerland) or a double dose (50/2.5 mg/kg). Table
3‐1 shows the dose administered based on WT. All dosing was directly observed with
subsequent monitoring and re‐administration of the dose if the infant vomited within
30 minutes. Infants with a positive blood film were also given a three‐day course of AQ
according to PNG national treatment guidelines. 343 All drugs were crushed and mixed
with either water or breast milk before administration by mouth using a syringe.
All infants were re‐assessed on days 1, 2, 3, 5, 7, 14, 21 and 28. A Hb concentration
was determined on each occasion and a repeat symptom questionnaire administered
Table 3‐1 Dosing guide for conventional and double‐dose groups with the SDX/PYR doses in mg given in parentheses.
Body weight Conventional dose Double dose
3‐5.9 kg ¼ tablet (125/6.25) ½ tablet (250/12.5)
6‐11.9 kg ½ tablet (250/12.5) 1 tablet (500/25)
96
at each visit up to day 7. Blood films were repeated on day 28 and/or when fever or a
recent history of a fever was reported. For PK analysis, four additional 500 µl capillary
blood samples were taken from each infant. The first three of these were randomly
selected for each infant from either 4‐8 h or 2, 5, 7, 14 or 21 days post‐dose. A final
sample was taken in all cases on day 28. The exact timing of each blood sample was
recorded. All samples were centrifuged promptly with RBCs and separated plasma
stored frozen at ‐80oC until assay.
3.2.3.3 102B102BLaboratoryMethods
Giemsa‐stained thick blood smears were examined independently by at least two
skilled microscopists who were blinded to dose group. Each microscopist viewed >100
fields at 1,000 x magnification before a slide was considered negative. Any slide
discrepant for positivity/negativity, or speciation was referred to a third microscopist.
CysC concentrations were measured by particle enhanced immunoturbidimetry
(PETIA) using the Tina‐quant CysC kit run on an Elecsys 2010 analyser (Roche,
Indianapolis, USA). Sodium, urea, creatinine, albumin, γ‐glutamyl transferase and
bilirubin were measured using an Integra 800 analyser (Roche) when sufficient plasma
was available.
SDX, sulfamethazine and PYR were obtained from Sigma‐Aldrich (Castle Hill, Australia)
and midazolam hydrochloride from Pfizer (West Ryde, Australia). NSX was synthesized
according to the method of Whelpton et al.344 and found to have a melting point of
230oC and >99.9% purity by HPLC. Acetonitrile was obtained from Merck (Damstadt,
Germany). All other chemicals were of analytical or HPLC grade.
For PYR, SDX and NSX, extraction and separation were performed based on previously
published HPLC‐ultraviolet (UV) methods.227, 314 The internal standards were
midazolam HCl for PYR and sulfamethazine for SDX and NSX. Analytes were assayed
using UV detection at 270 nm. Chemstation Software (version 9, Agilent Technology,
Waldbronn, Germany) was used for analysis of chromatograms. Standard curves were
linear from 5‐1000 microgram/l (µg/l), 0.1‐200 mg/l and 0.02‐10 mg/l for PYR, SDX and
NSX respectively. Intra‐ and inter‐day relative SD (RSDs) were <15% for all analytes at
97
all concentrations. The limits of quantification (LOQ) were 2.5 µg/l, 0.1 mg/l and 0.02
mg/l and the limits of detection (LOD, determined as a signal‐to‐noise ratio of 5) were
1 µg/l, 0.05 mg/l and 0.01 mg/l for PYR, SDX and NSX respectively.
3.2.3.4 103B103BPopulationpharmacokineticanalysis
LogReR concentration vs. time datasets for PYR, SDX and NSX were analysed by nonlinear
mixed effect modelling using NONMEM (version 6.2.0, ICON Development Solutions,
Ellicott City, MD) with an Intel Visual FORTRAN 10.0 compiler. Linear mammillary
model subroutines within NONMEM (ADVAN2/TRANS2 and ADVAN4/TRANS4), FOCE
with ‐ interaction, and OFV were used to construct and compare plausible models.
Unless otherwise specified, a difference in OFV ≥6.63 (2 distribution with 1 d.f.,
P<0.01) was considered significant. Due to the small number of samples with low
concentrations, those below the LOD were not included in the analysis while
concentrations between the LOD and LOQ were kept at their measured
concentrations.
As the subjects were infants with a range of ages, it was important to incorporate
maturation of CL into the model. Therefore total clearance (CLRTR) was defined as the
sum of hepatic clearance (CLRHR) and renal clearance (CLRRR), i.e. CLRTR=CLRHR+CLRRR. The age‐
adjusted hepatic clearance CLRHR was determined using a sigmoid ERmaxR model345 as
TVCLRH Rx (PMAHillCL/(PMAHillCL+MATCLR50R
HillCL)), where TVCLRHR is the population average
value for hepatic clearance, PMA is the postmenstrual age (the age of the infant
recorded from the last menstrual cycle of the mother during pregnancy rather than
birth), HillCL is the Hill coefficient for hepatic clearance and MATCLR50R is the PMA at
which CLRHR is 50% of the mature value. When an accurate PMA could not be obtained it
was estimated from the postnatal age (PNA) and average gestation in PNG.346‐348 CLRRR
was adjusted to a standardized value for an estimated GFR(eGFR) of 120
ml/min/1.76m2, i.e. TVCLRRR x (eGFR/120), where CLRRR is the adjusted renal clearance,
TVCLRRR is the population average value for renal clearance, and the eGFR was
determined from the CysC concentration as 91.62 x (1/CysC1.123).349
Allometric scaling using WT was also used on all volume (*(WT/70)1.0) and clearance
(*(WT/70)0.75) terms. One‐ and two‐compartment models with first order absorption
98
without lag time were assessed for both SDX and PYR. As few data exist to describe the
absorption phase of both drugs, kRaR was fixed to previously published value for
infants.350 IIV was added to parameters for which it could be estimated reasonably
from available data. As logReR concentration data were used, an additive model
(representing proportional error) was used for RUV.
In the development of the final models, we investigated the influence of the covariates
dosing group, relative dose (mg/kg), PMA, malaria status, concomitant treatment with
AQ, and initial Hb concentration using the generalized additive modelling procedure
within Xpose and by inspection of correlation plots. Covariate relationships identified
by this procedure were evaluated within the NONMEM model and inclusion of the
covariate required a significant decrease in OFV accompanied by a decrease in the IIV
of that parameter. Correlations among IIV terms and WRES plots were also used in
model evaluation.
Once a final model for SDX was obtained the parameter estimates were fixed and an
additional compartment was added in order to model NSX concentrations. In order to
allow identifiability in the model, the percentage conversion of SDX to NSX was fixed to
60% based on information from the product information.225 The elimination of NSX
was assumed to be entirely renal.351 The influence of the covariates was assessed on
new model parameters using the method described above.
A bootstrap procedure using Perl speaks NONMEM (PSN) and the resulting parameters
were then summarized as median and 2.5th and 97.5th percentiles (95% empirical CI) to
facilitate validation of the final model parameter estimates. In addition, stratified VPCs
and NPCs were also performed using PSN with 1000 replicate datasets simulated from
the original dataset. NPCs stratified according to PMA were assessed by comparing the
actual with the expected number of data points within the 20, 40, 60, 80, 90 and 95%
PI. For VPCs the resulting 80% PI for drug concentrations were plotted with the
observed data to assess the predictive performance of the model.
99
3.2.3.5 104B104BStatisticalanalysis
As previously reported in a study of SP PK in pregnant vs. non‐pregnant women,314 and
using estimates of centrality and variance for PK parameters from previous paediatric
studies226, 227, 230, 342, 352 and an assumed 20% attrition rate, a sample size of 35 in each
group in the present study would be expected to show a >30% increase in the
magnitude of any PK parameter in the double‐dose group at α=0.05 and β=0.1. SPSS
17.0 (SPSS inc. Chicago, IL, USA) was used for all statistical analysis unless otherwise
specified. Data are summarized as mean±SD or median [IQR] as appropriate. Student’s
t‐test or the Mann‐Whitney U test was used for two‐sample comparisons. Categorical
data were compared using either Pearson Chi‐squared or Fisher’s exact test, and
multiple means by repeated measures ANOVA. A two‐tailed level of significance of
0.05 was used.
100
3.2.4 47B47BResults
3.2.4.1 105B105BPatientcharacteristics
Seventy infants were enrolled between April 2008 and December 2008 with equal
numbers in each dose group. Baseline subject characteristics are summarized in Table
3‐2. The double‐dose group received a significantly higher mg/kg dose than the
conventional dose group (P<0.001) and was taller by a mean of 4.3 cm (P=0.015). The
double‐dose group was also older (by a mean of 47 days) and heavier (by 0.4 kg) than
the conventional dose group but these differences were not statistically significant
(P>0.05).
3.2.4.2 106B106BTolerability,safetyandefficacy
Both doses were well tolerated. There were no changes in symptoms in either group
compared to pre‐dose profiles, including an absence of dermatological conditions.
There were no significant changes in Hb, or in plasma urea, creatinine or CysC, over
time. In the conventional dose group, there was a significant but transient mean fall in
plasma albumin of 2 g/l at day 2 (from 38‐36 g/l, P<0.01), but there were no
concomitant increases in plasma bilirubin or hepatic enzymes in either group.
Table 3‐2 Baseline characteristics of study participants. Data are number (%), mean±SD or median [IQR].
Conventional dose
(n=35)
Double dose
(n=35)
Postmenstrual age (days) 454 [383‐513] 501 [428‐532]
Sex (% male) 22 (63%) 24 (69%)
Weight (kg) 6.58 ± 1.31 6.98 ± 1.1
Height (cm) 61.8 ± 6.5 66.1 ± 7.8
Axillary temperature (°C) 36.5 ± 0.6 36.4 ± 0.6
P. falciparum parasitaemiaa 1 (3%) 0 (0 %)
P. vivax parasitaemiaa 3 (9%) 3 (9%)
Respiratory rate (/min) 40 ± 11 42 ± 11
Supine pulse rate (/min) 133 ± 14 133 ± 15
Mean upper arm circumference (cm) 13.2 ± 3.5 13.7 ± 2.6
Haemoglobin (g/l) 9.5 ± 1.3 9.5 ± 1.2
eGFR (ml/min/1.73m2) 80 ± 20 84 ± 16
Sulfadoxine dose (mg/kg)b 35.6 ± 5.6 67.1 ± 12.6
Pyrimethamine dose (mg/kg)b 1.8 ± 0.3 3.4 ± 0.6 a one infant had a mixed P. vivax/falciparum infection, b P<0.001
101
Five infants with vivax malaria and one infant with a mixed P. vivax/ falciparum
infection at enrolment responded to treatment. Three other infants in the
conventional dose group and two in the double‐dose group were administered
antimalarial drugs during follow‐up at an external health care facility and no blood
smears were available for review. No other subjects became symptomatic during the
study. Only two infants in the conventional dosing group and one in the double dosing
group who were aparasitaemic at entry had a positive blood slide on day 28 (all for P.
vivax). All were asymptomatic and each was treated according to PNG national
treatment guidelines.
3.2.4.3 107B107BPharmacokineticmodelling
There were 248, 255 and 247 drug concentration measurements available for PK
modelling for PYR, SDX and NSX respectively. There were four samples with PYR
concentrations between the LOD and LOQ and a further four with concentrations
Table 3‐3 Final population PK parameters and bootstrap results for PYR.
Final model
estimate (RSE%)
Bootstrap (n=1000)
median [95% CI]
Structural and covariate model parameters
kRaR (/h) 0.779 FIXED
VRCR/F (l/70kg) 222 (4) 221 [202‐242]
VRPR/F (l/70kg) 64.1 (24) 63.0 [41.8‐128.5]
Q/F (l/h/70kg) 0.0735 (19) 0.0788 [0.0486‐0.1470]
CLRRR/F (l/h/70kg) 0.416 (64) 0.3820 [0.0621‐0.9868]
CLRHR/F (l/h/70kg) 0.854 (24) 0.878 [0.466‐1.220]
MATCLR50R (days) 318 (8) 326 [286‐367]
HillCL 7.39 (43) 7.80 [3.53‐35.18]
Random model parameters
IIV VRCR/F 13.0 (36) 13.6 [3.6‐24.7]
IIV CLRTR/F 27.8 (13) 27.0 [18.2‐35.0]
IIV Q/F 34.1 (32) 36.4 [17.4‐53.4]
Ra (VRCR/F, CLRTR/F) 0.533 (69) 0.563 [‐0.059‐0.826]
R (VRCR/F, Q/F) 1 FIXED
R (CLRTR/F, Q/F) 0.533 (69) 0.563 [‐0.059‐0.826]
RUV (%) 33.6 (23) 32.6 [26.9‐37.4] acorrelation coefficient.OFV in final model: ‐97.384, bootstrap OFV (median [95% CI]): ‐111.812 [‐170.137‐‐62.348]
102
below the LOD for PYR. In addition, seven samples were of insufficient volume for
measurement of PYR after SDX/NSX assay. There were no SDX or NSX concentrations
below the LOQ but NSX concentrations could not be determined in eight samples due
to an unidentified interfering peak. For PYR, a two‐compartment model was superior
to a one‐compartment model with a lower OFV (‐87.081 vs. ‐30.030) and a less biased
WRES vs. time plot. The model parameters were kRaR, CLRHR/F, CLRRR/F, VRCR/F, VRPR/F, Q/F,
HillCL and MATCLR50R. IIV was estimable on CLRTR/F, VRCR/F and Q/F. As the correlation
between the variability of VRCR/F and Q/F was very close to 1, it was subsequently fixed
to unity to assist with successful determination of the covariance matrix. None of the
covariates tested improved the model significantly and therefore the final model
contained only the effects of PMA and WT anticipated from maturation and allometric
scaling, respectively.
The final parameter estimates and the results of the bootstrap procedure for PYR are
shown in Table 3‐3. All model parameters had a bias <11%. GOF plots for PYR are
shown inFigure 3‐1. NPCs of the data showed good predictive performance, as did VPC
plots of the observed drug concentrations and their 80% PI (the 10th and 90th
percentile boundaries) stratified by dosing group (Figure 3‐2 A and B). Post hoc
parameter estimates are shown in Table 3‐4. There was no difference between the
two groups for any of these parameters except for AUCR0‐∞ Rwhich was significantly
higher in the double‐dose group (4,915 vs. 2,844 μg19T∙19Td/l). Median VRSSR for the combined
study sample was 27.8 l, and tR½αR and tR½βR were 72.7 and 374 h, respectively.
Table 3‐4 Post hoc Bayesian predicted PK parameters for PYR for PNG infants given conventional and double doses of SDX/PYR (median [IQR]).
Parameter Conventional dose
(n=35)
Double dose
(n=35) P valuea
CLT/F (l/h) 0.183 [0.13‐0.21] 0.199 [0.164‐0.229] NSb
VC/F (l) 20.2 [17.8‐24.1] 22.1 [19‐25] NS
VP/F (l) 6.18 [5.32‐6.81] 6.49 [5.83‐7.03] NS
VRSSR/F (l) 25.781 [23.319‐30.957] 28.8 [24.9‐31.8] NS
Q/F (l/h) 0.0118 [0.0073‐0.0165] 0.0135 [0.009‐0.0196] NS
tR½αR (h) 73.7 [67.1‐87.7] 70.7 [62.3‐82.3] NS
tR½β R(h) 391 [300‐565] 361 [272‐511] NS
AUC0‐∞ PYR (μg19T∙19Td/l) 2,844 [2,486‐3,571] 4,915 [4,311‐5,681] <0.001 a Mann‐Whitney test, b NS= non‐significant (P>0.05)
103
Initial modelling of SDX revealed that a one‐compartment model was appropriate as
there was minimal bias in the WRES plot that was not improved when a two‐
compartment model was fitted. The model parameters were kRaR, CLRHR/F, CLRRR/F, V/F,
HillCL and MATCLR50R. IIV was able to be estimated on CLRTR/F and V/F. There was a
significant relationship between relative dose (in mg/kg) and relative bioavailability
Time (h)
0 100 200 300 400 500 600 700
Co
nd
itio
na
l we
igh
ted
re
sid
ua
ls (
py
rim
eth
am
ine
)
-4
-2
0
2
4
Predicted plasma pyrimethamine (g/l)
1 10 100 1000O
bs
erv
ed
pla
sm
a p
yri
me
tha
min
e (
g/l)
1
10
100
1000
A
B
Figure 3‐1 (A) Population (○) and individual (●) predicted versus observed plasma pyrimethamine concentrations (µg/l on log10 scale) for the final model. The line of identity is also shown. (B) Conditional weighted residuals vs. time for pyrimethamine final model.
Time (h)
0 200 400 600 800
Pla
sm
a p
yri
me
tha
min
e (
g/l)
0.1
1
10
100
1000
10000 A
Time (h)
0 200 400 600 800
Pla
sm
a p
yri
me
tha
min
e (
g/l)
0.1
1
10
100
1000
10000 B
Figure 3‐2 Visual predicted check plots for PYR showing simulated 10th (short dashed line), 50th (dotted line) and 90th (solid line) percentile concentrations and observed concentration (log scale) data (grey open circles) versus time (log scale) for conventional dose (A) and double‐dose (B) participants.
104
which conformed to a power function, specifically individual relative bioavailability = 1
x ([individual relative dose]/[average relative dose]effect parameter). The value of the
power effect parameter was ‐0.56, indicating that, when the dose is doubled, the
bioavailability falls by 32.2%.
The final parameter estimates and the results of the bootstrap procedure are shown in
Table 3‐5. With the exception of CLRRR, all parameter estimates had bias <13%. The
median bootstrap value for CLRRR was almost double the initial estimate (195%),
demonstrating the difficulty in delineating the difference between and estimating the
hepatic and renal clearance using this methodology. GOF plots for SDX are shown in
Table 3‐5 Final population PK parameters and bootstrap results for SDX and NSX. Parameters for NSX modelling obtained after fixing model parameters for SDX are highlighted in grey.
Final model
estimate (RSE%)
Bootstrap (n=1000)
median [95% CI]
Structural and covariate model parameters
kRa R(/h) 1.23 FIXED
V/F (l/70kg) 24.2 (4) 24.2 [22.5‐26.1]
CLRRR/F (l/h/70kg) 0.0046 (113) 0.0086 [0.0005‐0.0267]
CLRHR/F (l/h/70kg) 0.0458 (16) 0.0427 [0.0290‐0.0640]
MATCLR50R (days) 271 (8) 286 [248‐360]
HillCL 4.07 (52) 4.61 [1.56‐15.54]
Relative dose on relative bioavailability [power]
‐0.56 (14) ‐0.54 [‐0.71‐ ‐0.38]
%NSX (%) 60 FIXED
VRNSXR/F (l/70kg) 11.7 (10.7) 11.7 [9.4‐14.4]
CLRNSXR/F (l/h/70kg) 0.758 (5) 0.756 [0.690‐0.838]
Random model parameters
IIV V/F 23.0 (11) 22.2 [17.1‐26.5]
IIV CLRTR/F 23.8 (11) 23.4 [17.9‐28.3]
IIV VRNSXR/F 42.8 (19) 41.7 [21.0‐56.3]
IIV CLRNSXR/F 36.2 (26) 35.6 [25.5‐45.1]
R (V/F, CLRTR/F) 0.644 0.653 [0.439‐0.814]
R (VRNSXR/F, CLRNSXR/F) 0.218 0.226 [‐0.474‐0.729]
RUV SDX (%) 16.5 (11) 16.4 [12.9‐20.1]
RUV NSX (%) 37.1 (9) 37.0 [30.2‐43.1]
OFV in final model: ‐521.177, Bootstrap OFV (median [95% CI]): ‐529.222[‐647.701‐ ‐428.084]
105
Figure 3‐3. NPCs of the data showed good predictive performance, as did VPC plots of
the observed drug concentrations and their 80% PI stratified by dose group in Figure
3‐4.
Time (h)
0 100 200 300 400 500 600 700Co
nd
itio
na
l we
igh
ted
re
sid
ua
ls (
su
lfa
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-4
-2
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4
Predicted plasma sulfadoxine (mg/l)
10 100 1000
Ob
se
rve
d p
las
ma
su
lfa
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mg
/l)
10
100
1000A
B
Figure 3‐3 (A) Population (○) and individual (●) predicted versus observed plasma sulfadoxine concentrations (µg/l on log10 scale) for the final model. The line of identity is also shown. (B) Conditional weighted residuals vs. time (log scale) for sulfadoxine final model.
Time (h)
0 200 400 600 800
Pla
sm
a s
ulf
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(m
g/l)
1
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1000 A
Time (h)
0 200 400 600 800
Pla
sm
a s
ulf
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(m
g/l)
1
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Figure 3‐4 Visual predicted check plots for SDX showing simulated 10th (short dashed line), 50th (dotted line) and 90th (solid line) percentile concentrations and observed concentration (log scale) data (grey open circles) versus time (log scale) for conventional dose (A) and double‐dose (B) participants.
106
An additional compartment was added to the final SDX PK model to incorporate the
data for NSX. This resulted in three additional model parameters; VRNSXR/F, CLRNSXR/F and
percentage of total SDX elimination representing conversion of SDX to NSX (%NSX). As
these three parameters cannot be estimated simultaneously, %NSX was fixed to 60%
based on published data.225 The estimates of VRNSXR/F and CLRNSXR/F are directly related to
%NSX and therefore the value of these parameters should be interpreted with caution.
However AUC and tR½R for NSX remain unchanged for different values of %NSX. VRNSXR/F
and CLRNSXR/F were not influenced by any of the available covariates. Final parameter
estimates and results of the bootstrap procedure are shown in Table 3‐5. Bias was <5%
for all NSX parameters and NPCs and VPCs were performed on the NSX dataset and
indicated good predictive performance of the model (data not shown).
There were some significant differences between conventional and double‐dose
groups in the post hoc parameter estimates for both SDX and NSX (Table 3‐6). These
included expected differences in the AUCR0‐∞R for both SDX and NSX, but also differences
in the tR½R and clearance for both drugs which were not revealed by the model covariate
building stage. A higher clearance and lower tR½R in the double‐dose group can be
attributed to organ maturation as these infants were older than those in the
conventional dose group. The median tR½ Rof NSX for the combined study sample was
shorter than that of SDX (8.9 vs. 218 h). The percentage of the AUCR0‐∞R of NSX when
compared to that for SDX was the same for both dose groups (approximately 5%).
Table 3‐6 Post hoc Bayesian predicted PK parameters for SDX and NSX in PNG infants given conventional and double dosing of SDX/PYR (median [IQR]).
Parameter Conventional dosing
(n=35)
Double dosing
(n=35) P‐valuea
CLRT,SDXR/F (l/h) 0.0068 [0.0057‐0.0087] 0.0072 [0.0068‐0.0105] 0.032
VRSDXR/F (l) 2.20 [1.95‐2.53] 2.23 [1.97‐2.64] NSb
tR½ SDXR (h) 232 [203‐252] 207 [179‐232] 0.006
AUCR0‐∞ SDXR (mg19T∙19Td/l) 1,460 [1,167‐1,707] 2,434 [1,881‐2,987] <0.001
CLRNSXR/F (l/h) 0.081 [0.060‐0.094] 0.101 [0.081‐0.116] 0.012
VRNSXR/F (l) 1.14 [1.00‐1.28] 1.17 [0.959‐1.30] NS
tR½ NSXR (h) 10.3 [7.86‐12.2] 8.69 [6.84‐10.6] 0.027
AUCR0‐∞ NSXR (mg19T∙19Td/l) 1,796 [1,397‐2,154] 2,890 [2,482‐3,609] <0.001
AUCR0‐∞ NSXR/ AUCR0‐∞ SDXR (%) 5.0 [4.3‐6.5] 5.0 [4.3‐6.0] NS a Mann‐Whitney test, b NS= non‐significant (P>0.05)
107
Sigmoid ERmaxR curves of hepatic maturity for SDX and PYR by PMA are shown in Figure
3‐5. They are closely related to MATCLR50R values of 318 days and 271 days for PYR and
SDX, respectively. Of the 70 infants, 48 (69%) and 38 (54%) had an estimated hepatic
clearance >90% of adult values for PYR and SDX, respectively.
Figure 3‐5 Maturation as a fraction of adult clearance for PYR (solid line) and SDX (dashed line) predicted from the PK model plotted against PMA. A box plot of the PMA in the recruited subjects is included to show its distribution in relation to maturation of clearance.
PMA (d)
100 200 300 400 500 600
Fra
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ce
0.00
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SDX PYR
108
3.2.5 48B48BDiscussion
The present study is the first to investigate the PK of SP in infants living in a malaria‐
endemic setting in which IPTi is appropriate. It is also the first to investigate the
possibility that a higher dose than conventionally recommended should be given to
achieve therapeutic plasma concentrations in this age group, as has been
recommended for children aged 2‐5 years.226 SP was well tolerated by all infants and
there was no evidence of hepatorenal or bone marrow toxicity even at the higher
dose. The AUCR0‐∞R of both SDX and PYR was significantly higher in the double‐dose
group. However, there was a 32% reduction in the relative bioavailability of SDX when
the dose was doubled, possibly due to saturation of absorption. The percentage of NSX
to SDX exposure (AUC) was the same in both groups suggesting that a double dose
does not affect the metabolic clearance of SDX. The PK properties of PYR were not
dose‐dependent in the present study.
The PK parameters for PYR observed in our children are different to those observed in
other paediatric studies.226, 227, 230, 350, 353 We found a longer tR½βR (15.6 vs. 2.67‐4.46 d)
and a higher conventional dose AUC (2,844 vs. 1,052‐2,607 μg19T∙19Td/l). This may reflect the
fact that most of our children were well. In addition, we employed a relatively long
duration of sampling that facilitated identification of bi‐exponential elimination, a
profile reported previously in studies of adults314, 354, 355 but not children. While one
paediatric study sampled out to 42 d, the drug could not be quantified in 40% of the
samples.226 Although the mean conventional dose PYR AUC in the present study was in
the range of previously reported values in adults (1,602‐3,166 μg19T∙19Td/l),226, 355‐357 these
latter data may have been underestimates because of truncated sampling and/or use
of a relatively insensitive assay. In a study of non‐pregnant PNG women using a similar
sampling profile, assay and PK modelling techniques to those of the present study,314
the mean conventional dose PYR AUC (4,419 μg19T∙19Td/l) was similar to that in the present
double‐dose group. Together with the available tolerability and safety data from the
present study, these considerations suggest that double‐dose PYR is appropriate as
part of SP IPTi.
We found that SDX also had a longer mean elimination tR½R (9.1 vs. 4.1‐8.6 d) and higher
conventional dose mean AUC (1,460 vs. 460‐932 mg19T∙19Td/l) when compared to other
109
studies in children.226, 227, 230, 342, 352 However, the mean AUC was within the range
found in adults (508‐2,757 mg19T∙19Td/l),226, 355‐357 including non‐pregnant women (1,386
mg19T∙19Td/l) from the same location as the present study.314 Although the difference in AUC
compared to other paediatric populations may be explained, as with PYR, by our ability
to detect drug concentrations for longer post‐dose than in previous studies as well as
to the relative health of our subjects, only a few studies have included infants aged <1
year and these formed a minority of the patients recruited. As our sample includes
only children <13 months of age, a limited maturation of elimination processes is likely
to play a role in the longer tR½R and higher AUC observed for both drugs even in the
conventional dose group. Indeed, we found evidence of a slower maturation of these
processes for SDX than PYR.
In the present study we used plasma CysC rather than creatinine to estimate GFR. The
conventional Schwartz creatinine‐based formula relies upon estimates of body
composition358 whereas CysC‐based formulae do not,359 making the estimates more
robust. We used the formula derived by Filler et al.349 as it was derived from a large
paediatric sample and the same PETIA CysC assay used in the present study. CysC
concentrations generated by other assays such as particle‐enhanced
immunonephelometry may differ from those from PETIA.359 The Filler et al. formula is
comparable to others based on CysC derived in children.360‐363
Since hepatic maturation would be still occurring within the age range of our subjects,
it was appropriate to include this phenomenon in our model.228, 364, 365 We used a
sigmoid ERmaxR approach as this has been used previously with a number of other
drugs.228, 366‐369 Our estimates of MATCLR50R, namely 315 and 271 days for PYR and SDX
respectively, fell in the range reported in these latter studies (270‐380 d). The estimate
of the Hill coefficient for SDX was also consistent (4.07 vs. 2.78‐4.6), but the Hill
coefficient for PYR was higher than previously reported (7.39). Although our study age
range captured the process of maturation, most of our infants had clearances >90% of
adult values and very few were <50% (see Figure 3‐5). This limits our ability to
characterize coefficients of maturation which are likely to be inappropriate outside this
age range. For example, adult estimates of tR½ Rfor SDX (333 h) and PYR (tR½αR 113 h, tR½βR
647 h) based on the modelling presented here are higher than previously reported.226,
110
314, 354‐357 Future studies of this type should include a larger range of ages so that the
maturation process from birth to adult activity levels can be determined more
accurately.
Other studies have provided data relevant to the question of whether a higher SP dose
should be given to infants. A PK evaluation of SDX in children aged 6 months to 5 years
with malaria found that those aged <24 months had a lower AUCR0‐336hR than their older
counterparts (12,500 vs. 16,900 mg19T∙19Th/l).352 However all children <24 months of age
received half the dose of older children regardless of WT and no average dose by WT
was reported, thus complicating interpretation of the data. In a similar study,226 an
age‐stratified non‐compartmental analysis of AUCR0‐∞R showed that 1‐2 year‐olds had
sufficient drug exposure while children aged 2‐5 years required a double dose. The
study only had 11 children within the 1‐2 year old age range and, because only whole
tablets were given, the mean dose in this group was almost twice that of ≥12 year olds
(50/2.5 vs. 27.3/1.36 mg/kg). In a population‐based PK analysis of SP in children with
congenital toxoplasmosis aged 1 week to 14 years,227 lighter children had a shorter tR1/2R
and therefore a lower drug exposure. This conclusion was based on use of allometry
since age‐based maturation contributed little to the model, perhaps because of the
small numbers in the younger age groups. Interpreted within their limitations, these
various studies also provide evidence that higher mg/kg SP doses are required in
younger children, including those <1 year of age.
Relatively recent data from the study area indicate that AQ‐SP treatment (until
recently the recommended first‐line antimalarial therapy for young PNG children) is
associated with close to a 90% 28‐day adequate clinical and parasitologic response for
both falciparum (PCR‐corrected) and vivax malaria.370 This is a suboptimal response
but still suggests that either conventional or double‐dose SP treatment in the present
study is likely to have contributed to the relatively small number of infections detected
during follow‐up. Although the present study was not designed to assess relative
efficacy, especially since interpretation of emergent vivax infections remains
problematic371 and given that only one dose was administered rather than the several
scheduled during IPTi, fewer children were treated for symptomatic malaria during
follow‐up or were slide positive on day 28 in the double‐dose group. Indeed, there is
111
evidence from epidemiologic studies utilizing fixed‐dose regimens62, 341 that
appropriate mg/kg doses of SP should be used in IPTi programs to ensure adequate
levels of prevention, especially for symptomatic compared to asymptomatic falciparum
malaria.372
In the light of this dose‐dependency, the fact that no study has shown >60% protective
efficacy during the first year of life,62, 339 evidence that higher blood PYR
concentrations facilitate parasite clearance in paediatric falciparum malaria,342 and the
fact that double‐dose SP in our subjects was safe, well tolerated and associated with
higher exposure to both drug components (especially SDX), the present data argue for
the potential use of double‐dose SP in infancy. As in recent adult studies of PYR
disposition,314 we found that the mean elimination tR½R, of PYR and SDX were longer
than previously reported, a factor that may contribute to the duration of effective
prophylaxis. Although allometric considerations (shorter half‐lives in smaller subjects)
may justify higher SP dosing in infants, we recommend that consideration must be
given to the maturation of hepatorenal elimination processes and the possibility that
increased doses may be inappropriate in very young infants.
112
3.2.6 49B49BAcknowledgements
We are most grateful to Sr Valsi Kurian and the staff of Alexishafen Health Centre for
their kind co‐operation during the study. We also thank Christine Kalopo and Bernard
(“Ben”) Maamu for clinical and/or logistic assistance. The authors note with deep
regret that Servina Gomorrai, who assisted with patient recruitment and data
collection, passed away during the study. The study was funded by a grant from the
IPTi Consortium and utilised facilities developed with support from the National Health
and Medical Research Council (NHMRC) of Australia (grant 458555). TMED is the
recipient of an NHMRC Practitioner Fellowship.
113
114
115
164B164BTREATMENTOF
UNCOMPLICATEDMALARIA
INCHILDREN
116
117
4 3B3BPopulationPharmacokineticsofArtemether,
Lumefantrine,andTheirRespectiveMetabolites
inPapuaNewGuineanChildrenwith
UncomplicatedMalaria
4.1 15B15BBackground
The aims of the study presented in this chapter were to add to the current literature
on the PK of AL in children, particularly those of DBL, and to then compare the PK in
children to those in adults in order to inform an appropriate paediatric dose regimen.
When this study was conceived, an efficacy trial was being undertaken in PNG to
assess a number of newer combinations against conventional CQ/SP21. One of these
was AL. The literature at that time contained few data of the PK of AL in children and
even fewer reports of DBL concentrations and its role in treatment outcome.
Therefore a study of a small group of children in PNG was conducted to aid
interpretation of the results from the efficacy trial and assist with delineating the role
of DBL in treatment outcome.
This study resulted in the publication2 presented in this chapter. Entitled, “Population
PK of artemether, lumefantrine, and their respective metabolites in Papua New
Guinean children with uncomplicated malaria” it was published in the journal
Antimicrobial Agents and Chemotherapy (2011. 55(11):p. 5306‐13). The contribution of
each of the authors is outlined in section i, which also contains details of ethical
approvals and supporting funding. While the complete publication is provided in
section xi.a below, it has been reformatted to conform to thesis requirements set by
the University of Western Australia. The references have been combined with those
for the thesis as a whole and can be found in section x below.
118
119
4.2 16B16BPublication
Sam Salman,A Madhu Page‐Sharp,B Susan Griffin,C Kaye Kose,C Peter M. Siba,C Kenneth
F. Ilett,A Ivo Mueller,C, Timothy M. E. DavisAR.
ASchool of Medicine and Pharmacology, University of Western Australia, Fremantle
Hospital, Fremantle, Western Australia, Australia;
BSchool of Pharmacy, Curtin University of Technology, Bentley, Australia;
CPapua New Guinea Institute of Medical Research, Madang, Papua New Guinea.
4.2.1 50B50BAbstract
There are sparse published data relating to the PK properties of ARM, LUM and their
active metabolites in children, especially DBL. We studied 13 Papua New Guinean
children aged 5‐10 years with uncomplicated malaria who received the six
recommended doses of ARM (1.7 mg/kg) plus LUM (12 mg/kg) given with fat over 3
days. Intensive blood sampling was carried out over 42 days. Plasma ARM, DHA, LUM
and DBL were assayed using liquid chromatography‐mass spectrometry. Multi‐
compartmental PK models for drug plus metabolite were developed using a population
approach that included plasma ARM and DHA BLQ concentrations. Although ARM
bioavailability was variable and its clearance increased by 67.8% with each dose, the
median areas under the plasma concentration‐time curve (AUCR0–∞R) for ARM and DHA
(3,063 and 2,839 µg∙h/l, respectively) were similar to those reported previously in
adults with malaria. For LUM, the median AUCR0–∞R (459,980 µg19T∙19Th/l) was also similar to
that in adults with malaria. These data support the 35% higher mg/kg dose
recommended for children 15‐35 kg vs. a 50 kg adult but question the
recommendation for a lower dose in children weighing 12.5‐15 kg. The median
DBL:LUM ratio in our children was 1.13%, within the range reported for adults and
higher at later time‐points because of the longer DBL terminal elimination tR½R. A
combined DBL plus LUM AUCR0–∞R weighted on in vitro antimalarial activity was
inversely associated with recurrent parasitaemia, suggesting that both parent drug and
metabolite contribute to AL treatment outcome.
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4.2.2 51B51BIntroduction
AL is a fixed‐dose combination therapy used widely for the treatment of malaria.373
ARM is a lipophilic artemisinin derivative that is converted in vivo to DHA, an active
metabolite. Both ARM and DHA have short half‐lives133, 135, 137‐139, 143, 144 but a rapid
effect on parasitaemia. LUM is a highly lipophilic drug with a longer tR½R
135, 137, 138, 143, 164,
182, 374 which is combined with ARM primarily to prevent late recrudescence. Although
the PK properties of ARM, DHA and LUM have been well documented in adults,133‐135,
137, 138, 143, 164, 182, 184, 374‐376 there are scant and inconsistent data relating to the
disposition of DBL, a potent LUM metabolite160, 163, 377, 378 that may influence AL
treatment outcome.163 Reported plasma DBL:LUM concentration ratios after AL dosing
in adults differ >10‐fold,143, 184 while the PK properties of DBL in children are unknown.
In addition, although several studies have attempted to characterize LUM disposition
in children with malaria,142, 144, 183 methodological issues complicate their comparison
with adult data. One study involving a limited sampling schedule suggested that AL‐
treated children with malaria receive an inadequate dose of LUM relative to healthy
adults,144 while the other studies either used pooled plasma concentrations183 or used
a truncated sampling schedule inadequate to characterize LUM PK.142
In view of this situation, we have characterized the population PK of ARM, LUM and
their metabolites in paediatric malaria using a rich sampling schedule to assess
potential differences in disposition between children and adults, and to add to the
limited data on DBL disposition and its role in AL treatment outcome.
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4.2.3 52B52BPatientsandmethods
4.2.3.1 108B108BPatients
We recruited children aged 5‐10 years from Alexishafen Health Centre, Madang
Province on the north coast of PNG. The clinic serves an area where P. falciparum and
P. vivax are hyperendemic, and P. ovale and P. malariae are also transmitted. Children
with an axillary temperature >37.5°C or a history of fever in the previous 24 h were
screened with a Giemsa‐stained thick blood film read by an on‐site trained
microscopist. Those with a mono‐infection of P. falciparum (>1,000 asexual
parasites/microliter), or P. vivax, ovale or malariae (>250/microliter) were eligible
provided that the child’s parents gave informed consent, there were no features of
severe malaria,379 they had not taken any antimalarial drug in the previous 14 days,
there was no evidence of another cause of fever, and there were no features of
malnutrition or other chronic co‐morbidity. The study was approved by the Medical
Research Advisory Committee of the Department of Health, PNG.
4.2.3.2 109B109BClinicalmethods
After enrolment, a standardized history was taken and a clinical examination
performed. A 3 ml blood sample was taken for blood film microscopy, a baseline Hb
and blood glucose, and subsequent drug assay of separated plasma. Urinalysis and
audiometric assessment were performed. Each child was given AL (Coartem, Novartis
Pharma Ltd, Switzerland) at a dose of 1.7 and 10 mg/kg, respectively to the nearest
tablet. This dose was repeated at 8, 24, 36, 48 and 60 h with the exact time of dosing
recorded. All doses were given under direct observation with at least 50 ml of cow’s
milk (equivalent to 2 g of fat). Further venous blood samples were taken from an
indwelling intravenous catheter at 4, 8, 12, 24, 36, 40, 48, 60, 64, 68 and 72 h, and
then by venesection on days 4, 5, 7, 14 and 28. All samples were centrifuged promptly
and RBCs and separated plasma stored frozen at ‐80°C until assay. Detailed clinical
assessment, including a symptom questionnaire, blood film, Hb and blood glucose, was
repeated on days 1, 2, 3 and 7, with additional clinical assessment and blood films on
days 14, 28 and 42.
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4.2.3.3 110B110BLaboratorymethods
All blood smears taken at baseline and during follow‐up were examined independently
by two skilled microscopists in a central laboratory. Each microscopist viewed 100
fields at 1,000x magnification before a slide was considered negative. Any slide
discrepant for positivity/negativity or speciation was referred to a third microscopist
for adjudication.
For drug assays, HPLC‐grade acetonitrile (Merck, Kilsyth, Australia), tert‐butyl chloride,
ethyl acetate, glacial acetic acid and formic acid (Merck, Darmstadt, Germany), and
ammonium formate (Sigma‐Aldrich, Gillingham, UK) were used. Other solvents and
chemicals were of analytical grade. Stock solutions (1 µg/l in methanol) of ARM (AAPIN
Chemicals, Abingdon, UK), DHA (Sigma, St Louis, MO) and ART (used as an internal
standard; Sigma) were stored protected from light at ‐80°C and used to prepare
working dilutions (0.1, 1, and 10 µg/ml). Calibration curves (2‐200 µg/l) were
constructed for DHA and ARM by spiking blank plasma. Quality control (QC) samples
were prepared in blank plasma at 10, 20, 50 and 200 µg/l and also stored at ‐80 °C
prior to use.
ARM and DHA were extracted as previously described380 with the following
modifications. Briefly, solid phase extraction (SPE) Bond Elut ® PH columns (Varian Inc,
Palo Alto, CA) were pre‐conditioned with 1 ml methanol followed by 1 ml 1 moles/l
(M) acetic acid. Plasma (0.5 ml) was spiked with internal standard (ART 100 µg/l) and
loaded onto the SPE column and drawn through with a medium vacuum. The column
was then washed twice with 1 M acetic acid (1 ml), followed by 20% v/v methanol in
1M acetic acid (1 ml). The column was dried under low vacuum for 30 min and the
retained drugs eluted using 2 ml t‐butyl chloride: ethyl acetate (80:20% v/v). The
eluate was then evaporated under vacuum at 35°C and reconstituted in 50 μl mobile
phase and kept overnight to equilibrate the α and β anomers of DHA.380 Only the α‐
anomer was used for quantification. The injection volume was 10 μl.
The LC‐MS system used was a single quad mass spectrometer (Shimadzu, Kyoto, Japan)
with electrospray ionization (ESI) and atmospheric pressure ionization (APCI) systems.
Assays were performed with 20 mM ammonium formate (pH 5):acetonitrile in 0.1%
123
formic acid (40:60) at a flow rate of 0.2 ml/min, and chromatographic separation
undertaken at ambient temperature on a Synergy fusion‐RP CR18R (150 mm x 2.0 mm
i.d.) column coupled with a 4 mm x 3 mm i.d., 5 µm particle CR18R guard column
(Phenomenex, Lane Cove, Australia). Retention times were 4.5, 7.5 and 12.7 min for
DHA, ART and ARM, respectively. Optimized mass spectra were acquired with an
interface voltage of 4.5 kV, a detector voltage of 1 kV, a heat block temperature of
400°C and a desolvation gas temperature of 250°C. Nitrogen was used as a nebulizer
gas at a flow rate of 1.5 l/min and dry gas flow of 10 l/min. Quantitation was
performed by selected ion monitoring using the dual ionization source mode. The
predominant fragmented ions m/z 221 for ARM and m/z 221 for DHA were used. For
ART, m/z=283 was monitored.
Standard curves were linear (r2 ≥0.999). Chromatographic data (peak area ratio of
DHA:ART and ARM:ART) were processed using LAB Solution software (Version 5,
Shimadzu, Japan). No matrix effect (ion suppression/enhancement) was observed
under methodologies described elsewhere,381 and performance of both assays,
assessed as intra‐ and inter‐day RSD across relevant concentration ranges, was similar
to that published previously.21, 380 Inter‐day accuracies of QC assays were <15% of
nominal values on all occasions. The limits of quantification and detection for DHA and
ARM were 2 and 1 μg/l, and 5 and 2 μg/l, respectively.
LUM and DBL were quantified in plasma using a UPLC‐LC‐MS/MS assay as previously
described.163 The linear range for LUM was 20‐20,000 ng/ml, and inter‐day variability
was 4.94, 4.93, 7.16 and 11.23% and intra‐day variability 2.83, 4.41, 4.11, 9.55% at
20,000, 2,000, 200 and 20 ng/ml, respectively. For DBL, the linear range was 0.5 ‐100
ng/ml, and inter‐day variability was 3.36, 3.47, 9.98 and 6.74% and intra‐day variability
2.47%, 3.46%, 8.16% and 3.48% at 50, 10, 1 and 0.5 ng/ml, respectively. As a LC‐
MS/MS method was used for DBL, matrix effects were assessed where IIV was 3.37%,
4.47% and 9.43% at 50, 10 and 1 ng/ml, respectively.
4.2.3.4 111B111BPharmacokineticmodelling
LogReR (natural log) plasma concentration‐time datasets for LUM with DBL and ARM with
DHA were analysed by nonlinear mixed effect modelling using NONMEM (v 6.2.0, ICON
124
Development Solutions, Ellicott City, MD) with an Intel Visual FORTRAN 10.0 compiler.
The first order conditional estimation with interaction (FOCE‐I) estimation method was
used for the LUM/DBL model and the Laplacian with interaction method for ARM/DHA.
OFV and WRES plots were used to choose suitable models during model‐building. As
FOCE‐I estimation was used, CWRES were also considered in the initial stages of model
building.299 However, as they were similar to WRES, the latter was considered suitable
for further model‐building. Concentrations were modelled as µg/ml with a conversion
factor for all metabolite parameters included into the model to account for the
difference in molecular weight between parent drug and metabolite. Allometric scaling
was used a priori, with volume terms multiplied by (WT/70)1.0 and clearance terms by
(WT/70)0.75.309 RUV was estimated as additive error for the logReR‐transformed data.
Models were parameterized using kRaR, VRCR/F, CL/F, VRPR/F and Q/F.
For the LUM/DBL model, plasma LUM concentrations were initially modelled using
inbuilt 2‐ and 3‐ compartment model structures with first‐order absorption and a fixed
lag time of 2 h134 (ADVAN 4 and 12). Once a suitable (3‐compartment) LUM model had
been determined, the DBL dataset was added and modelled simultaneously. User‐
defined linear mammillary models (ADVAN 5) were constructed testing 1‐, 2‐ and 3‐
compartments with and without FP LUM metabolism. As no data exist regarding the
degree of in vivo DBL conversion from LUM this was set to 100% to allow identifiability.
Therefore, all clearance and volume terms for DBL are relative to LUM bioavailability
(FRLUMR) as well as the degree of metabolic conversion from LUM (FRmet‐DBLR). The term
F*RDBLR (representing FRLUMR x FRmet‐DBLR) will be used for simplicity.
As 45% and 12% of plasma ARM and DHA concentrations, respectively, were BLQ we
used a published method known to produce reliable PK parameters in this situation.382,
383 The method (known as M3297) models continuous and categorical data
simultaneously. Concentrations above the LOQ are included as conventional
continuous data while those BLQ are treated as categorical and the (probability) that
they are BLQ maximized with respect to model parameters. This allows BLQ
observations to contribute to the determination of the OFV and in finalizing model
structure.
125
Initially plasma ARM concentrations were assessed using 1‐ and 2‐compartment
models with first order absorption (ADVAN2 and 4) to obtain a suitable structure. The
kRaR for ARM was fixed to 1 /h 139 as the data did not support its estimation. Once a
suitable (2‐compartment) ARM model had been determined, the DHA dataset was
added and modelled simultaneously using a user‐defined linear mammillary model
(ADVAN 5). For DHA, 1‐ and 2‐ compartments were assessed and the conversion of
ARM to DHA was considered complete for identifiability purposes. Therefore, all
clearance and volume terms for DHA are relative to ART bioavailability (FRARTR) as well as
the degree of metabolic conversion from ART (FRmet‐DHAR). The term F*RDHAR (representing
FRARTR x FRmet‐DHAR) will be used for simplicity.
Once model structure was established, IIV, IOV and their correlations were estimated.
Relationships between model parameters and the covariates age, sex, baseline
parasitaemia and baseline haemoglobin were identified using correlation plots and
subsequently evaluated within NONMEM. Inclusion of the covariate relationship
required a decrease in OFV ≥6.63 (2 distribution with 1 d.f., P<0.01) accompanied by a
decrease in the IIV of that parameter.
4.2.3.5 112B112BModelevaluation
A bootstrap using Perl speaks NONMEM (PSN) with 1,000 samples was performed and
the parameters derived from this analysis summarized as median and 2.5th and 97.5th
centiles (95% empirical CI) to facilitate evaluation of final model parameter estimates.
Runs were included in the bootstrap analysis regardless of their minimization status. In
addition, VPCs were performed with 1,000 datasets simulated from the final models.
The observed 10th, 50th and 90th percentiles were plotted with their respective
simulated 95% CI to assess the predictive performance of the model. For the
ARM/DHA model, the observed fraction of BLQ observations was compared with the
median and 95% PI of BLQ observations from these simulated datasets.382
The applicability of the final population models to younger patients from the present
sample was assessed using a NPC. Day 7 plasma LUM concentrations from a previous
study21 from children aged 0.5‐5 years were compared with simulated data from the
126
final models. The actual and simulated number of data points above and below the
20%, 40%, 60%, 80%, 90% and 95% simulated PI were compared.
4.2.3.6 113B113BStatisticalanalysis
Changes in Hb, glucose and audiometric data over time were assessed using the
Wilcoxon signed‐rank test. The AUCR0‐∞Rs of DBL and LUM were compared between
subjects with or without recurrent parasitaemia using the Mann–Whitney U test. A
two‐tailed level of significance of 0.05 was considered significant for all comparisons.
127
4.2.4 53B53BResults
4.2.4.1 114B114BClinicalcharacteristicsandcourse
The baseline characteristics of the 13 recruited children are summarized in Table 4‐1.
Eleven had a mono‐infection (9 P. falciparum, 2 P. malariae) on confirmatory expert
microscopy, while 2 had a mixed P. falciparum/vivax infection. AL treatment was well
tolerated and reported symptoms were mild/moderate, short‐lived (<3 days) and
consistent with clinical features of uncomplicated malaria. Initial fever and parasite
clearance were <48 h in all cases.
By 28 days of follow‐up, three children had developed slide‐positive P. vivax (two had
P. vivax at enrolment) and two children had developed P. falciparum (one had P.
falciparum at enrolment). By 42 days of follow‐up, five children had been diagnosed
with P. vivax (two had P. vivax at enrolment) and three with P. falciparum (two had P.
falciparum at enrolment). These data are consistent with the PCR uncorrected results
of a previous larger comparative treatment trial in younger children performed at the
same location.21 The recurrent P. vivax parasitaemia could have resulted from i)
recrudescent infection in those infected with this parasite before treatment, ii)
acquisition of a new P. vivax infection after treatment or, since no primaquine therapy
was administered, iii) appearance of P. vivax from hypnozoites present in the liver at
study entry. For P. falciparum parasitaemia detected during follow‐up, this could have
Table 4‐1 Baseline characteristics of study participants. Data are number (%), mean ± SD or median and [inter‐quartile range].
Children n=13
Age (years) 7.7 ± 1.4
Sex (% male) 8 (62%)
Weight (kg) 19.0 ± 3.5
Height (cm) 112 ± 9
Axillary temperature (°C) 36.8 ± 1.0
P. falciparum parasitaemia 9 (69%)
P. falciparum/vivax parasitaemia 2 (15%)
P. malariae parasitaemia 2 (15%)
Respiratory rate (/min) 28 ± 9
Supine pulse rate (/min) 102 ± 16
Mean upper arm circumference (cm) 16 ± 1
Haemoglobin (g/l) 8.9 ± 1.6
128
represented recrudescence or re‐infection.
The mean Hb concentration was significantly higher on day 28 compared to enrolment
(10.7 vs. 8.9 g/l, P<0.01). There was no significant change in blood glucose over the
first three days of enrolment or audiometric findings over 28 days (data not shown).
4.2.4.2 115B115BPharmacokineticmodelling
LUM and DBL plasma concentration‐time curves are shown in Figure 4‐1. A 3‐
compartment model proved superior to a 2‐compartment model for LUM with a lower
OFV and reduced bias in the WRES plot. The addition of two compartments and the
inclusion of FP metabolism provided the best model once the DBL dataset had been
added. Therefore, the final model comprised 3 compartments for LUM and 2
compartments for DBL. The structural model parameters were kRaR, VRCR /FRLUMR, VRP1R /FRLUMR,
VRP2R /FRLUMR, CL/FRLUMR, QR1R/FRLUMR, QR2R/FRLUMR, FP (percentage contribution of FP metabolism to
DBL metabolic conversion), VRCR/F*RDBLR, VRPR /F*RDBLR, CL/F*RDBLR, Q/F*RDBLR. IIV was able to be
estimated for kRaR, CL/FRLUMR, CRLR/F*RDBLR, VRCR/F*RDBLR and FRLUMR as well as inter‐occasion
variability for FRLUMR (the population value of FRLUMR remained fixed to 1). Variability in
FRLUMR was smaller between individuals than it was between doses in the same
individual (20 vs. 67%). Once IIV and IOV terms were added, inspection of the
Time (h)
0 200 400 600 800
Pla
sm
a lu
me
fan
trin
e o
r d
es
bu
tyl-
lum
efa
ntr
ine
(
g/l)
1
10
100
1000
10000
Figure 4‐1 Time‐concentration plots showing LUM (○) and DBL () in μg/l on log10 scale. Curves of the median concentration for LUM (solid black line) and DBL (dashed black line) are also shown.
129
WRES plot revealed a bias due to the absorption profile of the final dose. Estimation of
a separate kRaR for the 6th (final dose) (kRaD6R) improved the bias and reduced the OFV (‐
7.519 P<0.01). None of the covariates tested improved the model. Residual
unexplained variability (20.8% and 20.9% for LUM and DBL, respectively) was low.
The final model parameter estimates and the bootstrap results are summarized in
Table 4‐2. Bias was <10% for structural and random model parameters. Figure 4‐2and
Figure 4‐3 show GOF plots and VPCs, respectively. The half‐lives and AUC of LUM and
Table 4‐2 Final population pharmacokinetic estimates and bootstrap results for lumefantrine and desbutyl‐lumefantrine.
Parameter Final model
estimate (RSE%)a
Bootstrap
median [95% CI]
Structural model parameters
kRaR (/h) 0.461 (20) 0.442 [0.285‐0.644]
CL/FRLUMR (l/h/70kg) 7.29 (9) 7.21 [5.55‐9.04]
VRCR/FRLUMR (l/70kg) 227 (12) 225 [147‐284]
QR1R/FRLUMR (l/h/70kg) 1.52 (16) 1.57 [0.96‐2.32]
VRP1R/FRLUMR (l/70kg) 115 (19) 109 [57‐214]
QR2R/FRLUMR (l/h/70kg) 0.743 (13) 0.805 [0.208‐1.27]
VRP2R/FRLUMR (l/70kg) 164 (8) 168 [97‐240]
kaRD6R (/h) 1.20 (52) 1.14 [0.50‐3.68]
FP (%) 6.29 (15) 6.45 [4.36‐9.84]
CL/F*RDBLR (l/h/70kg) 701 (10) 694 [561‐851]
VRCR/F*RDBLR (l/70kg) 51,100 (10) 51,200 [42,200‐61,430]
Q/F*RDBLR (l/h/70kg) 439 (19) 424 [305‐632]
VRPR/F*RDBLR (l/70kg) 68,400 (14) 68,000 [51,800‐88,600]
Random model parameters
IIV in FRLUMR (%) 19.8 (42) 18.9 [2.5‐29.3]
IIV in ka (%) 55.4 (44) 55.8 [17.1‐92.2]
IIV in CL/FRLUMR (%) 17.7 (20) 16.9 [6.8‐23.7]
IIV in CL/F*RDBLR (%) 26.2 (26) 26.0 [10.4‐37.6]
IIV in VRCR/F*RDBLR (%) 34.1 (22) 33.3 [17.4‐47.8]
IOV in FRLUMR (%) 67.0 (9) 66.4 [53.4‐77.7]
RUV for LUM (%) 20.8 (7) 20.3 [17.4‐22.5]
RUV for DBL (%) 20.9 (7) 20.6 [17.5‐23.0]
a RSE% are the NONMEM produced values from the covariance step. OFV in the final model:‐586.510, bootstrap OFV (median [95% CI]):‐601.901 [‐668.687‐ ‐559.564]
130
Predicted plasma lumefantrine (g/l)
10 100 1000 10000
Ob
se
rve
d p
las
ma
lum
efa
ntr
ine
(
g/l)
10
100
1000
10000
Predicted plasma desbutyl-lumefantrine (g/l)
0.1 1 10 100Ob
se
rve
d p
las
ma
de
sb
uty
l-lu
me
fan
trin
e (
g/l)
0.1
1
10
100A B
Figure 4‐2 Population (○) and individual predicted (●) versus observed data for LUM (A) and DBL (B) concentrations (µg/l) for the final model. The lines of identity are also shown.
DBL are shown in Table 4‐4. The first distribution, second distribution and terminal
elimination half‐lives for LUM had median values of 10.4, 46.6 and 123 h, while DBL
had a median distribution tR½R of 19.7 h and a median terminal elimination tR½R of 141 h.
Overall the metabolite to parent drug ratio was 1.13 % (obtained from AUCR0‐∞R) but
there was a higher ratio at later time‐points. Day 7 LUM concentrations obtained from
younger children were consistent with prediction based on the final model with an
expected number of observations above and below the 20, 40, 60, 80, 90 and 95%
simulated PIs. When the same data for DBL were compared, there was an excess of
points above the 20, 40, 60 80 and 90% PIs and a lack of points below the 20 and 40%
PI, especially at a younger age, demonstrating that the day 7 DBL concentrations in the
younger children were higher than expected from the model.
Time (h)
0 100 200 300 400 500 600 700
Lu
me
fan
trin
e (
g/li
ter)
10
100
1000
10000
A
Time (h)
0 100 200 300 400 500 600 700
De
sb
uty
l-lu
me
fan
trin
e (
g/li
ter)
1
10
100 B
Figure 4‐3 Visual predictive check showing observed 50th (●), 10th () and 90th (○) percen les with the simulated 95% CI for the 50th (solid black line), 10th (grey dotted lines) and 90th (dashed grey lines) percentiles for LUM (A) and DBL (B) concentrations (μg/l on log10 scale) from the final model.
131
Initial modelling of ARM/DHA datasets proved difficult given the large proportion of
BLQ data (45% and 12% for ARM and DHA respectively). Once these data were
incorporated into the model using the method ‘M3’ in Ahn et al. 297, more acceptable
models were obtained. The dispositions of ARM and DHA were best described by a 2‐
compartment model for ARM and a 1‐compartment model for DHA. The structural
model parameters were kRaR, VRCR/FRARMR, VRPR/FRARMR,R RCL/FRARMR, Q/FRARMR, VRCR/F*RDHAR and
CL/F*RDHAR. As with LUM the IIV and IOV of FRARMR was estimated and variability between
doses was larger than between individuals (84.1 vs. 38.1 %). The IIV of CLRARMR was also
estimated. A relationship between CLRARMR and dose number was included and
demonstrated that for each subsequent dose of ARM CLRARMR increased by 67.8%
relative to its value after the first dose. This relationship was accompanied by a
decrease in the OFV (‐82.774, P<0.001) and a reduction in the RUV of both ARM and
DHA. No other covariate relationship improved the model. After the inclusion of
IIV/IOV terms and the covariate relationship, RUV was still high at 51.6% and 53.3% for
ARM and DHA, respectively.
Table 4‐3 Final population pharmacokinetic estimates and bootstrap results for ARM and DHA.
Parameter Final model
estimate (RSE%)a
Bootstrap
Median [95% CI]
Structural model parameters
CL/FRARMR (l/h/70kg) 102 (27) 96.3 [57.0‐167.0]
VRCR/FRARMR (l/70kg) 193 (62) 172 [40‐506]
Q/FRARMR (l/h/70kg) 49.6 (47) 45.8 [19.7‐111.1]
VRPR/FRARMR (l/70kg) 1070 (59) 1220 [593‐3011]
kRaR (/h) 1 [FIXED] 1 [1‐1]
VRCR/F*RDHAR (l/70kg) 440 (40) 417 [69‐826]
CL/F*RDHAR (l/h/70kg) 277 (26) 275 [140‐443]
% increase in CL/FRARMR for each subsequent dose (%)
67.8 (31) 73.3 [40.5‐125]
Random model parameters
IIV in FRARMR (%) 38.1 (72) 19.7 [0.3‐58.6]
IOV in FRARMR (%) 84.1 (38) 84.2 [52.2‐113.6]
IIV in CL/FRARMR (%) 84.0 (33) 75.8 [49.1‐108.2]
RUV for ARM (%) 51.6 (12) 50 [37‐61]
RUV for DHA (%) 53.3 (20) 61 [42‐83] a RSE% are derived from the bootstrap. OFV in the final model: 259.853, bootstrap OFV (median [95% CI]):‐ 177.255[77.606‐249.014]
132
The final model parameter estimates and the bootstrap results are summarized in
Table 4‐3. As the covariance step was not successful, NONMEM‐derived RSE could not
be obtained. Bias was <11% for structural and random parameters except IIV for FRARMR
which had a negative 48% bias. Figure 4‐4 and Figure 4‐5 show GOF plots and VPCs,
respectively. The VPCs show all observed 10th, 50th and 90th percentiles within their
simulated 95% CI and the fraction of BLQ data at each time point within its 95% CI for
both ARM and DHA. Secondary parameters for study participants are shown in Table
4‐4. The AUCR0‐∞R and half‐lives of ARM decreased with each dose while the median
DHA to ARM ratio increased.
Predicted plasma artemether (g/l)
1 10 100
Ob
se
rve
d p
las
ma
art
em
eth
er
( g
/l)
1
10
100
Predicted plasma dihydroartemisinin (g/l)
1 10 100
Ob
se
rve
d p
las
ma
dih
yd
roa
rte
mis
inin
(
g/l)
1
10
100
A B
Figure 4‐4 Population (○) and individual predicted (●) versus observed data for ARM (A) and DHA (B) concentrations (µg/l) for the final model. The lines of identity are also shown. The grey dashed line represents the LOQ of ARM in (A) and DHA in (B).
Time (h)
0 10 20 30 40 50 60 70
Fra
ctio
n B
LQ
0.0
0.2
0.4
0.6
0.8
1.0
Pla
sma
art
emis
inin
( g
/l)
0.1
1
10
100
A
Time (h)
0 10 20 30 40 50 60 70
Fra
ctio
n B
LQ
0.0
0.2
0.4
1.0
Pla
sma
dih
yd
roa
rte
mis
inin
( g
/l)
1
10
100
B
Figure 4‐5 Visual predictive check showing observed 50th (●), 10th () and 90th (○) percen les with the simulated 95% CI for the 50th (solid black line), 10th (grey dotted lines) and 90th (dashed grey lines) percentiles for ARM (A) and DHA (B) concentrations (μg/l on log10 scale) from the final model. The fraction of BLQ observations from the data (○ connected with a do ed black line) with the simulated 95% prediction interval are also shown for both ARM and DHA.
133
Table 4‐4 Secondary pharmacokinetic parameters derived from post hoc Bayesian estimates for study participants. Data are median [inter‐quartile range].
Parameter LUM DBL ARM – Dose 1 ARM – Dose 6 ARM – All doses DHA
tR½αR
aR R(h)
10.4
[10.3 – 11.8]
19.7
[18.4 – 22.5]
0.62
[0.60 – 0.64]
0.16
[0.12 – 0.33]
0.80
[0.76 – 0.82]
tR½βR
a (h) 46.6
[44.8 – 48.2]
141
[135 – 150]
16.4
[15.7 – 16.8]
11.9
[11.2 – 13.2]
tR½γR
a (h) 123
[120 – 127]
AUCR0–∞ R(µg∙h/l)b 459,980
[391,330 – 632,730]
5,434
[4,394 – 8,542]
983
[371 – 1,770]
164
[145 – 254]
3,063
[2,357 – 4,513]
2,839
[1,812 – 3,488]
AUCRMETABOLITER/AUCRPARENTR (%) 1.13
[0.93 – 1.55]
36.8
[36.8 – 36.8]
186
[91.8 – 268]
92.7
[59.2 – 94.3]
atR½αR, tR½βR and tR½γR are the first distribution, second distribution and terminal elimination half‐lives respectively for LUM, while for DBL and ARM tR½αR and tR½βR represent the distribution and terminal elimination half‐life respectively and for DHA tR½αR represents the terminal elimination half‐life. brepresents either the AUCR0–∞R for all six doses together or the AUCR0–∞R for individual doses as if they were given alone.
134
4.2.4.3 116B116BRelationshipbetweendrugexposureandtreatment
outcome
The LUM AUCR0‐∞R tended to be lower in children with recurrent parasitaemia on days
28 (n=5, P=0.057) and 42 (n=8, P=0.086), but this was not the case for DBL (P=0.46 and
0.89, respectively). However, a combined AUCR0‐∞R with DBL weighted four times more
than LUM, consistent with its greater antimalarial potency in vitro,160, 163, 377, 378 was
significantly lower in children with recurrent parasitaemia on day 28 (n=5, P=0.028)
and of borderline significance on day 42 (n=8, P=0.063).
135
4.2.5 54B54BDiscussion
In the present study of PNG children with uncomplicated malaria treated with a
conventional AL regimen, rich datasets of plasma concentrations of LUM, ARM and
their active metabolites measured during an extended follow‐up period were
successfully analysed using population PK modelling that allowed for a high proportion
of BLQ plasma ARM and DHA concentrations. Our analyses included the first
compartmental PK analysis of plasma DBL concentrations. We found that current dose
recommendations for AL in children result in a LUM AUC similar to that achieved in
adults, despite children receiving a higher average mg/kg dose relative to a 50 kg adult.
However, the subgroup of children weighing 12.5‐15 kg receive the lowest mg/kg dose
and may be at risk of under‐dosing.
Three studies, all from Africa, have examined LUM PK after AL treatment in children.
The first and simplest compared crushed tablets and a dispersible formulation using a
pooled analysis of single blood sample taken at one of six time‐points during a 14‐day
period from 726 children aged <12 years.183 The LUM AUC for both formulations was
higher than in the present study (574,000 and 636,000 vs. 459,980 µg∙h/l). In the
second study,144 six blood samples were taken from children aged 5‐13 years starting
when the last AL dose was given and the LUM AUCR60‐∞R was calculated using non‐
compartmental analysis. When we used our final models to generate an AUCR60‐∞R, this
was higher (257,010 vs. 210,000 µg h/l). Based on their data, the authors reported that
children have lower levels of exposure to LUM than adults using recommended AL
dose schedules.144 A third study of children aged 1‐10 years utilized a population
approach142 but there was no sampling beyond 72 h and no secondary PK parameters
were provided. A comparison with LUM disposition in the present study was,
therefore, not possible.
Comparisons of LUM AUC between studies in adults are also difficult as some report
AUC from the first dose while others use AUCR60‐∞R.Table 4‐5 summarizes the available
data for both measures of drug exposure. There is a difference between LUM exposure
in healthy adults and subjects with malaria, but the AUCs for non‐pregnant adults,
136
pregnant adults and children with malaria are similar. Current AL dose
recommendations for children ensure that those weighing 15‐35 kg receive a 35%
higher average mg/kg dose than a 50 kg adult, but those weighing 12.5‐15 kg receive a
lower mg/kg dose. The AUC data support the higher average mg/kg dose in children
and suggest that those weighing 12.5 ‐ 15 kg should receive 2 tablets rather than 1 to
avoid under‐dosing while not exceeding the highest recommended mg/kg dose (Figure
4‐6). As LUM exposure, measured either as AUC or day 7 concentrations, has
previously been shown to be a prime determinate of efficacy,141, 148 it is important that
under‐dosing is avoided.
The three studies of AL in children also measured plasma ARM/DHA concentrations.142,
144, 183 The first was not able to calculate AUCs from pooled concentration data due to a
sparse sampling schedule.183 The second employed a limited sampling schedule
starting from the last AL dose,144 and the AUCs were therefore lower than those of the
present study (168 vs. 217 µg∙h/l for ARM and 382 vs. 402 µg∙h/l for DHA). The
population approach used in the third study142 produced a similar model of the
disposition of ARM (two compartments) and DHA (one compartment) to that of the
present study. The authors reported a similar increase in CL/FRARM Rwith each dose (57%
vs. 67.8 % in our children) and a higher RUV (61% vs. 51.6% and 82 % vs. 53.3% for
ARM and DHA respectively), the latter observation likely a reflection of the fact that
many plasma concentrations were close to or below the LOQ. As no secondary PK
parameters were provided, a comparison of AUCs could not be performed. However
the half‐lives of ARM, estimated from the PK parameters provided, were longer than
those in our children (0.89 vs. 0.62 h and 32.0 vs. 16.4 h for distribution and
Table 4‐5 Summary of studies reporting area under the plasma concentration‐time curve (AUC) for lumefantrine.
Sample AUCR60–∞/t R(µg∙h/l)a AUCR0–∞/t R(µg∙h/l)a
Healthy adults 383,000‐456,000 135, 137 1,242,000‐2,730,000 135, 374b
Non‐pregnant adults with malaria ‐ 335,000‐758,000 134, 164, 184, 376
Pregnant women with malaria 252,000 143 472,000 182
Children with malaria 210,000 142 572,000‐636,000 183c
Present study 257,000 459,980 aAUC was either a median or mean and was reported either to the last data point or to ∞, bas subjects in Bindschedler et al (10) only received a single dose, the reported AUC has been multiplied by six, cthis study used a pooled approach from single observations in each subject to calculate AUC
137
elimination, respectively, of the first dose), while the elimination tR½R of DHA was
shorter (0.38 vs. 0.80 h).
The AUCs for ARM and DHA in the present study were similar to those reported
previously in adults with malaria133, 143 but higher than those in healthy adults.135, 137,
138 Our terminal elimination tR½R for ARM was longer than those reported in these
studies (16.4 vs. 1.5‐3.9 h) while for DHA it was shorter (0.80 vs. 1.2‐2.1 h). The adult
studies used non‐compartmental methods to determine these half‐lives and this may
account for the differences. Nevertheless, based on these comparisons, exposure to
ARM and DHA in children is adequate with current AL dose recommendations.
Few studies have evaluated the disposition of DBL, an active metabolite of LUM. Our
DBL:LUM ratio (1.13%) falls between values reported in previous treatment studies
(0.33% and 5.2%).143, 184 The lower value (0.33%) was from a study of non‐immune
Columbian adults with malaria that sampled to 168 h and reported AUCR0‐168R. The
higher value (5.2%) was from a study of pregnant Thai women with malaria in which
sampling started after the last dose and AUCR60‐∞R was reported. The difference
between these values can, at least in part, be explained by the study designs as the
metabolite‐to‐parent percentage calculated from AUCR60‐∞R in the present study is more
Figure 4‐6 The doses of lumefantrine and artemether in mg/kg given to children 5‐35 kg under current (solid black line) and suggested (dashed grey line) dosing regimens. The horizontal dotted black line represents the dose in mg/kg recommended for a 50 kg adult.
Weight (kg)
5 15 25 35
Lu
mef
an
trin
e d
os
e (
mg
/kg
)
0
6
12
18
24
Weight (kg)
5 15 25 35
Arte
meth
er d
ose
(mg
/kg
)
0
1
2
3
4
138
than double for AUCR0‐168R (1.96 vs. 0.76 %). However it is likely that ethnicity and
pregnancy contribute to the difference. Age may also influence metabolic conversion
of LUM to DBL as our PK model was able to predict concentrations of LUM but not DBL
in younger children effectively. It is uncertain as to whether malaria itself also
influences the ratio since it was 0.45%, within the range of studies of malaria, after a
single dose of AL in 22 healthy adults.384
As reported previously143 DBL had a longer terminal elimination tR½R than LUM in the
present study (141 vs. 123 h, P <0.001) and therefore the DBL:LUM ratio will increase
with time. Although the ratios found in available studies are low, the in vitro potency
of DBL is between 2.2 and 7.2 times that of LUM160, 163, 377, 378 and it may therefore
contribute to therapeutic outcome. We found a combined weighted LUM/DBL AUC
was more likely to be lower than the AUC of either LUM or DBL alone in subjects with
recurrent parasitaemia at days 28 and 42. This supports the suggestion that DBL may
influence AL treatment outcome.163
Although the variable bioavailability of ARM and LUM has been previously reported,164
it has not previously quantified in children. Given the significant increase in fed vs.
fasted healthy volunteers134 it is recommended that AL is administered with fat in
order to improve absorption. Based on a study in healthy adults who received a single
dose of AL, 1.2 g of fat (equivalent to 35 ml of full cream milk) is required to achieve
90% of maximal LUM bioavailability.375 Although these results may not be directly
applicable to the children with malaria in our study, they ingested 2 g of fat with each
dose and there was still significant between‐dose variability in the bioavailability of
both LUM (67.0%) and ARM (84.1%). We were unable to identify factors that may be
responsible for these observations.
In the analysis of the ARM/DHA dataset, there were a significant number of BLQ
plasma concentrations. This is an issue encountered in PK analyses of a variety of other
antimalarial drugs.142, 226, 385 Traditional approaches to this problem such as excluding
BLQ data from the analysis or setting them to a specific value (such as zero or 50% of
the LOQ) have been shown to bias the PK parameters even when only 10% of the data
are BLQ.297, 382, 383, 386 Our approach was to use a method within NONMEM shown to
139
have little bias in situations with up to 40% BLQ data in population analysis.383 This
method treats BLQ data points as categorical data and maximizes the that its value is
truly below the LOQ.297 Although the implementation of this method has previously
been difficult and time consuming, changes to NONMEM and more efficient data
processing have increased its accessibility. The benefits of this method demonstrated
in relatively simple models are likely to apply to more complex models with parent
drug and metabolite. We were unable to obtain RSE for our parameters in this model
as the covariance step was unsuccessful, a common problem when this method is
used.382, 383 However this does not impact on the reliability of the results obtained and
other methods of model evaluation (such as bootstrap and VPC) can still be used.
Our novel data relating to DBL PK and its favourable pharmacodynamic effects suggest
that future efficacy and PK studies of LUM should include DBL assay to further
elucidate its role. We have also shown that analytical techniques that utilize BLQ data
to refine PK parameter estimates can be applied in this situation. Extended sampling
and a population PK approach allow flexibility in deriving secondary parameters, an
important consideration when comparisons with published non‐standard measures
such as time‐limited AUC are of interest. Our data confirm that current AL dose
recommendations produce similar ARM, DHA and LUM exposure in children to that in
adults with malaria. However, smaller children weighting 12.5‐15 kg are at risk of
under‐dosing and AL doses could be doubled without exceeding the current weight‐
based maximum mg/kg dose in this patient group.
140
4.2.6 55B55BAcknowledgements
We thank the children and their parents/guardians for their participation. We are
most grateful to Sr Valsi Kurian and the staff of Alexishafen Health Centre for their kind
co‐operation during the study. We also thank Jovitha Lammey, Christine Kalopo and
Bernard (“Ben”) Maamu for clinical and/or logistic assistance, and Harin Karunajeewa
for assistance with protocol design. The National Health and Medical Research Council
(NHMRC) of Australia funded the study (grant #634343). TMED is supported by an
NHMRC Practitioner Fellowship.
141
142
143
5 4B4BAPharmacokineticComparisonofTwo
Piperaquine‐ContainingArtemisinin
CombinationTherapiesinPapuaNewGuinean
ChildrenwithUncomplicatedMalaria
5.1 17B17BBackground
The aims of this study presented in this chapter were twofold. Firstly to compare the
PK of ART/PQ base with that of DHA/PQ tetraphosphate and secondly to provide
preliminary data on the safety, tolerability and efficacy of ART/PQ in children.
Although ART/PQ base was being marketed and sold as a treatment for children in a
number of countries at the time of this study, the literature was lacking any
pharmacological data of the combination. Meanwhile, the PK of a closely related
combination, DHA/PQ tetraphosphate, had been previously described in PNG
children.387 There was a need to evaluate ART/PQ base in children to determine if it
had similar properties to DHA/PQ tetraphosphate. This study was conceived and
performed in a similar manner to the study of DHA/PQ tetraphosphate to enable PK
comparisons between the two regimens. It also allowed collection of preliminary
safety, tolerability and efficacy data for ART/PQ.
This study resulted in the publication4 presented in this chapter. Entitled, “A
pharmacokinetic comparison of two piperaquine‐containing artemisinin combination
therapies in Papua New Guinean children with uncomplicated malaria” was published
by the journal Antimicrobial Agents and Chemotherapy (2012. 56(6):p. 3288‐97). The
contribution of each of the authors is outlined in section i, which also contains details
of ethical approvals and supporting funding. It has been reformatted to conform to
thesis requirements set by the University of Western Australia. The references have
been combined with those for the thesis as a whole and can be found in section x
below.
144
145
5.2 18B18BPublication
Sam Salman,A Madhu Page‐Sharp,B Kevin T Batty,BR RKay Kose,C Susan Griffin,C Peter
Siba,C Kenneth F. Ilett,A Ivo Mueller,C Timothy M. E. DavisA.
ASchool of Medicine and Pharmacology, University of Western Australia, Fremantle
Hospital, Fremantle, Western Australia, Australia;
BSchool of Pharmacy, Curtin University of Technology, Bentley, Australia;
CPapua New Guinea Institute of Medical Research, Madang, Papua New Guinea.
5.2.1 56B56BAbstract
PK differences between PQ base and PQ tetraphosphate were investigated in 34 Papua
New Guinean children aged 5‐10 years treated for uncomplicated malaria with ART/PQ
base or DHA/PQ tetraphosphate. Twelve children received ART/PQ base (two daily
3:18 mg/kg doses as granules) as recommended by the manufacturer with regular
clinical assessment and blood sampling over 56 days. Plasma PQ concentrations from
22 children with malaria of similar age from a previously‐published PK study of
DHA/PQ tetraphosphate (three daily 2.5:20 mg/kg doses as tablets) were available for
comparison. The disposition of ART was also assessed in the 12 children who received
ART/PQ base. Plasma PQ was assayed by HPLC‐UV detection and ART using liquid
chromatography‐mass spectrometry. Multi‐compartment PK models for PQ and ART
were developed using a population‐based approach. ART/PQ base was well tolerated
and initial fever and parasite clearance were prompt. There were no differences in PQ
AUCR0–∞R between the two treatments with medians of 49,451 (n=12) and 44,556 (n=22)
µg∙h/l for ART/PQ base and DHA/PQ tetraphosphate, respectively. Recurrent
parasitaemia was associated with lower PQ exposure. Using a two‐compartment ART
model, the median AUCR0–∞R was 1,652 µg∙h/l. There was evidence of auto‐induction of
ART metabolism (relative bioavailability for the second dose 0.27). These and
previously‐published data suggest that a three‐day ART/PQ base regimen should be
further evaluated, in line with WHO recommendations for all ACTs.
146
5.2.2 57B57BIntroduction
The most recent WHO recommendations for the treatment of uncomplicated malaria
include a three‐day course of DHA/PQ as a first‐line ACT.388 Various formulations of
DHA/PQ are marketed in tropical countries (Duo‐cotecxin, Combimal and P‐Alaxin)389
or are in development (Eurartesim) ,390 all of which employ PQ tetraphosphate as the
DHA partner drug. DHA is a semi‐synthetic derivative of ART and its production adds to
the manufacturing cost but, unlike ART, it does not exhibit auto‐induction of
metabolism. In addition, although the tetraphosphate salt of PQ has greater water
solubility and therefore may have better oral bioavailability, incorporation of the lipid‐
soluble PQ base should also simplify production.
Artequick (Artepharm Co Ltd, Guangzhou, China) is an ACT that contains ART in place
of DHA and PQ base rather than PQ tetraphosphate. This combination is formulated as
tablets but also as granules for paediatric use. It is marketed in Cambodia and some
sub‐Saharan African countries. The current manufacturer’s recommendation is for
Artequick to be given as a two‐day regimen391 which contrasts with the three days
recommended for all ACTs by the WHO.388 Although the tolerability, safety, efficacy
and PK properties of DHQ/PQ tetraphosphate have been widely investigated in
children and adults,174, 178, 202, 392, 393 there are limited data relating to the efficacy and
tolerability of ART/PQ base211, 212 and no studies of the PK of this novel combination in
malaria‐infected patients. Concerns have been raised regarding possible under‐dosing
of a number of antimalarial drugs in children144, 226 including PQ.202, 392, 393 Although
children have been included in studies of PQ PK,174, 178 only one PK study of ART has
specifically enrolled paediatric patients.125
We have evaluated the population PK of ART/PQ base (Artequick) in children from PNG
with uncomplicated malaria and compared the data with those of a previously
published study of DHQ/PQ tetraphosphate (Duo‐cotecxin; (Beijing Holley‐Cotec,
Beijing, China) in the same category of patients.387 The primary aims of the present
study were to investigate PK differences between PQ base and PQ tetraphosphate, and
to describe the population PK of ART in PNG children. Secondary aims were to provide
preliminary data relating PK factors to recurrent parasitaemia, and to use both PK and
efficacy data to suggest improved dose regimens for these combinations.
147
5.2.3 58B58BPatientsandmethods
5.2.3.1 117B117BPatients
Assessment and recruitment of children for the present and published DHQ/PQ
tetraphosphate studies were as described previously.387 Briefly, all subjects were
children aged 5‐10 years presenting to Alexishafen Health Centre, Madang Province on
the north coast of PNG. The clinic serves an area where Plasmodium falciparum and P.
vivax are hyperendemic, and P. ovale and P. malariae are also transmitted.20 Children
with an axillary temperature >37.5°C or a history of fever in the previous 24 h were
screened with a Giemsa‐stained thick blood film read on‐site by a trained microscopist.
Those with a mono‐infection of P. falciparum (>1,000 asexual parasites µl‐1), or P.
vivax, ovale or malariae (>250 asexual parasites µl‐1) were eligible provided that the
child’s parents gave informed consent, there were no features of severe malaria,379
they had not taken any antimalarial drug in the previous 14 days, there was no
evidence of another cause of fever, and there were no features of malnutrition or
other chronic co‐morbidity. Although the location, population and enrolment
procedures used in the two studies were identical, the DHQ/PQ group was enrolled
between August 2005 and January 2006 while the ART/PQ base group was enrolled
from March 2008 to May 2008.The study was approved by the PNG Institute of
Medical Research Institutional Review Board and the Medical Research Advisory
Committee of the PNG Department of Health.
5.2.3.2 118B118BClinicalmethods
In the present study of ART/PQ base, a standardized history was taken and a clinical
examination was performed. A 3 ml venous blood sample was taken for baseline blood
film microscopy, Hb and blood glucose, and for subsequent drug assay of separated
plasma. Each child treated with granules of ART/PQ base (Artequick) according to WT
(approximately 3:18 mg/kg/day respectively). This dose was repeated at 24 h, as
recommended by the manufacturer, with the exact time of each dose recorded. All
doses were given under direct observation. The full contents of each sachet were
mixed with at least 50 ml of cow’s milk (equivalent to 2 g of fat), as fat has been
reported to increase the bioavailability of PQ tetraphosphate.173, 215 The volume of milk
used was based on previous experience with its palatability and association with
148
nausea in PNG children, as well as the amount of fat found to maximize the absorption
of LUM, another highly lipophilic antimalarial drug, in healthy adults.375
Further venous blood samples were taken from an indwelling intravenous catheter at
1, 2, 4, 12, 24, 28, 36 and 48 h, and then by venesection on days 3, 5, 7, 14, 28, 42 and
56. All samples were centrifuged promptly and RBCs and separated plasma stored
frozen at ‐80°C until assayed. Detailed clinical assessment, including a symptom
questionnaire, blood film, Hb and blood glucose, was repeated on days 1, 2, 3 and 7,
with additional clinical assessment and blood films on days 14, 28, 42 and 56. All blood
smears taken at baseline and during follow‐up were examined independently by at
least two skilled microscopists in a central laboratory. Each microscopist viewed 100
fields at 1,000x magnification before a slide was considered negative. Any slide
discrepant for positivity/negativity or speciation was referred to a third microscopist
for adjudication.
The clinical procedures followed for the DHQ/PQ group have been previously
described387 and were similar to those of the ART/PQ base group. Differences included
i) administration of three days of DHQ/PQ tetraphosphate tablets at a dose of 2.5:20
mg/kg daily (equivalent to 11.5 mg/kg of PQ base daily), ii) drug administration with
water, and iii) blood sampling and clinical follow up to 42 days only.
5.2.3.3 119B119BLaboratorymethods
PQ tetraphosphate reference standard was obtained from Yick‐Vic Chemicals and
Pharmaceuticals, Ltd. (Hong Kong, China); CQ diphosphate and authentic ART were
from Sigma‐Aldrich (St. Louis, USA), and ARM from AAPIN Chemicals Ltd (Abingdon,
UK). Solid phase extraction (SPE) Bond Elut ® PH columns were purchased from Varian
Inc. (Palo Alto, USA). HPLC grade methanol was obtained from Merck Pty Ltd (Kilsyth,
Australia) and LC‐MS grade ammonium formate was from Sigma‐Aldrich (Gillingham,
UK). All other solvents and chemicals were of analytical grade.
For the ART/PQ base group, PQ in plasma was analysed by HPLC as for the original
DHQ/PQ group387 with minor modifications. Briefly, plasma was spiked with CQ as an
internal standard, alkalinized, and extracted into 8 ml of hexane‐isoamyl alcohol (99:1).
149
Baseline samples were assayed for CQ prior to quantification of PQ to ensure no
interference with the internal standard. After centrifugation, the supernatant was
back‐extracted into 100 µl of 0.1 M HCl, aspirated and re‐centrifuged. Aliquots of 80 µl
were injected onto on a Phenomenex CR6R‐phenyl column (Phenomenex, Torrance, CA)
with a mobile phase of 11% acetonitrile in 0.1 M phosphate buffer (pH 2.5) pumped at
1 ml/m. Retention times were 2.5 and 7.3 min for PQ and CQ, respectively and were
detected at 340 nm. The linear assay range was 2‐1,000 µg/l and the intra‐day relative
standard deviations (RSDs) were 10.8%, 8.2%, and 9.4%, and the inter‐day RSDs were
11.6%, 4.4%, and 6.7% at 5, 100, and 1,000 µg/l, respectively. The limits of
quantification and detection were 2 µg/l and 1 µg/l, respectively.
For ART, the extraction procedure used a 1 ml CR18R SPE column as previously
described,380 with following modifications. Briefly, the SPE column was pre‐
conditioned with 1 ml of methanol followed by 1 ml of 1M acetic acid. Plasma samples
(0.5 ml) were spiked with internal standard (ARM, 1,000 µg/l) and loaded onto the pre‐
conditioned SPE column and drawn through using a medium vacuum. The column was
then washed with 1M acetic acid (1 ml x 2), followed by 20% v/v methanol in 1M acetic
acid (1 ml). The column was dried under low vacuum for 30min and retained drugs
were eluted with 2 ml of t‐butyl chloride:ethyl acetate (80:20% v/v). The eluate was
evaporated in a vacuum evaporator at 35°C then reconstituted in 50 µl of mobile
phase and 5 µl aliquots were injected onto the LC‐MS system.
The LC‐MS system used was a single quad mass spectrometer (Model 2020, Shimadzu,
Kyoto, Japan) consisting of a binary pump Mmodel 20AD), vacuum degasser,
thermostated autosampler (model SIL 20ACHT), thermostated column compartment
(Model CTO 20A), photodiode detector (Model SPD M 20A) and mass analyser (Model
MS 2020) with both electrospray ionization (ESI) and atmospheric pressure ionization
(APCI) systems. Analyses were performed in isocratic mode with a mobile phase of
20mM ammonium formate (pH 4.8): methanol (20:80) pumped at a flow rate of 0.2
ml/min. Chromatographic separation was undertaken at 30°C on a Synergy fusion‐RP
CR18R (150 mm x 2.0 mm i.d.) column coupled with a 4 mm x 3 mm i.d., 5 µm particle CR18R
guard column (Phenomenex, Lane Cove, Australia). Retention times were 4.2 min and
7.5 min for ART and ARM respectively. Optimized mass spectra were acquired with an
150
interface voltage of 4.5kV, a detector voltage of 1 kV, a heat block temperature of
400°C and a desolvation gas temperature of 250°C. Nitrogen was used as a nebulizer
gas at a flow rate of 1.5 l/min and dry gas flow of 10 l/min.
Quantification was performed by selected ion monitoring (SIM), using DUIS mode in
which both ACPI and ESI are used simultaneously. All standard curves were linear with
an r2 ≥0.999. Chromatographic data (peak area ratio of ART:ARM) were processed
using the LAB Solution software package (Version 5, Shimadzu, Japan). Responses
from analysis of samples containing three different ART concentrations (5, 200 and
2,000 µg/l) and one ARM concentration (1,000 µg/l) spiked into five separate plasma
samples were used to determine matrix effects (ion suppression/enhancement),
absolute recovery, and process efficiency381 which were between 90 ‐ 98 %, 82‐93%
and 86‐91% respectively. The assay intra‐day RSDs were 9.3, 7.2, and 3.7 % and inter‐
day RSDs were 9.5, 7.1 and 6.5% at 5, 200 and 2,000 µg/l, respectively. The limits of
quantification and detection for ART were 2.5 and 1 µg/l, respectively.
5.2.3.4 120B120BPharmacokineticmodelling
LogReR plasma concentration‐time datasets for PQ and ART were analysed by nonlinear
mixed effects modelling using NONMEM (v 6.2.0, ICON Development Solutions, Ellicott
City, MD, USA) with an Intel Visual FORTRAN 10.0 compiler. The PQ plasma
concentration vs. time data from the published study of DHQ/PQ performed by our
group, which were originally analysed using a patient rather than a population
approach,387 were pooled with the PQ concentration data from the present study. The
FOCE with interaction estimation method was used. OFV and CWRES plots were used
to choose suitable models during the model‐building process. Allometric scaling was
employed a priori, with volume terms multiplied by (WT/70)1.0 and clearance terms by
(WT/70)0.75.394 RUV was estimated as additive error for the log‐transformed data.
Secondary PK parameters including AUCR0–∞R and elimination tR1/2R for the participants
were obtained from post hoc Bayesian prediction in NONMEM using the final model
parameters. Base models were parameterized using kRaR, VRCR/F, CL/F, VRPR/F and Q/F.
For the PQ dataset, two‐ and three‐ compartment models (ADVAN 4 and 12) with first
order absorption with and without lag time were tested. Since inspection of the time
151
concentration curves indicated that there was significant variability in the absorption
phase, a transit compartment model was also tested.287 In this model, the dose passes
through a series of transit compartments before entering the absorption compartment
in order to model the delay often associated with drug absorption. A single rate
constant (kRtrR) describes the entry and exit for all transit compartments. Using a
previously described implementation of the transit compartment model in
NONMEM,287 NN and MTT (equal to (1+NN) / kRtrR) were estimated as continuous
variables. For the ART dataset, 1‐ and 2‐ compartment models (ADVAN 2 and 4) with
first order absorption with and without lag time were evaluated. Once the structure of
the models was established, IIV, IOV, and correlations between IIV terms were
estimated, where supported by the data.
As two different formulations of PQ with different water/lipid solubilities were used,
potential differences in their relative bioavailability were assessed. The difference in
relative bioavailability between first and subsequent doses of PQ and ART was also
investigated. For PQ this was achieved by estimating the difference between the
relative bioavailability of the first dose of PQ phosphate (fixed to 1) and the two doses
of PQ base as well as the two subsequent doses of PQ phosphate. Similarly for ART, the
relative bioavailability of the first dose was fixed to 1 and potential differences
between this and subsequent doses were assessed. The inclusion of an extra
parameter to account for differences in relative bioavailability was only considered if
accompanied by a significant fall in the OFV (>6.63, P<0.01) and an improvement in the
CWRES plot. Differences in absorption parameters (kRaR, NN and MTT) between the two
groups were also assessed within NONMEM. As described below, the effect size of the
difference (%) was estimated. To maintain the extra parameter estimating this
difference, a significant fall in the OFV (>6.63, P<0.01) was required. Differences
between clearance and volume terms between the two formulations were not
assessed, as differences between a salt and base formulation of the same drug are
biologically implausible.
Finally, relationships between model parameters and the covariates age, sex,
log(baseline parasitaemia) and fever were identified through inspection of scatterplots
and boxplots of eta vs. covariate, and subsequently evaluated within NONMEM. The
152
effect size (%) of categorical data (sex, fever) was assessed, while both linear and
power relationships were evaluated for continuous covariates (age, log(baseline
parasitaemia)). For effect size, the individual parameter value = population parameter
value × (1 + effect parameter * covariate value [0 or 1]). For linear relationships, the
individual parameter value = population parameter value × (1 + effect parameter ×
[covariate value for individual]/[average value of covariate]). For power relationships,
the individual parameter value = population parameter value × ([covariate value for
individual]/[average value of covariate]effect parameter). A stepwise forward inclusion and
backward elimination method was used with a significance of P<0.05 required for
inclusion of a covariate relationship and P<0.01 to retain a covariate relationship.
As CQ was used as the internal standard in the PQ assay, the potential impact of
residual CQ in the plasma of the children on PK parameters was assessed through
simulation. A previous study in a similar group of children resident in the same study
area demonstrated that approximately 50% had a measurable plasma CQ
concentration when hospitalized.395 Using plasma CQ concentrations from a previous
PK study of Madang children,387 we simulated i) that half of the children had, at
random, received a treatment course of CQ finishing 14 days prior to the study (just
before to the exclusion period for such treatment) and ii) only children from one of the
treatment groups received CQ treatment 14 days prior to the study. This latter
simulation represents the ‘worst case’ scenario in terms of the effect of residual CQ on
the comparative PK properties of the two PQ formulations through exogenous
augmentation of the internal standard.
5.2.3.5 121B121BModelevaluation
Initially, plots of observed vs. individual and population predicted values, and time vs.
CWRES, were assessed. A bootstrap using Perl speaks NONMEM (PSN) with 1,000
samples was performed (for NQ this was stratified according to dose regimen), and the
parameters derived from this analysis summarized as median and 2.5th and 97.5th
percentiles (95% empirical CI) to facilitate evaluation of final model parameter
estimates. In addition, prediction corrected VPCs (pcVPCs)396 and NPCs were
performed with 1,000 datasets simulated from the final models, and these were
stratified according to treatment group for PQ. The observed 10th, 50th and 90th
153
percentiles were plotted with their respective simulated 95% CI to assess the
predictive performance of the model. NPCs were assessed by comparing the actual
with the expected number of data points within the 20, 40, 60, 80, 90 and 95% PI.
These were also stratified according to treatment group for the PQ model.
5.2.3.6 122B122BStatisticalanalysis
Comparisons between the baseline characteristics and secondary PK parameters of the
subjects in the DHQ/PQ and ART/PQ base studies were assessed using the Mann‐
Whitney U test for continuous variables and the Fisher exact test for categorical
variables. A two‐tailed level of significance of 0.05 was considered significant for all
comparisons.
154
5.2.4 59B59BResults
5.2.4.1 123B123BClinicalcharacteristicsandcourse
The baseline characteristics of all children are summarized in Table 5‐1. Of those who
received ART/PQ base, 11 had a mono‐infection with P. falciparum and one had a
mono‐infection with P. vivax. One child was lost to follow‐up after day 14. ART/PQ
base treatment was well tolerated with reported symptoms were mild, short‐lived (<2
days) and consistent with clinical features of uncomplicated malaria. Initial fever
clearance was <24 h in all cases, and parasite clearance was <48 h in all but one child in
whom it was within 72 h. The child with P. vivax at enrolment cleared parasitaemia
promptly and remained slide negative for the 56 days of follow‐up. Of the 11 children
with P. falciparum, one developed slide‐positive P. falciparum on day 28, another on
day 42 and two more by day 56. As PCR was not performed it was not possible to
determine if these represented recrudescence or re‐infection. Only one child with P.
falciparum at entry became slide positive for P. vivax, on day 56. The mean Hb
concentration, when available, increased as a result of treatment regardless of malaria
status during follow‐up with mean (95% CI) increases from baseline of 1.9 (0.40‐3.3)
(n=9), 1.1 (0.15‐2.5) (n=11) and 1.5 (0.20‐2.5) (n=10) g/dl on days 28, 42 and 56,
respectively (P=0.027, P=0.19 and P=0.041). No cases of hypoglycaemia were
recorded.
Table 5‐1 Baseline characteristics of study participants. Data are number (%), mean ± SD or median [IQR].
19TDuo‐cotecxin387
19T(historical) n=22
19TArtequick
19T(present study) n=12 19TP value
19TAge (years) 19T6.9 ± 1.4 19T7.1 ± 1.5 19T0.790a
19TSex (% male) 19T17 (86%) 19T8 (66%) 19T0.687b
19TWeight (kg) 19T19.1 ± 3.8 19T18.3 ± 3.1 19T0.986a
19TAxillary temperature (°C) 19T37.2 ± 1.2 19T36.3 ± 0.7 19T0.034a
19TP. falciparum parasitaemia 19T19 (86%) 19T11 (92%) 19T1.00b
19TParasite density (/µl whole blood) 19T13,360 [6,900‐51,650] 19T26,270 [3,480‐35,30] 19T0.736a
19TP. vivax parasitaemia 19T2 (9.1 %) 19T1 (8%) 19T1.00b
19TP. malariae parasitaemia 19T1 (4.5 %) 19T0 (0%) 19T1.00b
19THaemoglobin (g/dl) 19T8.6 ± 1.8 19T9.3 ± 2.1 19T0.168a
19TTotal PQ base dose (mg/kg) 19T35.3 ± 4.4 19T38.3 ± 5.8 19T0.136a
19TTotal DHA dose (mg/kg) 19T7.7 ± 1.0
19TTotal ART dose (mg/kg) 19T6.4 ± 1.0 aMann‐Whitney U test, bFisher exact test
155
5.2.4.2 124B124BPharmacokineticmodelling
There were 298 and 174 individual plasma PQ concentrations available from the
DHQ/PQ (n=22) and ART/PQ base (n=12) studies, respectively. No drug concentrations
were BLQ during the 56‐day follow up period. A 3‐comparment model fitted the data
better than a 2‐compartment model with a significant decrease in the OFV (Δ OFV = ‐
109.232, P<0.001). Although the addition of a lag‐time improved the model
significantly (Δ OFV = ‐31.059, P<0.001), the absorption phase was poorly described
with first‐order absorption with or without lag‐time. Therefore a transit compartment
model was tested where NN and MTT through the transit compartments, were
estimated as continuous variables. The transit compartment was significantly better
than a model with lag‐time, resulting in a 37.173 point reduction in the OFV (P<0.001).
Further testing of the combined data sets with models in which the absorption process
of the two formulations of PQ differed (for example, use of a lag‐time model for PQ
base and a transit compartment model for PQ tetraphosphate) were also tested and
offered no advantage over the use of a single transit compartment model. A three‐
compartment model remained superior to a two‐compartment model with the use of
a transit compartment absorption (Δ OFV = ‐57.937, P<0.001).
The structural model parameters were kRaR, NN, MTT, VRCR/FRPQR, VRP1R/FRPQR, VRP2R/FRPQR, CL/FRPQR,
QR1R/FRPQR, QR2R/FRPQR and FR1,ArtequickR. There was poor precision for the estimate of kRaR (%RSE
>100%) as well as a high correlation between kRaR and MTT (>0.95). Therefore, with the
data available in this study, these two parameters could not be estimated
simultaneously and kRaR was set to be the same as kRtrR, i.e. equal to (1+NN) / MTT. IIV was
estimable for MTT, CL/FRPQR, VRCR/FRPQR andVRP1R/FRPQR. Correlation between IIV terms was
estimated for CL/FRPQ Rand VRCR/FRPQR and VRCR/FRPQR andVRP1R/FRPQR. The IOV on FRPQR was also
estimable and accompanied by significant falls in OFV (Δ OFV = ‐69.12, P<0.001) and
RUV (35% to 29%). There was no significant difference between the relative
bioavailability of the two formulations, or between the subsequent doses of PQ base
or tetraphosphate when compared to the first dose. Although inspection of the
concentration‐time curves appeared to indicate a difference in the absorption phase
between the two formulations, when differences in NN and MTT were evaluated, they
did not improve the model. Likewise, none of the tested covariates improved the
model.
156
Table 5‐2 Final population pharmacokinetic estimates and bootstrap results for piperaquine.
19TParameter 19TFinal model
19Testimate (RSE%)
19TBootstrap (n=1000)
19Tmedian [95% CI]
19TStructural and covariate model parameters
19TMTT (h) 19T1.27 (11) 19T1.25 [1.12‐1.58]
19TNN 19T4.20 (19) 19T3.70 [2.77‐5.36]
19TCL/FRPQR (l/h/70kg) 19T40.1 (7) 19T40.7 [36.6‐45.1]
19TVRCR/FRPQR (l/70kg) 19T2,580 (13) 19T2,550 [1,996‐3,142]
19TQR1R/FRPQR (l/h/70kg) 19T113 (21) 19T119.0 [84.3‐166.0]
19TVRP1R/FRPQR (l/70kg) 19T2,760 (24) 19T3,440 [2,750‐5,510]
19TQR2R/FRPQR (l/h/70kg) 19T52.4 (15) 19T52.9 [43.8‐67.1]
19TVRP2R/FRPQR (l/70kg) 19T21,600 (8) 19T22,300 [19,300‐25,320]
19TRandom model parameters
19TIOV in FRPQR (%) 19T46 (14) 19T42 [36‐54]
19TIIV in CL/FRPQR (%) 19T16 (53) 19T16 [5‐29]
19TIIV in VRCR/FRPQR (%) 19T53 (33) 19T45 [31‐71]
19TIIV in VRP1R/FRPQR (%) 19T68 (32) 19T64 [16‐93]
19TIIV in MTT (%) 19T43 (13) 19T42 [34‐52]
19TR (CL/FRPQR, VRCR/FRPQR ) 19T0.33 19T0.272 [‐0.186‐0.710]
19TR (VRCR/FRPQR, VRP1R/FRPQR ) 19T0.85 19T0.874 [0.381‐1.00]
19TRUV (%) 19T29 (5) 19T29 [27‐32]
RSE calculated from bootstrap results. OFV in final model: ‐329.926, bootstrap OFV: (median [95% CI]) ‐316.869 [‐416.930‐‐285.019].
The impact of residual CQ proved to be minimal as assessed using the simulations, with
population PK parameter estimates differing by <9%. When all participants in the same
formulation group were presumed to have taken CQ 14 days prior to the start of the
study, there was still no significant difference between the population PK parameter
estimates of the two PQ formulations.
The final model parameter estimates and the bootstrap results for both PQ
formulations are summarized in Table 5‐2. Bias was <10% for all fixed and random
157
Time (h)
1 10 100 1000Co
nd
itio
na
l we
igh
ted
re
sid
ua
ls (
pip
era
qu
ine
)
-4
-2
0
2
4
Predicted plasma piperaquine (g/l)
1 10 100 1000
Ob
se
rve
d p
las
ma
pip
era
qu
ine
(
g/l)
1
10
100
1000
A
B
Figure 5‐1 (A) Population predicted (○) and individual (●) predicted versus observed plasma piperaquine concentrations (µg/l on logR10R scale) for the final model. The line of identity is also shown. (B) Conditional weighted residuals vs. time (log scale) for piperaquine final model.
model parameters. With the exception of IIV in CL/FPQ all parameters were reasonably
well estimated with RSE of <33%. The correlation between CL/FPQ and VC/FPQ
displayed a wide 95% CI (‐0.186‐0.710). Figure 5‐1 and Figure 5‐2 show GOF plots and
pcVPCs, respectively. The pcVPCs show wide 95% CI for the 10th, 50th and 90th
percentiles due to relatively small numbers of children. The actual 10th, 50th and 90th
percentiles fell into their respective 95% CI for all time‐points for both groups. The
stratified NPCs demonstrated good predictive performance with the expected number
of points above and below the 20, 40, 60, 80, 90 and 95% PIs. The half‐lives, total
AUCR0–∞R and dose adjusted AUCR0–∞R are shown in Table 5‐3. There were no significant
differences in either of these secondary parameters between the two PQ compounds.
The first distribution, second distribution and terminal elimination tR1/2R for all
158
Table 5‐3 Secondary pharmacokinetic parameters of piperaquine derived from post hoc Bayesian estimates for study participants, and day 7 plasma piperaquine concentrations. Data are median [inter‐quartile range].
a Mann‐Whitney U test; b tR½αR, tR½βR and tR½γR are the first distribution, second distribution and terminal elimination half‐lives respectively.
19TParameter 19TPQ (Duo‐cotecxin)
19Tn=22 19TPQ (Artequick)
19Tn=12 19TP valuea
19Tt½α b (h) 19T4.44 [3.43 ‐ 5.30] 19T4.52 [3.76 ‐ 6.41] 19T0.48
19Tt½β b (h) 19T36.1 [33.0 ‐ 45.2] 19T35.3 [28.1 ‐ 58.7] 19T0.82
19Tt½γ b (h) 19T513 [503 ‐ 574] 19T512 [497 ‐ 566] 19T0.82
19TDay 7 concentration (µg/l) 19T39.3 [34.9 ‐ 45.9] 19T42.0 [34.6 ‐ 55.6] 19T0.56
19TAUCR0–∞R (µg∙h/l) 19T49,451 [40,507 – 52,438] 19T44,556 [33,215 – 51,873] 19T0.36
19TAUCR0–∞R (µg∙h/l) / total PQ dose (mg/kg)
19T1.27 [1.06 ‐ 1.50] 19T1.37 [1.09 ‐ 1.65] 19T0.40
participants had median values of 4.5, 36.0 and 512 h respectively. The median PQ
AUCR0–∞Rs for the Artequick and Duo‐cotecxin formulations were 49,451 µg∙h/l and
44,556 µg∙h/l, respectively.
Of the ninety‐six ART drug concentrations (ART/PQ base group, n=12) that were
available for analysis, six (6.25%) were BLQ but above the limit of detection. As these
Time (h)
0 200 400 600 800 1000 1200 1400
Pla
sm
a p
ipe
raq
uin
e (
g/l)
1
10
100
1000
B
Time (h)
0 24 48 72 96
Pla
sm
a p
ipe
raq
uin
e (
g/l
)
1
10
100
1000
Time (h)
0 200 400 600 800 1000 1200 1400
Pla
sm
a p
ipe
raq
uin
e (
g/l)
1
10
100
1000
A
Time (h)
0 24 48 72 96
Pla
sm
a p
ipe
raq
uin
e (
g/l
)
1
10
100
1000
Figure 5‐2 Visual predictive check showing observed 50th (●), 10th () and 90th (○) percen les with the simulated 95% CI for the 50th (solid black line), 10th (grey dotted lines) and 90th (dashed grey lines) percentiles for plasma piperaquine concentrations (µg/l on logR10R scale) vs. time (h) for Artequick (A) and Duo‐cotecxin (B) from the final model. The observed data are superimposed as grey crosses. The insert shows data for the first 96 h.
159
represented a small proportion of the data, they were included at their measured
values. All twelve children has measurable concentrations of ART to 48 h. Initial
modelling of the ART dataset demonstrated a two‐compartment model was
significantly better than a one‐compartment model (ΔOFV = ‐73.417, P<0.001) and that
the absorption phase was best described by a first‐order absorption without a lag time.
Therefore, the structural model parameters were kRaR, VRCR/FRARTR, VRPR/FRARTR,R RCL/FRARTR and
Q/FRARTR. The IIV of VRCR/FRARTR was estimable, as was the IOV on FRARTR. The data supported
the estimation of a relative bioavailability term for the second dose of ART (FR2, ARTR),
with its addition resulting in a significant fall in the OFV (Δ OFV = ‐24.029, P<0.001).
The bioavailability of the second dose was 0.270, relative to the first. No significant
covariate relationships were identified.
The final model parameter estimates and the bootstrap results for ART are
summarized in Table 5‐4. Bias was <10% for all fixed and random parameters. kRaR was
not well estimated with a RSE of 55%, and a four‐range in the non‐parametric 95% CI.
Figure 5‐3 and Figure 5‐4 show GOF plots and pcVPCs, respectively. The pcVPC showed
all observed 10th, 50th and 90th percentiles were within their simulated 95% CI. Due to
the small numbers in the analysis these CI were wide and overlapping. The NPC
Table 5‐4 Final population pharmacokinetic estimates and bootstrap results for artemisinin (n=12).
19TParameter 19TFinal model
19Testimate (RSE%)
19TBootstrap
19TMedian [95% CI]
19TStructural model parameters
19TkRaR (/h) 19T1.67 (55) 19T1.62 [1.01‐4.40]
19TCL/FRARTR (l/h/70kg) 19T124 (12) 19T125 [99‐157]
19TVRCR/FRARTR (l/70kg) 19T590 (30) 19T533 [318‐874]
19TQ/FRARTR (l/h/70kg) 19T43.7 (38) 19T46.4 [19.5‐79.4]
19TVP/FRARTR (l/70kg) 19T435 (26) 19T456 [259‐696]
19TFR2, ART R– relative bioavailability of 2nd dose 19T0.270 (17) 19T0.275 [0.192‐0.368]
19TRandom model parameters
19TIOV in FRARTR (%) 19T43 (27) 19T39 [15‐58]
19TIIV in CL/FRARTR (%) 19T12 (29) 19T12 [4‐18]
19TRUV (%) 19T33 (11) 19T32 [26‐38]
RSE (Relative standard error) calculated from bootstrap results. OFV in final model: ‐63.562, bootstrap OFV: (median [95% CI]‐73.838 [‐110.720‐‐43.043].
160
demonstrated good predictive performance with the expected number of points above
and below the 20, 40, 60, 80, 90 and 95% PIs. The tR1/2R, the AUCR0–∞R of each dose as well
as the total AUCR0–∞R for the study participants are shown in Table 5‐5. The median
distribution and terminal elimination tR1/2R were 1.55 and 7.43 h while the median total
AUCR0–∞R
was
1,652
µg∙h/l.
Predicted plasma artemesinin (g/l)
1 10 100 1000
Ob
se
rve
d p
las
ma
art
em
isin
in (
g/l)
1
10
100
1000A
Time (h)
0 12 24 36 48Co
nd
itio
na
l we
igh
ted
re
sid
ua
ls (
art
em
isin
in)
-4
-2
0
2
4B
Figure 5‐3 (A) Population (○) and individual (●) predicted versus observed plasma artemisinin concentra ons (µg/l on logR10R scale) for the final model. The line of identity is also shown. (B) Conditional weighted residuals vs. time for artemisinin final model.
Table 5‐5 Secondary pharmacokinetic parameters for artemisinin derived from post hoc Bayesian estimates for study participants. Data are median [inter‐quartile range].
19TParameter 19TART (Artequick)
19Tn=12
19TtR½αR
a (h) 19T1.55 [1.49 ‐ 1.60]
19TtR½βR
a (h) 19T7.43 [7.22 ‐ 7.68]
19TAUC (µg∙h/l) – first dose 19T1,347 [1,065 – 1,594]
19TAUC (µg∙h/l) – second dose 19T312 [253 ‐ 438]
19TAUCR0–∞R (µg∙h/l) – total 19T1,652 [1,333 – 2,177] a tR½α Rand tR½βR represent the distribution and terminal elimination half‐life respectively AUCs for each dose has been calculated using standard PK formula to determine the relative contribution of each dose to the total AUCR0–
∞R
161
5.2.4.3 125B125BRelationshipbetweendrugexposureandtreatment
outcome
In the eight children who became slide positive for P. falciparum by day 42 (two in the
ART/PQ base group and six in the DHQ/PQ group), the AUCR0–∞R of PQ was significantly
lower than in those who remained free of P. falciparum infection (median 39,297 vs.
49,776 µg∙h/l, P=0.0060). Clearance and terminal elimination tR1/2R were not significantly
different, however, these children received a lower total mg/kg dose of PQ (median
31.4 vs. 35.7 mg/kg of PQ base, P=0.11). When adjusted for dose, the AUCR0–∞R was no
longer significant between those children with and without slide positivity for P.
falciparum by day 42 (median 1.16 vs. 1.42 µg∙h/l per mg/kg of PQ base, P=0.14).
There was a significant positive correlation between AUCR0–∞R and day 7 drug
concentrations that did reach significance (r=0.70, 95%CI 0.48 – 0.84, P<0.001). Unlike
AUCR0–∞R, day 7 concentrations were not significantly lower in those who developed P.
falciparum slide positivity (n=8) compared to those who did not (n=26) (median 44.1vs.
48.0 µg/l, P=0.22). Similar results were evident when the two children from the
DHQ/PQ group who developed slide positivity for P. vivax by day 42 were included in
the analysis (data not shown). In the child who took >48 hours to clear initial
parasitaemia, the AUCR0–∞Rs of ART and PQ were within the ranges of the other patients.
Time (h)
0 12 24 36 48P
las
ma
art
em
isin
in (
g/l)
1
10
100
1000
Figure 5‐4 Visual predictive check showing observed 50th (●), 10th () and 90th (○) percen les with the simulated 95% CI for the 50
th (solid black line), 10th (grey dotted lines) and 90th (dashed grey lines) percentiles for plasma artemisinin concentrations (µg/l on logR10R scale) vs. time (h) from the final model. The observed data are superimposed as grey crosses.
162
5.2.5 60B60BDiscussion
The development of ACTs has seen a variety of different combinations, formulations
and dose regimens introduced into clinical use without a detailed assessment of
tolerability, safety, PK and efficacy. One recently‐marketed ACT, Artequick, appears
relatively inexpensive to produce but uses component drugs that have not been
investigated extensively, especially in a paediatric setting. The present PK and
preliminary efficacy study in PNG children adds to available data 212, 213 suggesting that
there would be benefit in extending the Artequick manufacturer’s recommended two‐
day regimen to three days as this will increase PQ exposure and thus limit late
recurrence of parasitaemia. However, the selection of a relatively low dose of ART (3
mg/kg vs. the 10‐20 mg/kg per dose conventionally recommended), a drug that
induces its own metabolism, may have implications for efficacy, especially in patients
with limited immunity to malaria or where parasite resistance against artemisinin
compounds has started to develop.397
Children in the DHQ/PQ tetraphosphate group were given a mean of 35.3 mg/kg PQ
base over three days387 compared with 38.3 mg/kg PQ base over two days in the
present children treated with ART/PQ base. Overall the exposure to PQ was similar
between the two formulations, and no differences in the post hoc PK parameters were
identified. Although, this suggests that the tablet and granule formulations have
similar bioavailability and that the small amount of fat we administered with each dose
(2 g) is unlikely to influence exposure to PQ, it is not possible to differentiate the
differential influence of food and formulation with the current study design. Two of
three studies involving healthy adults found that fat‐containing foods increased
exposure to PQ tetraphosphate,175, 176, 215 but the volunteers in these studies
consumed relatively large quantities of fat (17‐54 g). Consistent with the present
results, 6.4 g of fat did not increase the exposure to PQ tetraphosphate in adults with
malaria.214 However PQ base is less water soluble than PQ tetraphosphate and
exogenous lipids are known to increase the solubility of lipophilic drugs and thus affect
the extent of absorption.398 PQ base may behave similarly to LUM, another highly
lipophilic drug, and require a smaller amount of fat to maximize absorption.375 Granule
formulations have been reported to increase bioavailability relative to tablets399
consistent with the increased surface area available for dissolution compared to
163
tablets, but our data suggest that this was not a major effect in the case of Artequick.
Future studies evaluating the effect of food and formulation on the disposition of PQ
base in malaria could help refine dose regimens for therapies such as Artequick.
A model with three compartments and a transit sequence prior to absorption best
described the PQ concentration‐time dataset. Most previous studies have used a two‐
compartment model.174, 175, 178 One study in healthy adults found that, although a
three‐compartment model described the post‐administration profile better, there
were insufficient data to support its use over a two‐compartment model.177 The mean
elimination tR1/2R, a parameter influenced by the duration of sampling,216 was 512 h,
within the previously‐reported range of 224‐667 h.172‐179 Since there was substantial
variability in the absorption phase of the plasma PQ concentration profile, a transit
compartment model was tested and proved better than simpler absorption models
that used lag time, as has been found in studies of other drugs.287
It has recently been suggested that children should be given a higher dose of PQ than
adults due to lower day 7 plasma concentrations202 and reduced efficacy.202, 392, 393 This
is supported by comparative PK studies in children and adults that found children had
a higher clearance174 or a lower PQ exposure at critical times during the illness.178
These concerns have also been raised for other antimalarial drugs144, 226 and reflect PK
effects due to the effects of body size, maturation and organ function.394 Although only
children aged between 5 and 10 years were included in the present study, we found
that recurrence of parasitaemia was associated with a lower PQ AUC resulting from a
lower mg/kg dose, consistent with other studies of DHQ/PQ tetraphosphate.196 As PCR
was not performed, these cases may represent either recrudescence (treatment
failure) or reinfection (failure of post‐treatment prophylaxis).
The dose‐adjusted PQ exposure in our children was similar to that found in Caucasian
and Vietnamese healthy adults172, 177, 215 and Thai adults with malaria.214 When
compared to studies of Vietnamese and Chinese healthy adults, the dose‐adjusted PQ
exposure was three and six times lower in our children, respectively,173, 175 suggesting
that there are PQ PK differences between populations. Currently recommended PQ
tetraphosphate doses are 18 mg/kg/day (10 mg/kg/day PQ base) for 3 days.388 A
164
higher average daily dose of PQ base in the Artequick group (19 mg/kg/day) was well
tolerated when given for two days and the same dose given on the third day might
both satisfy WHO recommendations for duration of ACT and address the issue of the
need for higher mg/kg doses in children.
The use of CQ as an internal standard for the PQ is potentially problematic in samples
taken from a malaria‐endemic area where CQ is widely available and used empirically
for treatment of fever. Utilizing CQ usage and PK data from other studies in children
from the Madang area,387, 395 we investigated whether the 14‐day exclusion for prior
antimalarial treatment was sufficient to limit such potential confounding. Even in the
worst case scenario, there was only a small effect on the estimated PK parameters and
one which did not produce falsely significant differences between the two
formulations. Although this is reassuring, it would be best if future similar studies
employed an alternative internal standard.
A number of published studies have evaluated the PK of ART in healthy adults112‐117
and adults with malaria118‐124 but only one has included children with malaria.125 In this
latter Vietnamese study, 23 children aged 2‐12 years were given five days of ART
dosed according to WT (approximately 10 mg/kg) and 31 adults received 500 mg ART
daily for 5 days. Sparse sampling was used to characterize ART population PK in plasma
using NONMEM after the first and final doses, with two samples collected from each
patient on day 1 and a single sample collected on day 5 from some patients. A one‐
compartment model was used, with clearance and volume terms for children and
adults estimated separately. The median WTs and ages of the children were lower in
the present study (18.3 vs. 20 kg and 7.1 vs. 9 years, respectively). Although our value
for kRaR was comparable to that in the Vietnamese study (2.0/h vs. 1.7 /h), a two‐
compartment model provided a better fit in the present study with distribution and
elimination tR1/2Rs of 1.9 h and 8.3 h respectively, compared to a tR1/2R of 1.8 h in the
previous study.125 This difference may reflect the longer sampling duration in the
present study (24 h vs. 8 h post dose) which enabled the identification of a second
exponential phase in the elimination of ART.
165
The elimination tR1/2R of ART has been reported to be between 1.4 and 4.8 h in non‐
compartmental112, 113, 117, 118, 120, 121, 123, 124, 400 and compartmental114‐116, 122, 125 analyses.
The present analysis supports a bi‐exponential disposition for ART, while most previous
compartmental analyses have reported a mono‐exponential disposition. A shorter
sampling duration may be responsible for this difference, as sampling was confined to
<10 h after the last dose in all but one of the studies to date. In addition, assay
sensitivity may also contribute by limiting quantification of ART to those samples taken
<12 h after dose.114, 115 One study of healthy adults given a single dose of ART112 also
reported a bi‐exponential disposition and found longer distribution (2.61 h vs. 1.55 h)
and shorter elimination (4.34 h vs. 7.43 h) tR1/2Rs than in the present study. Although this
study sampled blood to 24 h post dose, ART could only be quantified in samples up to
8 h.
The present median AUCR0–∞R of the first dose of ART (1,347 µg∙h/l) was within the
range reported for healthy adults (1,190‐2,690 µg∙h/l)112‐115, 117 but well below that of
adults with malaria (2,601‐2,780 µg∙h/l)118, 123, 400 who received 500 mg of ART. Our
children received a lower dose of ART 3.2 mg/kg/day and, when adjusted for the
relative dose administered, the AUCR0–∞R for the first dose was above those seen in
adults.118, 123, 400 The auto‐induction of ART metabolism has been well characterized,
with a primary effect on the bioavailability of subsequent doses rather than on
systemic clearance.116 It is likely, therefore, this represents an increase in the activity of
gut wall rather than liver metabolism.
We found a difference in the PK of ART for the second dose that was explained by a
lower relative bioavailability of 0.27 when compared to the first dose. When the AUCs
of different doses have been compared in previous studies, the relative bioavailability
after 4‐7 days was between 0.13 and 0.29.113, 117, 121, 123, 125, 400 One study of African
adults with malaria who received 500 mg ART daily for three days and a single dose of
MQ124 measured ART in saliva and found relative bioavailability was only lower on the
third day (0.45) when MQ was given after the last dose of ART. However, when MQ
was given on the first day at the same time as the first dose of ART, the relative
bioavailability of both the second and third doses of ART was lower, at 0.23 and 0.25
respectively.
166
Our data are in agreement with a rapid mean auto‐induction time of 1.9 h estimated
using a semi‐physiological model for ART116 indicating that all doses after the first have
a lower relative bioavailability. If a third daily dose of ART/PQ base was given, its
relative bioavailability would also be low. The rapid initial parasite clearance in
Artequick‐treated children in the present study despite relatively low and short‐lived
plasma ART concentrations, may reflect the level of malarial immunity in this area of
intense transmission.401 It is likely that relatively low ART doses, even if given over
three days rather than two, might not be as effective where transmission and
consequent immunity are less or where parasite resistance against artemisinin
compounds has started to develop.397
When compared to three days of DHQ/PQ tetraphosphate, the efficacy of two days of
Artequick in adults was equivalent in one study211 and inferior in another.212 A three‐
day Artequick regimen (3.2 and 16.0 mg/kg/day of ART and PQ base, respectively) has
been found to be both well tolerated and more effective than a two day regimen.213
Our preliminary data suggest that the efficacy of two days of Artequick appeared
similar to that of three days of Duo‐cotecxin in PNG children. However, the weight of
evidence from previous studies,212, 213 the low dose of ART in Artequick and its auto‐
induction at a time when the spectre of ART resistance has emerged,397 and the issue
of potential PQ under‐dosing in children would all support further evaluation of a
theoretically more efficacious three‐day Artequick regimen, as recommended by the
WHO for all ACTs.388
167
5.2.6 61B61BAcknowledgements
We are most grateful to Sr Valsi Kurian and the staff of Alexishafen Health Centre for
their kind co‐operation during the study. We also thank Jovitha Lammey, Christine
Kalopo and Bernard (“Ben”) Maamu for clinical and/or logistic assistance. Dr Harin
Karunajeewa is acknowledged for his pivotal role in co‐ordinating the original DHA/PQ
tetraphosphate study. We thank Artepharm Co Ltd for kind provision of Artequick. The
National Health and Medical Research Council (NHMRC) of Australia funded the study
(grant #634343). TMED is supported by an NHMRC Practitioner Fellowship.
168
169
6 5B5BArtemisinin‐NaphthoquineCombination
TherapyforUncomplicatedPaediatricMalaria:
APharmacokineticStudy
6.1 19B19BBackground
The primary aim of the final study presented in this thesis was to provide the first PK,
safety, tolerability and efficacy evaluation of ART/NQ in children. As a number of
different dose regimens were used it also aimed to assess the impact of these different
dose regimens on PK, safety, tolerability and efficacy.
As with ART/PQ base, ART/NQ is a combination readily available for purchase in a
number of countries despite few pharmacological data available in the literature.
Unlike ART/PQ base, however, the partner drug, NQ, is not present in any other
available antimalarial formulation. Data of this combination at the time of this study
was lacking in adults, let alone children. The role of the first part of the study was to
determine the required paediatric dose the recommended adult dose. In the second
part, supported by a preliminary analysis of the data from the first part, two different
dose regimens were tested. This study was designed not only to ensure the safety and
efficacy of a combination not previously studied in children, but also to provide a basis
for future efficacy evaluations (now ongoing in PNG).
This study resulted in the publication5 presented in this chapter. Entitled, “Artemisinin‐
naphthoquine combination therapy for uncomplicated paediatric malaria: A
pharmacokinetic study” it was published by the journal Antimicrobial Agents and
Chemotherapy (2012. 56(5):p. 2472‐2484). The contribution of each of the authors is
outlined in section i, which also contains details of ethical approvals and supporting
funding. It has been reformatted to conform to thesis requirements set by the
University of Western Australia. The references have been combined with those for
the thesis as a whole and can be found in section x below.
170
171
6.2 20B20BPublication
Kevin T. Batty,A,B Sam Salman,C Brioni R. Moore,C John Benjamin,D Sook Ting Lee,C
Madhu Page‐Sharp,A Nolene Pitus,D Kenneth F. Ilett,C Ivo Mueller,E Francis W.
Hombhanje,F Peter Siba,D Timothy M. E. DavisC.
ASchool of Pharmacy, Curtin University, Bentley, Western Australia
BCurtin Health Innovation Research Institute, Curtin University, Bentley, Western
Australia
CSchool of Medicine and Pharmacology, University of Western Australia, Crawley,
Western Australia
DPapua New Guinea Institute of Medical Research, Madang, Papua New Guinea
EInfection and Immunity Division, Walter and Eliza Hall Institute of Medical Research,
Victoria, Australia and Center de Recerca en Salut Internacional de Barcelona (CRESIB),
Barcelona, Spain
FCentre for Health Research, Divine Word University, Madang, Papua New Guinea
6.2.1 62B62BAbstract
ART/NQ is a co‐formulated antimalarial therapy marketed as single‐dose treatment in
PNG and other tropical countries. To build on limited knowledge of the PK properties
of the components, especially the tetra‐aminoquinoline NQ, we studied ART‐NQ
disposition in PNG children aged 5‐12 years with uncomplicated malaria, comparing
single‐dose (15:6 mg/kg) administered with water (Group 1, n=13), single‐dose (22:9
mg/kg) with milk (Group 2, n=17) or two daily 22:9 mg/kg doses with water (Group 3,
n=16). Plasma NQ was assayed by HPLC and ART using liquid chromatography‐mass
spectrometry. Population‐based multi‐compartment PK models for NQ and ART were
developed. NQ disposition was best characterized by a three‐compartment model with
a mean absorption tR½R of 1.0 h and predicted median maximum plasma concentrations
that ranged up to 57 µg/l after the second dose in Group 3. The mean NQ elimination
tR½R was 22.8 days CL/F was 1.1 l/h/kg and VRSSR/F was 710 l/kg. Administration of NQ with
fat (8.5 g; 615 kJ) vs. water was associated with a 25% increased bioavailability. ART
disposition was best characterized by a two‐compartment model with mean CL/F (4.1
l/h/kg) and V/F (21 l/kg) that were similar to those of previous studies. There was a
77% reduction in the bioavailability of the second ART dose (Group 3). NQ has PK
172
properties that confirm its potential as an artemisinin partner drug for treatment of
uncomplicated paediatric malaria.
173
6.2.2 63B63BIntroduction
Available data relating to the PK of the antimalarial drug NQ phosphate are limited and
inconsistent. Initial reports suggested that NQ has a high oral bioavailability (>90%)
and a tR½R of 41‐57 h.217 In a more recent study in healthy Chinese men in which NQ was
given alone and co‐formulated with ART,220 the elimination tR½R of NQ was substantially
longer at 250‐300 h. This volunteer study also showed that the AUC for NQ exhibited
an unusual relationship between formulation and co‐administered fat. The mean value
was similar in the fasted NQ monotherapy and fed combination ART‐NQ therapy
groups but more than double this in the fasted volunteers given the fixed
combination.220 The fact that the highest bioavailability was in the fasting state
appears in contrast to the effect of fat on absorption of related drugs such as LUM and
PQ,173, 215, 375, 402 while the apparent beneficial effects of co‐formulation on
bioavailability was difficult to explain.220
It has been shown that NQ is a P‐glycoprotein substrate and that NQ efflux is
saturable,403 suggesting that absorption could be non‐linear at high doses. However,
the Chinese volunteer study of ART‐NQ found dose‐proportional increases in the
maximum plasma concentration (CRmaxR) and AUC for NQ at doses of between 200 and
600 mg.220 The maximum individual value for CRmaxR was just over 100 µg/l in this
study,220 but a CRmaxR of up to 245 µg/l has been reported after a 600 mg dose in
adults.217
The Chinese volunteer study of NQ and ART‐NQ reported a tR½R for ART of 3.6‐4.0 h, a
CL/F of approximately 1.5 l/h/kg and V/F of 8 l/kg.220 By contrast, a number of previous
studies in healthy adult volunteers112, 115, 404, 405 and patients with uncomplicated
falciparum malaria118, 119, 123, 125, 406 have reported lower mean values for tR½R, of 2.0‐2.7
h (mean 2.3 h), higher mean values for CL/F of 5.1‐9.3 l/h/kg (mean 6.7 l/h/kg), and a
higher mean V/F of 16.4‐35.5 l/kg (mean 27 l/kg). The reported mean CL/F and V/F for
ART in children were even greater at 14.4 l/h/kg and 38 l/kg, respectively.125 The
Chinese study did, however, show that the AUC for ART increased with co‐
administered fat,220 consistent with most past reports.407
174
Because of inconsistencies between the few published studies of NQ PK and a lack of
PK data in children, we conducted two PK studies of ART‐NQ in children from PNG with
uncomplicated malaria. An initial pilot study (Study 1), carried out before the
manufacturer had produced a paediatric dosing schedule and utilizing a conservative
(calculated in mg/kg using WT based on the dose for adults) was designed to provide
preliminary PK data relating to NQ disposition in children, while the second study
(Study 2) aimed to characterize the PK of NQ as well as ART in more detail when given
at the manufacturer’s recommended dose with fat (milk) or as a two‐dose regimen.
175
6.2.3 64B64BPatientsandmethods
6.2.3.1 126B126BPatientsandclinicalmethods
Full details of the studies have been provided in a separate report.408 In brief, children
aged 5‐10 years who presented with an axillary temperature >37.5°C or a history of
fever in the previous 24 h who were slide‐positive for malaria (P. falciparum >1,000
asexual parasites/μl whole blood or P. vivax >250 parasites/μl) were eligible provided
that had no complications or concomitant illness, no prior treatment with study drugs
no history of allergy to ART or aminoquinoline drugs. Each child’s parents or guardians
gave written informed consent. Approvals were obtained from the PNG Institute of
Medical Research Institutional Review Board and the Medical Research Advisory
Committee of the PNG Health Department.
At enrolment, a history was taken and a full physical examination was performed. An
intravenous cannula was inserted and a baseline venous blood sample was drawn. In
Study 1, all children were administered ARCO™ tablets (50 mg NQ plus 125 mg ART;
Kunming Pharmaceuticals, Kunming, China) orally as a single dose of 2‐4 whole tablets
with water (Group 1). The dose was based on WT as per those recommended by the
manufacturer in mg/kg for adults,409 and represented a dose range of 5.0‐7.5 mg/kg
for NQ phosphate and of 12.5‐16.8 mg/kg for ART. Subjects were not required to fast
prior to, or after, treatment. If the child vomited within one hour the same dose was
re‐administered and the time of re‐administration recorded.
In Study 2, children were randomized by a computer‐generated sequence to receive
ARCO tablets (50 mg NQ plus 125 mg ART) orally based on WT as recommended by the
manufacturer for children409 as either i) a single dose of 3‐6 tablets given with 250 ml
full cream cow’s milk (8.5 g fat) with dose ranges of 6.1‐9.5 mg/kg for NQ and 15.3‐
23.8 mg/kg for ART (Group 2), or ii) the same dose given with water on two occasions
24 h apart (Group 3). Each child was kept fasting under observation and if he/she
vomited within 1 h the same dose was re‐administered and the time of re‐
administration recorded.
176
Group 1 patients had additional venous blood samples drawn through the cannula at
1, 2, 4, 8, 12, 18, 24, 48, 72 h, and by venesection at 4, 7, 14, 28, 42, 56 days. Blood was
collected into lithium heparin tubes, centrifuged at 1,800 g for 5 min and the
separated plasma stored at ‐80°C until analysed for NQ concentration within 8 months
of collection. These children were re‐assessed clinically at 4 and 24 h, and on days 2, 3,
7, 14, 28 and 56.408 Group 2 and 3 patients had further 2.5 ml blood samples for drug
assay taken at 1, 2, 4, 8, 12, 18, 24, 48 and 72 h through the sampling cannula, and by
venesection at 4, 7, 14, 28 and 42 days. Group 3 patients had a second ART‐NQ dose
given with water at 24 h. Groups 2 and 3 had similar post‐treatment clinical and other
monitoring to that performed in Group 1.408
6.2.3.2 127B127BAnalyticalmethods
NQ diphosphate was obtained from ZYF Pharm Chemicals, Shanghai, China, tramadol
hydrochloride and ART were from Sigma‐Aldrich Chemicals, St Louis, MO, USA, and
ARM was from AAPIN Chemicals Ltd, Abingdon, Oxon, UK. All general laboratory
chemicals were of analytical grade (Sigma‐Aldrich Chemicals, St Louis, MO, USA; Merck
Chemicals, Darmstadt, Germany).
NQ in plasma was analysed using a validated HPLC assay, based on established
analytical methods for CQ, PQ and MQ.387, 410 Briefly, plasma samples (500 µl) were
spiked with tramadol as internal standard (500 ng), alkalinized with sodium
tetraborate 2% w/v solution (1 ml) and extracted into 8 ml hexane:ethyl acetate
(80:20) by shaking for 10 min. The samples were then centrifuged at 1300 g for 10 min.
Supernatant (7.5 ml) was back extracted into 0.1 ml of 0.1 M HCl by shaking for 5 min,
followed by centrifugation as above. The HCl layer was transferred to 1.5 ml
microcentrifuge tubes and re‐centrifuged at 1300 g for 25 min to evaporate traces of
organic solvent, after which 70 µl was injected onto the HPLC. Analytes were separated
on a Luna CR18R HPLC column (length, 100 mm; internal diameter[i.d.] 4.6 mm; particle
size 3 µm; Phenomenex, Australia) in series with an Octadecyl CR18R (length, 4 mm; i.d. 3
mm; Phenomenex, Australia) guard column at 30°C with a mobile phase of 18% v/v
acetonitrile in 50 mM KHR2RPOR4R buffer (pH 2.5) pumped at 1 ml/min. Approximate
retention times for NQ and tramadol were 9.4 min and 6.8 min, respectively and the
analytes were detected by UV absorbance at 222 nm (see Figure 6‐1). The linear
177
calibration
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178
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179
standard (ARM, 1 µg), loaded onto the pre‐conditioned SPE column and drawn through
using a medium vacuum. The column was then washed with 1M acetic acid (1 ml; two
washes), followed by 20% v/v methanol in 1M acetic acid (1 ml). The column was dried
under low vacuum for 30 min and retained drugs were eluted with 2 ml of t‐butyl
chloride:ethyl acetate (80:20% v/v). The eluate was evaporated in a vacuum
evaporator at 35°C, reconstituted in 50 µl of mobile phase and 5 µl aliquots injected
into the LC‐MS system.
The single‐quad LC‐MS system (Model 2020, Shimadzu, Kyoto, Japan) comprised a
binary pump (20AD), vacuum degasser, thermostated autosampler (SIL 20ACHT),
thermostated column compartment (CTO 20A), photodiode detector (SPD M 20A) and
mass analyser (MS 2020) with both electrospray ionization (ESI) and atmospheric
pressure chemical ionization (APCI) systems. Analysis was performed in the isocratic
mode with 20 mM ammonium formate (pH 4.8):methanol (20:80) at a flow rate of 0.2
ml/min. Chromatographic separation was undertaken at 30°C on a Synergy Fusion‐RP
CR18R 4 µm column (150 mm × 2 mm i.d.) coupled with a 5 µm CR18R guard column (4 mm ×
2 mm i.d.; Phenomenex, Australia). Retention times were 4.3 and 7.9 min for ART and
ARM respectively (see Figure 6‐2). Optimized mass spectra were acquired with an
interface voltage of 4.5kV, a detector voltage of 1kV, a heat block temperature of
400°C and a desolvation gas temperature of 250°C. Nitrogen was used as the nebulizer
gas at a flow rate of 1.5 l/min and a dry gas flow of 10 l/min.
Both ART and ARM standard solutions were first scanned from (m/z 100‐500) in ESI
and APCI positive mode, as well as combined ESI and APCI (DUIS) mode to identify the
abundance of ions corresponding to respective drugs. The base peak intensity of all
three modes were compared, and showed that DUIS mode gave highest signal
intensity. Therefore, quantitation was performed by selected ion monitoring (SIM),
using DUIS mode. For ART, the parent molecule [M+H]+, (m/z = 283) was used for
quantitation, while for ARM the predominant fragmented ion (m/z =221) was
monitored.411
All standard curves were linear (r20.999). Chromatographic data (peak area ratio of
ART:ARM) were processed using LAB Solution (Version 5, Shimadzu, Japan). Responses
180
from the analysis of three ART concentrations (5, 200 and 2,000 µg/l) spiked into five
separate plasma samples were used to determine matrix effects (ion
suppression/enhancement), absolute recovery and process efficiency.381, 412 Three sets
of matrix solutions were prepared. Set 1 comprised blank plasma spiked first and then
extracted, Set 2 comprised blank plasma extracted first and then spiked post‐
extraction, and Set 3 comprised pure solutions of analyte. The matrix effect (%) was
determined from [Set 2 response × 100]/[Set 3 response], process efficiency (%) from
[Set 1 response × 100]/[Set 3 response] and absolute recovery (%) from [Set 1
response × 100]/[Set 2 response]. The mean ± SD (range) matrix effect for ART was 94
± 12 (79‐105), 90 ± 13 (75‐106) and 91 ± 2 (88‐92)% at 5, 200 and 2,000 µg/l,
respectively. The mean ± SD (range) process efficiency for ART was 93 ± 18 (73‐121),
84 ± 11 (75‐102) and 82 ± 4 (80‐89)% at 5, 200 and 2,000 µg/l, respectively. The mean
± SD (range) absolute recovery for ART was 88 ± 10 (78‐101), 86 ± 9 (77‐102) and 90 ±
7 (87‐101)% at 5, 200 and 2,000 µg/l, respectively. The mean ± SD (range) matrix
effect, process efficiency and absolute recovery for the internal standard, ARM, were
98 ± 10 (87‐113), 88 ± 4 (82‐92) and 91 ± 6 (81‐96)% at 1,000 µg/l. The assay intra‐day
RSDs were 9.3, 7.2 and 3.7% at 5, 200 and 2,000 µg/l, respectively (n=5), while the
inter‐day RSDs were 9.5, 7.1 and 6.5% at 5, 200 and 2,000 µg/l, respectively (n=15).
Inter‐day accuracies determined from the QC samples for each assay batch at 5, 200
and 1,000 µg/l were 108 ± 7% (86‐114), 103 ± 6 (93‐109) and 107 ± 8 (86‐115),
respectively (n=16). The limits of quantification and detection for ART were 2.5 and 1
µg/l, respectively.
6.2.3.3 128B128BPharmacokineticandstatisticalanalysis
The PK properties of NQ were assessed using non‐compartmental analysis (Kinetica
Version 4.4.1; Thermo LabSystems Inc., Philadelphia, PA, USA) for Group 1 subjects and
the data (not shown) were used to refine the study design for Groups 2 and 3. All NQ
data were subsequently pooled and analysed by population PK methods, as were ART
data which were available for Groups 2 and 3.
In the population PK analysis, logReR concentration‐time datasets of NQ and ART were
analysed by nonlinear mixed effect modelling using NONMEM (Version 6.2.0, ICON
Development Solutions, Ellicott City, MD, USA) with an Intel Visual FORTRAN 10.0
181
compiler. NQ data were available for all three groups and ART were available only for
Groups 2 and 3. Linear mammillary model subroutines within NONMEM, FOCE with ‐
interaction, and the OFV were used to construct and compare plausible models.
Unless otherwise specified, a difference in OFV ≥ 3.84 (2 distribution with 1 d.f.,
P<0.05) was considered statistically significant. Secondary PK parameters including VRSSR,
AUCR0–∞R and elimination tR½R for the participants were obtained from post‐hoc Bayesian
prediction in NONMEM using the final model parameters. Macro constants for the
three compartment model were calculated from the modelled parameters using
previously published equations.333 CRmaxR and TRmaxR were estimated by predicting the
concentration of NQ and ART for each individual at six minute intervals to capture the
post‐dose peak.
Allometric scaling was used a priori with all volume terms scaled using (×(WT/70)1.0)
and all clearance terms using (×(WT/70)0.75).309 IIV was added to parameters for which
it could be estimated from the available data. An additive error model was used for
RUV, approximating proportional error as logReR concentration data were used. In the
development of the final models, the influence of the following covariates on the
various model parameters was investigated: dose group, dose occasion, relative dose
(mg/kg), gender, spleen grade, malaria status (by slide positivity), baseline
logR10R(parasitaemia), age, fever and initial Hb concentration. Covariate relationships
identified using the generalized additive modelling procedure within Xpose332 and by
inspection of correlation plots of eta vs. covariate were evaluated within NONMEM.
The potential effect of these covariates on bioavailability, particularly dose group and
occasion, was also considered in cases where a similar relationship was identified for
all volume and clearance terms, given these were relative to bioavailability. The effect
size (%) of categorical data was assessed while both linear and power relationships
were evaluated for continuous covariates. For linear relationships: individual
parameter value = population parameter value×(1+effect parameter×[covariate value
for individual]‐[median value of covariate]), and for power relationships: individual
parameter value = population parameter value×([covariate value for
individual]/[median value of covariate]effect parameter). A stepwise forward inclusion and
backward elimination method was used with a significance level (P value) of <0.05,
accompanied by a decrease in the IIV of the parameter, was required for inclusion of a
182
covariate relationship, and a P value of <0.01 to retain a covariate relationship. For
relationships involving bioavailability, a fall in the IIV of any volume or clearance terms
was required. Correlations among IIV terms were also investigated and CWRES plots
were assessed in arriving at a final model. Two and 3‐compartment models for NQ and
1‐ and 2‐compartment models for ART were compared with first order absorption,
with and without lag time.
A bootstrap procedure in Perl speaks NONMEM (PsN), stratified according to dose
group and WT, was used to sample individuals from the original dataset, and to
generate 1,000 new datasets that were subsequently analysed using NONMEM. The
resulting parameters were then summarized as median and 2.5th and 97.5th percentiles
(95% empirical CI) to facilitate evaluation of the final model parameter estimates. In
addition, pcVPCs304 were performed using PsN with 1,000 replicate datasets simulated
from the original dataset. The observed 10th, 50th and 90th percentiles were plotted
with their respective simulated 95% CI to assess the predictive performance of the
model.304 As a number of covariate effects were found in the model building process
for NQ, NPCs were performed stratified according to those covariates and were
assessed by comparing the actual with the expected number of data points within the
20, 40, 60, 80, 90 and 95% PI.
Data analysis and representation were performed using SigmaPlot version 11 (Systat
Software Inc., San Jose, CA). Data are mean ± SD unless otherwise indicated. Student’s
t‐test (for parametric data) or Mann‐Whitney U test (non‐parametric data) was used
for two‐sample comparisons as appropriate with a significance level of P<0.05.
183
6.2.4 65B65BResults
6.2.4.1 129B129BClinicalcharacteristicsandcourse
Thirteen of 15 Group 1 children completed all essential requirements for the PK
component of the study. All of these children had P. falciparum infections at baseline
and one had a mixed infection with P. vivax at low density (160 parasites/µl of blood).
In Groups 2 and 3, there were four children in each group who were considered to
have a low‐grade parasitaemia on screening microscopy at the study site but who were
subsequently found to be slide‐negative on confirmatory expert microscopy. All
recruited Group 2 and 3 children were included in the PK study. Demographic data are
summarized in Table 6‐1.
The content of NQ in the ARCO tablets was determined by dissolving each tablet (n=5)
in 500 ml water using sonication (twice, for 5 min each time), and measuring the
concentration in 8 aliquots. The ART content was determined after dissolving each
tablet (n=6) in 250 ml methanol and following the same procedure. The mean NQ and
ART contents of the ARCO tablets used in the study were 49 ± 5 mg (nominal potency
50 mg NQ) and 129 ± 3 mg (nominal potency 125 mg ART), respectively.
Table 6‐1 Demographic data for children given artemisinin‐naphthoquine for the treatment of uncomplicated falciparum malaria. Data are mean ± SD unless otherwise indicated.
Group 1 Group 2 Group 3
Number 13 17 16
Gender 6 male:
7 female
11 male:
6 female
12 male:
4 female
Age (years) 7.1 ± 1.8 7.7 ± 2.0 6.7 ± 1.6
Weight (kg) 18.0 ± 3.7 18.9 ± 5.2 16.8 ± 3.2
Height (cm) 110 ± 10 117 ± 12 110 ± 9
Admission parasitaemia
(/µl of blood)a
14,757
(5,189‐41,966)
6,674
(2,264‐19,674)
29,416
(12,290‐70,406)
Naphthoquine dose (mg/kg)
6.3 ± 0.9 8.8 ± 1.4 2 × (9.5 ± 0.9)
Artemisinin dose (mg/kg) 15.7 ± 2.3 22.0 ± 3.6 2 × (23.8 ± 2.2) aGeometric mean and 95% confidence interval for children with parasitaemia
184
6.2.4.2 130B130BNaphthoquinepharmacokineticsandpharmacodynamics
The plasma concentration‐time profiles for NQ are shown in Figure 6‐3. For pooled
data from the three groups, a three‐compartment model proved superior to a two
compartment model with a lower OFV (‐404.855 vs. ‐388.736; P<0.01) and no bias in
the CWRES plot in the initial stages of modelling. As there was no evidence of model
misspecification using a three‐compartment model with first order absorption with a
lag‐time, more complex models were not tested. The structural model parameters
(where C refers to the central compartment and P1 and P2 to the two peripheral
compartments) were kRaR, lag time, CL/F, VRCR/F, VRP1R/F, VRP2R/F, and QR1R/F and QR2R/F.
Estimates for the IIV of kRaR, VRCR, VRP2R, CL and QR2R and correlation between some IIV pairs
Time (h)
0 200 400 600 800 1000 1200
Pla
sm
a n
ap
hth
oq
uin
e (
g/l)
1
10
100A
Time (h)
0 200 400 600 800 1000 1200
Pla
sm
a n
ap
hth
oq
uin
e (
g/l)
1
10
100B
Time (h)
0 20 40 60 80 100
Pla
sm
a n
ap
hth
oq
uin
e (
g/l
ite
r)
1
10
100
Time (h)
0 20 40 60 80 100
Pla
sm
a n
ap
hth
oq
uin
e (
g/l
ite
r)
1
10
100
Time (h)
0 200 400 600 800 1000 1200
Pla
sm
a n
ap
hth
oq
uin
e (
g/l)
1
10
100C
Time (h)
0 20 40 60 80 100
Pla
sm
a n
ap
hth
oq
uin
e (
g/l
ite
r)
1
10
100
Figure 6‐3 Time‐concentration plots of NQ for Group 1 (Panel A), Group 2 (Panel B; milk) and Group 3 (Panel C; water and double‐dose) patients. Inset shows plasma concentration‐time data from 0‐100 h after the dose.
185
(kRaR:VRCR/F, VRCR/F:CL/F, CL/F:VRP2R/F and VRP2R/F:QR2R/F) could be obtained (see Table 6‐2).
Significant covariate relationships were added in the following order (written as
covariate‐parameter [relationship type]); fever‐predicted F [negative categorical], first
dose for Group 3‐predicted F[negative categorical] and Hb‐VRCR/F [positive linear].
Although children in Group 2 were estimated to have an approximately 50% lower kRaR,
this
relationship did not satisfy the significance requirements to be included in the final
model (0.01<P<0.05). Fever (axillary temperature >37.3oC) and the first NQ dose in
Table 6‐2 Population pharmacokinetic parameters and bootstrap results for NQ in children with uncomplicated falciparum malaria.
Parameter Final Model
estimate (RSE%)
Bootstrap
median [95% CI]
Structural and covariate model parameters
kRaR (/h) 1.1 (22) 1.0 [0.7‐1.6]
Lag time (h) 0.7 (7) 0.7 [0.6‐0.8]
VRCR/F (l/70kg) 12,500 (15) 12,200 [9,503‐14,958]
VRP1R/F (l/70kg) 15,500 (19) 17,000 [11,843‐83,415]
VRP2R/F (l/70kg 17,200 (8) 16,000 [10,343‐21,600]
CL/F (l/h/70kg) 51.9 (6) 51.5 [30.1‐58.7]
QR1R/F (l/h/70kg) 40.6 (9) 48.2 [24.2‐113.0]
QR2R/F (l/h/70kg) 398 (13) 407 [318‐536]
% decrease in predicted F with fever 31.8 (21) 31.8 [18.3‐47.1]
% decrease in predicted F with 1st dose in Group 3
26.3 (33) 27.7 [9.3‐40.9]
% increase in VRCR/F per g/dl haemoglobin 16.4 (66) 14.9 [3.2‐19.1]
Random model parameters
IIV in kRaR (%) 104 (14) 103 [80‐131]
IIV in VRCR/F (%) 77 (9) 77 [63‐90]
IIV in CL/F (%) 32 (13) 31 [23‐57]
IIV in VRP2R/F (%) 37 (17) 40 [25‐59]
IIV in QR2R/F (%) 52 (41) 50 [6‐84]
R (kRaR, VRCR/F) 0.20 0.25 [‐0.08 – 0.58]
R (VRCR/F, CL/F) 0.47 0.46 [0.04 – 0.72]
R (CL/F, VRP2R/F) 0.50 0.51 [0.09 – 0.86]
R (VRP2R/F, QR2R/F) 0.20 0.18 [‐0.62 – 0.91]
RUV (%) 24 (7) 24 [21‐26]
OFV in final model: ‐687.786, bootstrap OFV: (median [95% CI] ‐712.006 [‐817.316 ‐ ‐615.107]
186
Group 3 were associated with 32% and 26% decreases in bioavailability, respectively.
Every 1 g/dl increase in Hb increased VRCR/F by 16%. Presence of slide positivity at
baseline and logR10R(parasitaemia) were not significant covariates in the model. The
residual error for the model was 24% (see Table 6‐2).Goodness of fit and CWRES plots
for NQ are shown in Figure 6‐4. The results of the parameter estimates and the
bootstrap results are summarized in Table 6‐2and post hoc Bayesian parameter
estimates with derived secondary PK parameters in Table 6‐3. The bootstrap
demonstrated reasonable estimates of structural and covariate effect parameters with
a bias of <10% for all parameters except VRP1R for which bias was 19%. Random
parameters had a bias of <7%. AUC was significantly higher in Group 3 (two doses)
when compared to Groups 1 and 2 (P<0.001) and was higher in Group 2 when
compared to Group 1. When the AUC was normalized for total relative (in mg/kg),
Predicted plasma naphthoquine (g/l)
1 10 100
Ob
se
rve
d p
lasm
a n
ap
hth
oq
uin
e (
g/l)
1
10
100
Time (h)
1 10 100 1000
Co
nd
itio
na
l we
igh
ted
re
sid
ua
ls (
na
ph
tho
qu
ine
)
-6
-4
-2
0
2
4
6
A
B
Figure 6‐4 (A) Population predicted (○) and individual predicted (●) versus observed NQ plasma concentra on (µg/l; log scale) for the final model. The line of identity is shown. (B) Conditional weight residuals vs. time (log scale) for NQ final model.
187
there was no longer any significant difference between the groups. The predicted CRmaxR
was <200 µg/l in all children apart from one Group 3 child with a value of 270 µg/l after
188
Table 6‐3 Post hoc Bayesian parameter estimates and derived secondary pharmacokinetic parameters for NQ in children with uncomplicated falciparum malaria. Data are median [IQR].
aP=0.053 and 0.094 for the comparison between Groups 2 and 1, and Groups 2 and 3, respectively. btR½αR, tR½βR and tR½γR are the first distribution, second distribution and terminal elimination half‐lives respectively. cP<0.01 for comparisons between Groups 3 and 1, and Groups 3 and 1. dP<0.01 for comparison between Groups 3 and 1
Parameter Group 1
(n = 13)
Group 2
(n = 17)
Group 3
(n = 16)
kRaR (/h)a 1.3 [0.9 ‐ 1.6] 0.7 [0.4 ‐ 1.0] 1.7 [0.6 ‐ 2.2]
CL/F (l/h) 17.3 [15.3 ‐ 21.8] 19.5 [16.2 ‐ 25.1] 16.6 [14.9 ‐ 19.2]
VRCR/F (l) 2,115 [1,735 – 2,753] 3,494 [1,817 – 7,818] 3,610 [1,383 – 7,030]
VRP1R/F (l) 3,986 [3,432 – 4,318] 3,986 [3,321 – 4,871] 3,543 [3,183 – 3,903]
VRP2R/F (l) 4,392 [3,602 – 5,208] 4,662 [3,816 – 5,347] 4,370 [3,633 – 4,760]
VRSSR/F (l) 10,464 [9,366 – 13,888] 13,161 [10,485 – 14,053] 12,001 [9,390 – 14,882]
tR½αR (h)b 6.8 [4.4 ‐ 9.2] 8.2 [5.7 ‐ 9.7] 7.3 [5.5 ‐ 12.0]
tR½βR (h)b 109 [92 ‐ 121] 115 [103 ‐ 126] 118 [104 ‐ 130]
tR½γR (h)b 525 [490 ‐ 544] 500 [455 ‐ 629] 595 [525 ‐ 624]
AUCR0–∞R (µg∙h/l)c 5,935 [4,776 – 6,551] 7,104 [5,954 – 7,914] 15,385 [13,200 – 18,486]
AUCR1R/dose (µg∙h/l per mg/kg) 917 [822 ‐ 1158] 728 [611 ‐ 1004] 813 [629 ‐ 999]
Relative bioavailabilityd 1.00 [1.00 ‐ 1.00] 1.00 [0.68 ‐ 1.00] 0.75 [0.75 ‐ 0.87]
Observed day 7 concentration (µg/l)c 7.0 [4.9 ‐ 8.3] 8.1 [7.3 ‐ 9.8] 17.9 [12.0 ‐ 22.9]
Predicted CRmax1R ‐ Dose 1 (µg/l) 40.6 [32.6 ‐ 45.5] 33.9 [14.7 ‐ 52.7] 22.9 [14.1 ‐ 49.1]
Predicted TRmax1R ‐ Dose 1 (h) 3.1 [2.7 ‐ 3.7] 4.6 [3.7 ‐ 7.1] 3.3 [2.4 ‐ 4.8]
Predicted CRmax2R ‐ Dose 2 (µg/l) 57.0 [42.2 ‐ 138]
Predicted TRmax2R ‐ Dose 2 (h) 27.3 [26.7 ‐ 28.3]
189
the second dose. The pcVPC for NQ, shown in, demonstrates reasonable predictive
performance of the model. NPCs stratified according to dose group (three strata), Hb
(three strata) and fever (two strata) showed good predictive performance, with the
expected number of data points above and below most PI (data not shown). As the
dose‐corrected PK parameters were consistent across the three groups (Table 6‐3),
data were pooled to provide estimates from the total of 46 patients. Overall, mean±SD
CL/F, VRssR/F, tR½αR, tR½βR and tR½γR for NQ were 1.30 ± 0.45 l/h/kg, 805 ± 256 l/kg, 8.2 ± 3.8 h,
98 ± 16 h and 518 ± 94 h respectively.
Of the 13 of 15 Group 1 patients included in the PK analysis, seven developed
recurrent parasitaemia during the 42‐day follow up period.408 One was a PCR‐
confirmed recrudescence of P. falciparum, four had reinfections with P. falciparum,
and two had an emergence of P. vivax. In Group 2 there was only one emergent P.
vivax and no P. falciparum recurrence, while there were no episodes of slide positivity
during follow‐up in Group 3. The AUCR0‐RRR and Day 7 concentrations of NQ were
significantly lower in the children with any parasitaemia during follow‐up than in those
who remained free of malaria infection (P=0.001 and P=0.005, respectively). However,
the NQ dose in mg/kg was also significantly lower (P=0.001) and the difference in
AUCR0‐RRR was no longer significant when corrected for dose (P=0.97), indicating that the
lower dose rather than individual PK differences was responsible. Day 7 NQ
concentrations correlated significantly with AUCR0‐RRR overall (r=0.91, P<0.001) and in
each of the three groups (r>0.79; P<0.001).
Time (h)
0 200 400 600 800 1000
Pla
sm
a n
ap
hth
oq
uin
e (
g/l
)
1
10
100
Tim e (h)
0 20 40 60 80 100Pla
sm
a n
aph
tho
qu
ine
(
g/l)
1
10
100
Figure 6‐5 Prediction corrected VPC plots for NQ in children with uncomplicated falciparum malaria, showing the observed 50th (●), 10th and 90th (○) percen les with the simulated 95% CI for the 50th (solid black line), 10th and 90th (dashed grey lines) percentiles. Inset shows plasma concentration‐time data from 0‐100 h after the dose.
190
6.2.4.3 131B131BArtemisininpharmacokinetics
Raw plasma ART concentration‐time data are presented in Figure 6‐6. A two‐
compartment model was superior to a one‐compartment model for ART with a lower
OFV (255.146 vs. 122.637, P<0.01) and an improved CWRES. As there was no evidence
of model miss‐specification using a two‐compartment model with first order
absorption and a lag‐time, more complex models were not tested. The structural
model parameters for ART were kRaR, lag time, CL/F, VRCR/F, VRPR/F and Q/F (inter‐
compartmental clearance for VRPR/F). Estimates for the IIV of CL, VRCR/F, kRaR and lag time
could be estimated and a full covariance matrix was obtained. The correlation between
CL/F and V/F was >0.99 and was fixed to 1. As the CWRES plot revealed plasma
concentrations after the second dose were lower than expected, the effect of dose
occasion on observed F was tested as a negative categorical relationship. The addition
of this relationship reduced the OFV by 46.626 (P<0.001) and reduced the residual
Time (h)
0 12 24 36 48
Pla
sm
a a
rte
mis
inin
(
g/l)
1
10
100
1000
10000A
Time (h)
0 12 24 36 48
Pla
sm
a a
rte
mis
inin
(
g/l)
1
10
100
1000
10000B
Figure 6‐6 Time‐concentration plots of ART for Group 2 (Panel A; milk) and Group 3 (Panel B; water and double‐dose) patients.
191
Table 6‐4 Population pharmacokinetic parameters and bootstrap results for ART in children with uncomplicated falciparum malaria.
Parameter Final Model
estimate (RSE%)
Bootstrap
median [95% CI]
Structural and covariate model parameters
kRaR (/h) 1.8 (110) 1.8 [0.6‐6.5]
Lag time (h) 0.7 (31) 0.7 [0.4‐0.9]
VRCR/F (l/70kg) 1160 (31) 1140 [625‐1520]
VRPR/F (l/70kg) 166 (37) 211 [96.1‐1270]
CL/F (l/h/70kg) 178 (12) 176 [141‐216]
Q/F (l/h/70kg) 14.2 (103) 15.3 [6.6‐52.3]
Covariate effect parameters
% decrease in predicted F with 2nd dose 77.0 (9) 78.6 [63.3‐89.9]
Random model parameters
IIV in CL/F (%) 57 (25) 56 [43‐67]
IIV in lag time (%) 23 (169) 21 [5‐57]
IIV in kRaR (%) 139 (81) 141 [72‐230]
IIV in VRCR/F : IIV in CL/F (ratio) 0.995 (28) 0.989 [0.760‐1.671]
R (CL/F, lag time) 0.571 0.565 [0.201‐0.997]
R (CL/F, kRaR) 0.0225 0.011 [‐0.550‐0.628]
R (lag time, kRaR) ‐0.340 ‐0.343 [‐0.956‐0.319]
R (CL/F, VRCR/F) 1 FIXED
RUV (%) 51 (31) 50 [37‐65]
OFV in final model: 85.171, bootstrap OFV: (median [95% CI] 73.924 [‐60.386‐178.368]
error of the model by 7%. The second ART dose had 77% lower bioavailability relative
to the first. No other covariate relationships were identified. The residual error in the
final model was 51% (Table 6‐4).
Goodness of fit and CWRES plots for ART are shown in Figure 6‐7. The results of the
final parameter estimates and the bootstrap results are summarized in Table 6‐4 and
post hoc Bayesian parameter estimates with derived secondary PK parameters in Table
6‐5. The bootstrap demonstrated reasonable estimates of structural and random
parameters with a bias of <8% and <10% respectively, with the exception of VRPR/F
where there was a positive bias of 27%. No significant differences were found in
secondary parameters between the groups although there was substantial variability
192
Predicted plasma artemisinin (g/l)
1 10 100 1000 10000
Ob
se
rved
pla
sma
art
emis
inin
(
g/l)
1
10
100
1000
10000
Time (h)
0 10 20 30 40 50
Co
nd
itio
na
l we
igh
ted
re
sid
ua
ls (
art
emis
inin
)
-6
-4
-2
0
2
4
6
A
B
Figure 6‐7 (A) Population predicted (○) and individual predicted (●) versus observed ART plasma concentra on (µg/l; log scale) for the final model. The line of identity is shown. (B) Conditional weight residuals vs. time (log scale) for ART final model.
within each group. The pcVPC for ART is shown in Figure 6‐8 and demonstrates
reasonable predictive performance of the model.
Time (h)
0 12 24 36 48
Pla
sm
a a
rte
mis
inin
(
g/l
)
1
10
100
1000
10000
Figure 6‐8 Prediction corrected VPC plots for ART in children with uncomplicated falciparum malaria, showing the observed 50th (●), 10th and 90th (○) percen les with the simulated 95% CI for the 50th (solid black line), 10th and 90th (dashed grey lines) percentiles.
193
Table 6‐5 Post hoc Bayesian parameter estimates and derived secondary pharmacokinetic parameters for artemisinin in children with uncomplicated falciparum malaria. Data are median [IQR]. All between‐group comparisons were statistically non‐significant.
Parameter Group 2
(n = 17)
Group 3
(n = 16)
kRaR (/h) 2.0 [0.7 ‐ 3.5] 1.1 [0.8 ‐ 4.1]
CL/F (l/h) 82.1 [76.2 ‐ 74.8] 66.9 [61.5 ‐ 62.4]
VRCR/F (l) 348 [246 ‐ 449] 279 [165 ‐ 324]
Q/F (l/h) 5.13 [4.47 ‐ 5.96] 4.69 [4.33 ‐ 5.05]
VRPR/F (l) 42.7 [35.6 ‐ 52.2] 37.9 [34.1 ‐ 41.8]
VRSSR/F (l) 388 [289 ‐ 482] 315 [202 ‐ 362]
tR½αR (h) 2.8 [2.7 – 3.0] 2.7 [2.5 ‐ 2.8]
tR½βR (h) 6.8 [6.2 – 7.0] 6.6 [6.5 ‐ 6.9]
AUCR1R ‐ Dose 1 (µg∙h/l) 5,127 [3,631 – 8,237] 6,770 [5,249 – 10,235]
AUCR1R/dose (µg∙h/l per mg/kg) 267 [170 ‐ 340] 281 [202 ‐ 417]
AUCR2R ‐ Dose 2 (µg∙h/l) 1557 [1207 ‐ 2354]
AUCR0–∞R (µg∙h/l) 8,327 [6,457 – 12,590]
Predicted CRmax1R ‐ Dose 1 (µg/l) 843 [522 – 1,353] 1,105 [736 ‐ 1398]
Predicted TRmax1R ‐ Dose 1 (h) 2.1 [1.6 ‐ 2.9] 2.5 [1.5 ‐ 3.0]
Predicted CRmax2R ‐ Dose 2 (µg/l) 269 [179 ‐ 345]
Predicted TRmax2R ‐ Dose 2 (h) 26.6 [26.0 – 27.4]
The dose‐corrected, first‐dose data indicated that the median AUC for ART was 5%
higher in Group 3 than Group 2. However these and other PK parameters for the two
groups were not significantly different (see Table 6‐5), hence the data were pooled for
total patient group estimates. Overall, mean (±SD) CL/F, VRssR/F and tR½αR for ART were 4.1
± 2.0 l/h/kg, 21 ± 10 l/kg and 2.7 ± 0.3 h respectively. Although the best PK model was
two‐compartment with tR½βR 6.7 ± 0.5 h, this may be a spurious finding due to the
limited concentration‐time data in the present study design and 27% bias in the
bootstrap for VRPR/F.
194
6.2.5 66B66BDiscussion
The present study has provided the first paediatric PK data for NQ and additional ART
disposition data to complement the few available for this age‐group. NQ given in the
form of ART‐NQ fixed combination therapy was promptly absorbed (mean absorption
tR½R 1.0 h) and reached a predicted CRmaxR that was <200 µg/l in all but one child even
after the second dose in Group 3. The mean elimination tR½R of NQ (524 h) was longer
than estimates in early reports (41‐57 h)217 and in the recent Chinese adult volunteer
study (156‐299 h).220 There was some evidence of a modest increase in NQ
bioavailability when administered with a small amount of fat, in contrast to the
substantial food‐associated reduction in NQ bioavailability in Chinese adults.220 The
CL/F (1.1 l/h/kg) and V/F (71 l/kg) for NQ in our study were lower than the results
reported in healthy Chinese adults (7.0 l/h/kg and 2,277 l/kg),220 but there are no other
data presently available for direct comparison. In the case of ART, the mean CL/F and
V/F (4.1 l/h/kg and 21 l/kg, respectively) were comparable to those in most previous
studies (means 6.7 l/h/kg and 27 l/kg, respectively).112, 115, 118, 119, 123, 125, 404, 406
The long elimination tR½R and high V/F of NQ in our children were consistent with most
other quinolines and related drugs in clinical use.387, 413, 414 PK modelling indicated that
a three‐compartment model best described the disposition of NQ in the present study.
This finding is consistent with similar PK studies involving CQ 313, 415‐417 and PQ.216 A
number of studies of quinoline and related antimalarial drugs have shown biphasic
drug concentration‐time profiles that can be analysed using a two‐compartment
model.174, 178, 215, 387, 414, 418‐421 Improved PK study design, including more frequent and
longer duration sampling, as well as lower limits of quantification for the analytical
techniques, may explain why recent studies such as ours reveal more complex
elimination kinetics. Indeed, the relatively short NQ elimination tR½R in the Chinese
volunteer study220 could be explained by a short sampling period (216 h) as well as the
use of non‐compartmental methods. In relation to the latter point, we found an
elimination tR½R of 298 h by non‐compartmental methods in Group 1 patients vs. 547 h
in compartmental population analyses of pooled NQ data.
The effect of fat on NQ bioavailability in the present study needs interpretation in light
of the study design. In the preliminary PK study in Group 1 children, there was no
195
requirement for fasting before or after drug administration. It is likely that these
children consumed some fat around the time ART‐NQ was given even though the dose
was administered with water. Group 3 children, who were required to fast throughout
and were given the dose with water, had a 26% lower relative bioavailability compared
to Group 1 and also Group 2 which was the group in which all children were given ART‐
NQ with milk. This evidence of a modest positive effect of fat on bioavailability
contrasts with the observation that the AUC and tR½R of NQ were approximately 50%
lower after food (60% lipid; 2400 kJ) in healthy Chinese adults,220 suggesting an
increased CL and/or reduced oral bioavailability. Studies with quinolines and related
drugs have shown increased absorption with high‐fat meals215, 375, 402 but a standard
Vietnamese meal (17 g fat; 2000 kJ) had little effect on the PK properties of PQ.173 It is
possible that relatively high fat meals such as that used by Qu et al.220 might interfere
with NQ absorption from the gastrointestinal tract, but our experience is that amounts
of fat given as milk greater than that used in the present study (>8.5 g; >615 kJ) have a
high likelihood of inducing significant nausea in an unwell child with malaria. This
observation and our NQ PK data do not suggest that food‐associated under‐dosing will
be problematic in children. The improved NQ bioavailability after the second
compared with the first dose in Group 3 may relate to clinical improvement reflecting
parasite clearance as has been seen with LUM.141
Fever was independently associated with reduced NQ bioavailability, consistent with
PK studies in other contexts.422, 423 There was an independent association between Hb
and VRCR that might suggest NQ accumulation in RBCs but an assessment of partitioning
was beyond the scope of the present study. As is the case for a range of other drugs
including anti‐infectives,424 co‐administration of milk reduced the rate of NQ
absorption.
Relative to previous studies of ART PK112, 115, 118, 119, 123, 125, 404, 406 and compared with
ART monotherapy, Qu et al.220 reported lower values for CL/F (mean 2.7 l/h/kg) in
adults given ART‐NQ while Sidhu et al.125 found a significantly higher value for this
parameter (14.4 l/h/kg) when ART was given to children with uncomplicated
falciparum malaria. While it is difficult to explain the former observation, an
apparently high CL/F may relate to underestimation of the ART AUC. Almost all
196
previous studies have used non‐compartment analysis or one‐compartment models to
determine the PK parameters for ART. A two‐compartment model was, however, the
best fit for the ART concentration‐time data in the present study, probably reflecting
the fact that our limits of quantification (2.5 µg/l) and detection (1 µg/l) were
considerably lower than previous studies (4‐20 µg/l).112, 123, 125, 404, 406 A prolonged
elimination phase may have been undetected in past studies, thus truncating the AUC.
Our assay sensitivity led, in part, to an unexpected limitation of the present study,
namely a lack of sampling >24 h post‐dose. Based on the established PK properties of
ART, we anticipated that ART plasma concentrations would not be detectable beyond
24 h. If prolonged sampling had been performed, this would have allowed a more
definitive multi‐compartment PK characterization.
In a Vietnamese study, co‐ingestion of food was reported to be associated with a non‐
significant 20% reduction in ART AUC after oral ART in healthy adults.404 By contrast,
Qu et al.220 reported that the AUC and tR½R of ART were approximately 75% higher after
co‐administration of food with ART‐NQ combination therapy, suggesting an increased
bioavailability and possible reduction in CL. Our data are consistent with the earlier
study of Dien et al.404, as we also found a non‐significant 5% lower AUC for ART after
food (milk) compared with administration with water. There were no significant
differences when dose group was added as a covariate in the population PK model,
further evidence that fat has no clinically meaningful effect on the PK of ART.
Although the importance of developing paediatric formulations of antimalarial drugs
has been emphasized,425 it is unclear how the manufacturer’s paediatric ART‐NQ dose
recommendations have been developed. Using either a WT‐based equation426,
(DoseRCHILDR (mg) = DoseRADULTR (mg) × [WTRCHILDR/ WTRADULTR]0.75), or a body surface area (BSA)
equation426, 427 (DoseRCHILDR (mg) = DoseRADULTR (mg) × [BSARCHILDR/BSARADULTR])where the
regular adult dose of NQ is 400 mg, adult WT is assumed to be 50 kg and adult BSA is
1.73 m2, the adult dose of 8 mg/kg would scale up to ≥10 mg/kg in children. Our initial
mean conservative dose of 6.3 NQ mg/kg in Group 1 as part of ART‐NQ was associated
with a relatively high late treatment failure rate.408 The regimens used in Groups 2 and
3 (means 9.0 and 9.5 mg/kg NQ per dose) were based on manufacturer’s
197
recommendations of 6.5‐9.5 mg/kg for children up to 40 kg409, 428 which is still short of
the allometrically scaled dose of ≥10 mg/kg.
Efficacy against asexual parasite forms over 42 days of follow‐up in Groups 2 and 3 was
100% but prolonged gametocyte carriage was observed in some patients.408 This latter
observation, concerns regarding the emergence of parasite resistance against
artemisinin compounds in endemic areas with a history of sub‐therapeutic drug use,429
the implication that higher individual paediatric doses than recommended by the
manufacturer can be used, and the safety of the two‐dose ART‐NQ regimen employed
in Group 3,408 are all considerations that make an argument for a three‐day ART‐NQ
regimen in line with WHO recommendations for all ACT.430 We have used the final NQ
model to simulate CRmaxR after three ART‐NQ doses given with milk to 1,000 children
with similar characteristics to the present subjects. The median [95% PI] after three
consecutive daily doses were 36 [19‐76], 69 [44‐128] and 89 [61‐152] µg/l,
respectively, with an absolute range up to 350 µg/l after the third simulated dose. A
predicted CRmaxR >300 µg/l occurred in a small minority of subjects in the simulation. The
Group 3 child with a predicted CRmaxR of 270 µg/l had an uncomplicated clinical course in
the present study and a CRmaxR of 245 µg/l in an adult was not reported to be associated
with toxicity,217 but careful tolerability and safety monitoring would need to be carried
out if a three dose regimen was implemented.
A further argument for multiple dose ART‐NQ relates to the disposition of the ART
component. The conventional adult dose regimen for orally administered ART of 10‐20
mg/kg on the first day followed by 500 mg daily for 4 days407 has been questioned due
to the auto‐induction of ART metabolism that, as in the present study, progressively
and substantially reduces the bioavailability of subsequent doses but does not increase
CL.123 The 15‐24 mg/kg dose of ART used in the present study could, therefore, be
appropriate part of a three‐day ART‐NQ regimen based on the single dose now
recommended by the manufacturer.
The present study had limitations, in part because of the present paucity of PK and
other data relating to ART‐NQ (especially when Group 1 was recruited) but also the
context of a paediatric study in the rural tropics. The sampling schedule could have
198
included more time points after the second dose in Group 3 but relatively robust
estimates for model parameters could still be derived. It was unfortunate that no pure
vivax malaria cases were recruited but the fact that there was only one late P. vivax
infection in Groups 2 and 3 suggests that the long NQ tR½R helps prevent the emergence
of this infection that is seen after other therapies for falciparum malaria in this area
including AL.21
In conclusion, when normalized by WT, the PK parameters for ART in children are
comparable to most previous studies in adults, but CL/F was higher than recently
reported data when ART‐NQ was co‐administered to healthy adults.220 By contrast,
CL/F and V/F for NQ was lower in the present study and the terminal elimination tR½R
was longer at a mean of 21.8 days. Although the predicted bioavailability of the first
dose of NQ was lower in a fasted state, this is unlikely to translate into clinical
meaningful effects. The present PK characterization, as well as associated tolerability,
safety and preliminary efficacy data,408 may justify using the currently recommended
single dose of ART‐NQ for three days in children with uncomplicated malaria.
199
6.2.6 67B67BAcknowledgements
We thank the children and their parents/guardians for their participation. We are also
most grateful to Sister Valsi Kurian and the staff of Alexishafen Health Centre for their
kind co‐operation during the study and to Dr Michele Senn and staff of the Papua New
Guinea Institute of Medical Research for clinical and logistic assistance. Valuable
technical support was provided by Mr Michael Boddy and Mr John Hess, School of
Pharmacy, Curtin University. The study was funded by the National Health and Medical
Research Council (NHMRC) of Australia (grant 634343). STL was the recipient of a
Cranmore Undergraduate Scholarship through the Faculty of Medicine, Dentistry and
Health Science, University of Western Australia, and TMED is supported by an NHMRC
Practitioner Fellowship.
6.2.7 68B68BConflictofintereststatement
FWH has received research funding from Kunming Pharmaceuticals, the manufacturers
of ARCO.
200
201
7 6B6BGeneralDiscussionMalaria continues to be a major global health concern. In PNG there is high
transmission of malaria in coastal areas that puts pregnant women, infants and
children at the highest risk. New pharmaceutical strategies are being employed that
aim to prevent malaria in pregnancy and infancy and treat acute malaria effectively in
childhood. These populations are also those for which the pharmacology of these
treatments is not well characterised. Therefore, it was the general aim of the thesis to
add to the available literature regarding the pharmacology of selected important
antimalarials in pregnancy, infancy and childhood.
202
7.1 21B21BSignificanceoffindings
The aims of this thesis as set out in the outline (section 1.4) were achieved, with five
successful small scale studies performed, interpreted, presented and, in some cases,
used to guide health policy and further investigations. The PK analysis in all studies
used a population approach based on the computer program NONMEM.
7.1.1 69B69BPreventionofmalariainpregnancy
The study presented in Chapter 2 was able to adequately characterise the PK of AZI
when given with CQ or SP in pregnant and non‐pregnant women using a three
compartment model with a sequential zero then first order absorption. When tested
as a categorical covariate, pregnancy was found only to influence the VRCR which was
almost doubled in the pregnant state. This resulted in pregnant women having lower
peak concentrations, but they had similar overall exposure (AUCR0–∞R) and almost
parallel plasma concentration‐time curves after the first few days. Additionally, there
was no difference in AZI disposition between the groups that were co administered CQ
or SP. Therefore, there was no evidence for a dose adjustment of AZI in pregnancy or
when AZI was given with CQ and SP.
The most common side‐effects encountered in this study were nausea and vomiting.
This was not surprising given prior experience with AZI,239 and is a potential barrier to
its implementation in malaria where higher doses are likely required for sufficient
efficacy. The preliminary efficacy data for both groups in this study, with no woman
experiencing parasitaemia in the 42 days after drug administration, are promising,
although by no means a true measure of the efficacy of these combinations. It was also
noted that the 96h concentrations of AZI correlated well with the AUCR0–∞R and could
therefore be used as a surrogate marker of exposure in efficacy trials where extensive
sampling is not feasible.
The results of this study have been used in determining the dose regimen for a large
scale IPTp trial of AZI with SP.431 Given the number of participants experiencing nausea
with 2 g as a single dose in this study, 1 g of AZI is given twice daily for two day in this
ongoing IPTp trial.
203
7.1.2 70B70BPreventionofmalariaininfancy
Chapter 3 contained the results of a study of conventional and double dose SP in
infants. The population PK of PYR, SDX and NSX, a metabolite of SDX, that incorporated
maturational processes were successfully characterised using a sparse sampling
design. A two compartment model with first‐order absorption for PYR was obtained
with an estimated maturation half‐time of 318 days and a Hill coefficient of 7.39. For
SDX, a one compartment model with first‐order absorption and a maturation half‐time
of 271 days with a Hill coefficient of 4.07 was obtained. When the NSX data were
added, only one additional compartment was required to characterize its disposition.
Overall, the AUCR0‐∞R of both SDX and PYR was significantly higher in the double‐dose
group despite a 32% reduction in the relative bioavailability of SDX when the dose was
doubled, possibly due to saturation of absorption.
The double dose was found to be well tolerated and safe in these infants with no
significant symptomatology, as reported by the parent, and no changes in measured
biochemical markers over time. Preliminary efficacy data demonstrated that fewer
children in the double dose group have symptomatic malaria during follow up.
Although double dose of SP resulted in higher exposure to the drugs and was safe and
well tolerated, the study found that the AUCR0‐∞R for both drugs in the conventional
dosing group was higher than previously reported in children and similar to that
reported in adults from other areas. This may be explained, in part, by methodological
differences between this and previous studies, as the AUCR0‐∞R for PYR in the
conventional dose group was approximately half that reported for non‐pregnant
women in the same area.432
Regardless, the findings of this study will assist with the interpretation of an ongoing
IPTi study performed in PNG (results forthcoming) and provide a basis for future
investigations of higher doses of SP in infants.
204
7.1.3 71B71BTreatmentofuncomplicatedmalariainchildren
This thesis contained three studies of ACT treatments for the treatment of
uncomplicated malaria in children.
7.1.3.1 132B132BArtemether‐lumefantrine
In Chapter 4, results from a study of the widely used combination AL were presented.
In this study, the PK of the two components with their active metabolites, DBL for LUM
and DHA for ART, were analysed using NONMEM. The simultaneous modelling of LUM
and DBL was achieved using a model with three compartments for LUM, two
compartments for DBL, first‐order absorption and an element of FP metabolism. This
represented the first population PK model of DBL and the first description of its
disposition in children. For the ARM and DHA model, there were a substantial number
of BLQ observations which called for the use of a likelihood‐based method to allow
them to be retained in the analysis. This method was successfully applied to the data
to obtain a model with two compartments for ARM, one compartment for DHA, first‐
order absorption and a dose dependent clearance for ARM.
Exposure of the children to LUM, ARM and DHA was similar to that previously reported
for adults with malaria, although children receive an average 35% higher mg/kg dose.
Given that a higher dose is appropriate in children, as would be expected from the
principle of allometry, an increased dose is suggested for children weighing 12.5‐15 kg
who currently receive a lower mg/kg dose than a 50kg adult. This change is being
reviewed for use in PNG, where the manufacturers recommended dose regimen is
used.
The metabolic ratio of DBL was similar to that reported previously.137, 178 DBL was
found to play a potential role in determining treatment outcome, outlining the need
for its measurement in future efficacy trials to assess the extent of its contribution to
efficacy. Although the model demonstrated a suitable predictive performance for
LUM, this was not true for DBL where the concentrations were higher in children under
5 years of age than would be predicted.
205
The results from this study were taken into consideration when the results of a large
efficacy trial in the same area resulted in the implementation of AL as the first‐line
treatment for falciparum malaria in PNG.
7.1.3.2 133B133BArtemisinin/piperaquinebase
A comparison of two ACT drugs containing PQ was presented in Chapter 5. A PK model
for PQ was established using historical data from children who received DHA/PQ
tetraphosphate and new data from children who received ART/PQ base. This
comprised of three compartments with a transit compartment model to explain the
varied absorption phase. There were no significant PK differences between the two
formulations and the AUCR0‐∞R of PQ was similar between the two groups. Analysis of
the ART concentrations in the ART/PQ base group found a two compartment model
with first‐order absorption and a dose dependent relative bioavailability to be
adequate. The second dose of ART resulted in a 73% reduction in its bioavailability,
and therefore the contribution of the second dose to the total AUCR0‐∞R was much
smaller than that of the first.
The new combination was found to be well tolerated and reasonably effective,
particularly against appearance of P. vivax parasitaemia.
In light of evidence for a higher dose of PQ in children212, 213 and WHO
recommendations, 69 the evaluation of an extension of the currently recommended
two day regimen for ART/PQ base to a three day regimen is supported by the results of
the study.
7.1.3.3 134B134BArtemisinin/naphthoquine
The final original data chapter of this thesis contained a PK evaluation of ART/NQ, an
ACT for which there is limited current information in the literature. NQ concentrations
were adequately described by a three compartment model with first‐order absorption
and a lag time. Fever at the time of administration reduced bioavailability. Hb was also
found to be a significant covariate with a positive relationship with the VRCR.
Bioavailability was also found to be lower for the first dose in the fasted group,
although there was no difference in the dose‐adjusted total AUCR0‐∞R. ART
206
concentrations were also available for two out of the three groups and were described
by a two compartment model with first‐order absorption and a lag time. Once again
there was a reduction in the relative bioavailability of the second dose, similar to the
finding for ART/PQ base. There were considerable differences in the NQ PK results in
this study when compared to those obtained in healthy adults (the only published PK
available for comparison),220 although there were significant methodological
limitations in the latter study. Results for ART PK were similar to those previously
reported in patients with malaria.
These results, combined with those of the safety, tolerability and efficacy (not a part of
this thesis), were used in determining the dose regimen of ART/NQ for an ongoing
large scale efficacy trial in PNG.
207
7.2 22B22BImprovementsandfuturedirections
With the benefit of time, experience and further consideration, a number of potential
improvements to the analysis of the data presented in this thesis have been
considered by the author. These are presented here alongside potential future
avenues of research stemming from the studies presented here.
208
7.2.1 72B72BPreventionofmalariainpregnancy
Improvement: Although the model presented in Chapter 2 was adequate in describing
the concentration‐time data for AZI after the first few hours, there was still some
model misspecification in regards to the absorption phase. A transit compartment
model, resembling that used for PQ in Chapter 5, may have been more appropriate
and more likely to provide a better account of the absorption phase of AZI.
Improvement: Like many other studies of PK in pregnancy, the categorical relationship
of pregnancy status was evaluated within the covariate testing procedure. In fact, as
the pregnant women were heavier and an allometric model was used, it would be
more appropriate to state that the use of allometry adequately described the PK
difference in pregnancy with the exception of VRCR/F. For the VRCR/F, not only could the
relationship have been presented as a percentage, but the effect of the size difference
between the ideal WT of the pregnant women and their actual WT (ie. the additional
WT due to pregnancy) could have been tested as a continuous covariate. This would
have at least provided a more physiologically related description of the effect of
pregnancy on VRCR/F.
Future direction: Although a correlation between the 96 h concentrations and the
AUCR0–∞R was found, providing a reasonable surrogate in studies where it is not feasible
to determine the latter, Bayesian forecasting is potentially a more informative
alternative. This would provide an estimate of all model parameters for an individual
given the value of their covariates that were influential in the developed model. In an
efficacy trial, not only would this be able to provide a more individualised estimate of
AUCR0–∞R but it would enable more complex comparisons to be made, such as time to
fall below a defined concentration. Nevertheless, the results obtained are only as good
as information available, so if only one concentration measurement was available
some days after the dose, predictions relating the absorption phase would not be as
reliable as those for the elimination phase.
209
7.2.1 73B73BPreventionofmalariaininfancy
Improvement: This paper used a sigmoid ERmaxR model to account for the maturation of
hepatic clearance of the drugs, with excellent parameter precision for estimates of
maturation half‐time (RSE of 8% for PYR and SDX) but not for estimates of the Hill
coefficient (RSE of 43% and 52% for PYR and SDX, respectively. Although a larger age
range may have assisted with these estimates, as stated in the paper, an alternative
would be to use the $PRIOR subroutine within NONMEM to help stabilise the
estimates of these parameters.433 Using this method the Hill coefficient from other
studies reporting hepatic maturation would be included in the NONMEM control
stream and the OFV would be penalized for deviating from this prior information.
Future direction: There is no doubt that there may be a role for a higher dose of SP in
infants. Given the preliminary safety, tolerability and efficacy data presented in this
thesis a comparison of a higher dose with a standard dose IPTi would be warranted.
Unfortunately such studies are difficult and expensive to run and no country has
adopted IPTi as policy despite the current evidence in its favour.
210
7.2.2 74B74BTreatmentofuncomplicatedmalariainchildren
Improvement: In all three studies in children, no calculation of sample size was
performed prior to the study. This is particularly important in the PQ and NQ studies in
which comparisons were made between different groups. It may also have assisted in
the determination of the appropriate sample size for the exploration of covariate
relationships in the PK analysis.
Improvement: In the AL study dose dependent changes in the PK of ARM were
described using a model that account for an increased clearance (including metabolic
conversion to DHA) with each subsequent dose. Although a similar model had been
previously reported, no basis for this relationship is provided.142 In fact, the process
may resemble that of ART, which is best explained by a decreased in relative
bioavailability (presumably by FP metabolism). In the case of ARM this would also be
able to account for the increasing concentrations of DHA seen with subsequent doses.
Although the sample schedule was not ideal for the characterization of ARM
disposition, this alternative explanation is plausible and could have been tested.
Improvement: The use of Bayesian forecasting, as described above, would be an
addition to the data provided in the AL study. Similar to the type of prediction based
forecasting from simulated individuals performed in the ART/NQ study to obtain
estimates of CRmaxR, simulated individuals could be used to compare current and
proposed dose regimens. This would be particularly useful for AL as paediatric
formulations which allow for more precise dosing are available. A more complex dose
regimen based on WT could then be suggested to be used with these formulations.
Similar modelling would also assist with developing paediatric dose recommendations
for the other combination studies in this thesis.
Future direction: As indicated for AZI, Bayesian forecasting of real individuals in
efficacy trials could be performed to provide more informative estimates of PK
parameters. This is particularly pertinent for AL and ART/NQ which are the subject of
an ongoing efficacy trial in PNG. Although the children in these trials are younger, 0.5‐5
years old versus 5‐10/12 years, the data could first be externally validated for the
younger individuals. This was done for LUM for children in an earlier efficacy trial,
211
where the developed model showed good predictive performance (see Chapter 4).
Assessment of this method compared to a single concentration (day 7 for example) will
be required.
Future direction: Fever was found to be associated with low bioavailability for NQ,
despite parasitaemia also being tested as a potential covariate in both of these cases.
This suggests an independent role of fever on the absorption of these lipid soluble
drugs. Currently few data regarding the effects of fever on PK exist in the literature,
with no clear mechanism for the changes noted. There is potential for this covariate to
be assessed in future PK evaluations of these and other antimalarial drugs.
Additionally, there is scope to investigate the mechanism by which fever affects
bioavailability.
Future direction: The very rapid change in relative bioavailability of ART seen in the
two studies of its PK is an interesting phenomenon. As it has been previously shown to
effect bioavailability and not clearance, it is unlikely that it is due to an increase in
hepatic metabolism.122 This effect would likely be an affect of the gut wall, which also
contains metabolising enzymes. This hypothesis could be tested with the use of
grapefruit juice, a potent inhibitor of CYP3A4 in gut wall and not in the liver.
The only other study that presents data for the second dose of the ART found apparent
discordant results.118 When given alone for three days, followed by a dose of MQ,
there was only decreased in the bioavailability if the third, and final, dose of ART.118 In
contrast, when MQ was given with the first dose of ART there was a reduction in the
bioavailability of the second and third doses of ART.118 This result along with the
results of those presented in this thesis, suggest that, when co‐administered with
drugs such as NQ, PQ and MQ, the effects of the autoinduction of ART are accelerated.
A study involving the dosing of ART alone and with various other drugs could provide
insights into potential drug‐drug interactions. These suggested studies could be
performed in healthy volunteers.
212
213
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ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, Jan. 2010, p. 360–366 Vol. 54, No. 10066-4804/10/$12.00 doi:10.1128/AAC.00771-09Copyright © 2010, American Society for Microbiology. All Rights Reserved.
Pharmacokinetic Properties of Azithromycin in Pregnancy�
Sam Salman,1 Stephen J. Rogerson,2 Kay Kose,3 Susan Griffin,3 Servina Gomorai,3 Francesca Baiwog,3Josephine Winmai,3 Josin Kandai,3 Harin A. Karunajeewa,1,4 Sean J. O’Halloran,5 Peter Siba,3
Kenneth F. Ilett,1,5 Ivo Mueller,3 and Timothy M. E. Davis1*School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia, Australia1; Faculty of Medicine,
University of Melbourne, Melbourne, Australia2; Papua New Guinea Institute of Medical Research, Madang, Papua New Guinea3;Western Health, Melbourne, Australia4; and Clinical Pharmacology and Toxicology Laboratory,
Path West Laboratory Medicine, Nedlands, Australia5
Received 9 June 2009/Returned for modification 17 September 2009/Accepted 19 October 2009
Azithromycin (AZI) is an azalide antibiotic with antimalarial activity that is considered safe in pregnancy.To assess its pharmacokinetic properties when administered as intermittent preventive treatment in pregnancy(IPTp), two 2-g doses were given 24 h apart to 31 pregnant and 29 age-matched nonpregnant Papua NewGuinean women. All subjects also received single-dose sulfadoxine-pyrimethamine (SP) (1,500 mg or 75 mg)or chloroquine (450-mg base daily for 3 days). Blood samples were taken at 0, 1, 2, 3, 6, 12, 24, 32, 40, 48, and72 h and on days 4, 5, 7, 10, and 14 for AZI assay by ultra-high-performance liquid chromatography-tandemmass spectrometry. The treatments were well tolerated. Using population pharmacokinetic modeling, a three-compartment model with zero-order followed by first-order absorption and no lag time provided the best fit.The areas under the plasma concentration-time curve (AUC0–�) (28.7 and 31.8 mg � h liter�1 for pregnant andnonpregnant subjects, respectively) were consistent with the results of previous studies, but the estimatedterminal elimination half-lives (78 and 77 h, respectively) were generally longer. The only significant relation-ship for a range of potential covariates, including malarial parasitemia, was with pregnancy, which accountedfor an 86% increase in the volume of distribution of the central compartment relative to bioavailability withouta significant change in the AUC0–�. These data suggest that AZI can be combined with compounds with longerhalf-lives, such as SP, in combination IPTp without the need for dose adjustment.
Azithromycin (AZI) is a semisynthetic azalide antibiotic thatis structurally related to erythromycin but has a broader spec-trum of antibacterial activity and a more favorable pharmaco-kinetic profile (13, 22). It is widely used in the treatment ofrespiratory and sexually transmitted infections, including thosein HIV-infected patients (32, 34). AZI also inhibits proteinsynthesis in the plasmodial apicoplast (39, 40) and thus hasactivity against both Plasmodium falciparum and Plasmodiumvivax (5, 12, 16, 27–30, 41). It acts mainly against the progenyof parasites that inherit a nonfunctioning apicoplast after ex-posure, with the result that its antimalarial effect has a slowonset and is relatively weak. Therefore, AZI is best used incombination with other antimalarial compounds as both treat-ment (20, 27, 29) and chemoprophylaxis (5, 19), with likelyadditive or synergistic effects (28, 30, 31).
Malaria in pregnancy can result in adverse outcomes forboth mother and fetus (14). Intermittent preventive treatmentin pregnancy (IPTp) aims to reduce the burden of malaria byadministering treatment doses of antimalarial drugs at prede-termined intervals as part of routine antenatal care in areas ofendemicity (44). Because AZI is considered safe in pregnancyand could have activity against other clinically significantpathogens (8, 38), it has been suggested as a candidate forIPTp. Although the pharmacokinetics of AZI have been inves-
tigated (2, 6, 7, 11, 13, 23–26, 35–37, 45), only one studyincluded pregnant women (36), and most focused on its anti-bacterial properties. In addition, AZI is likely to be partneredwith conventional antimalarial drugs if given as IPTp, andthere is evidence that such combinations are safe and welltolerated in studies with chloroquine (CQ) in healthy volun-teers (11) and with sulfadoxine-pyrimethamine (SP) in preg-nant women (20). Although there does not appear to be aclinically significant pharmacokinetic interaction with CQ (11),AZI interactions with other conventional IPTp treatments areunknown. Therefore, we investigated the pharmacokineticproperties of AZI in combination with CQ or SP in pregnantand nonpregnant women from an area of Papua New Guinea(PNG) with intense transmission of both P. falciparum and P.vivax malaria.
MATERIALS AND METHODS
Study site, sample, and approvals. The present study was conducted at Alex-ishafen Health Centre, Madang Province, on the north coast of PNG. The preg-nant women were recruited at their first antenatal clinic visit, and the age-matched nonpregnant volunteers were from the same communities as thepregnant participants. Women were eligible if (i) they had not taken any of thestudy drugs in the previous 28 days, (ii) they had no history of significant allergyto any study drug, (iii) there was no significant comorbidity or clinical evidenceof severe malaria, and (v) follow-up was possible for the duration of the study.The study was approved by the Medical Research Advisory Committee of PNGand the Human Ethics Research Committee at the University of Western Aus-tralia. Written informed consent was obtained from all participants.
Clinical procedures. A detailed assessment was performed prior to drug ad-ministration, including a side effects questionnaire, point-of-care hemoglobinand blood glucose (HemoCue, Angelholm, Sweden), thick and thin blood films,and (for pregnant participants) estimation of gestational age by fundal height. A3-ml blood sample was taken for subsequent antimalarial drug assay. All women
* Corresponding author. Mailing address: Department of Medicine,Fremantle Hospital, P.O. Box 480, Fremantle 6959, Western Australia,Australia. Phone: (618) 9431 3229. Fax: (618) 9431 2977. E-mail:[email protected].
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received 2 g AZI (Zithromax; Pfizer, New York, NY) both at enrolment and 24 hlater. Subjects were also randomized to receive single-dose SP (1,500 mg or 75mg; Fansidar; Roche, Basel, Switzerland) at enrolment (AZI-SP arm) or CQ(Chloroquin; Astra, Sydney, Australia) (450 mg base daily for 3 days; AZI-CQarm) in accordance with regimens recommended for PNG (15). The adminis-tration of all doses was directly observed. The dosing schedule for AZI waschosen as the simplest regimen that would be likely to ensure effective drugconcentrations during the first 4 days of treatment (40).
Following the first dose of AZI (day 0), additional blood samples were takenat 1, 2, 3, 6, 12, 24, 32, 40, 48, and 72 h and then on days 4, 5, 7, 10, and 14 fordrug assay. The exact timing of each blood sample was recorded. All sampleswere centrifuged promptly, with red cells and separated plasma stored frozen at�80°C. The side effects questionnaire was readministered at 6 h and then at 1,2, 3, and 7 days. Hemoglobin, erect and supine heart rates and blood pressure,respiratory rate, temperature, and blood slides were taken on days 1, 2, 3, 7, 14,28, and 42, and blood glucose was measured on days 1, 2, and 3. After thecompletion of follow-up, pregnant patients were returned to the usual antenatalcare.
Laboratory methods. Giemsa-stained thick blood smears were examined in-dependently by at least two skilled microscopists who were blinded to pregnancyand treatment status. Each microscopist viewed �100 fields at �1,000 magnifi-cation before a slide was considered negative. Any slide discrepant for positivity/negativity or species identification was referred to a third microscopist.
AZI levels were measured using a validated ultrahigh-performance liquidchromatography-tandem mass spectrometry (UPLC-LCMS-MS) method using adeuterated internal standard. The samples were retained for subsequent SPassay. AZI USP was obtained from APAC Pharmaceutical LLC (Ellicott City,MD) and deuterated AZI from Toronto Research Chemicals (North York,Canada). In brief, following the addition of an internal standard, AZI wasextracted from 5 �l of plasma by protein precipitation. After centrifugation,supernatant (5 �l) was injected onto a 2795/Quattro Premier XE UPLC-ESI-MS/MS (Waters Corp, MA) using a Waters BEH C18 1.7-�m, 2.1- by 100-mmcolumn. Gradient elution was performed using mobile phases A (45/55 [vol/vol],comprising 1 g/liter ammonium bicarbonate in 50/50 [vol/vol] methanol-waterand acetonitrile) and B (50/50 [vol/vol] methanol-acetonitrile) at 0.4 ml/min.Adduct transitions were monitored using positive electrospray ionization withmultiple-reaction monitoring for AZI and d3-AZI and were m/z 749.6 to 591.4and m/z 752.6 to 594.4, respectively. The method was linear to 1,012 ng/ml (r2 �0.9997) with a limit of quantification of 2.5 �g/liter AZI. All inter- and intradaycoefficients of variation were �10%, and the between-subject variability (BSV)was �5% when matrix effects were investigated at three concentrations.
Population pharmacokinetic analysis. Concentration-time data sets were an-alyzed by nonlinear mixed-effect modeling using NONMEM (version 6.2.0; IconDevelopment Solutions, Ellicott City, MD) with an Intel Visual FORTRAN 10.0compiler. Linear mamillary model subroutines within NONMEM (ADVAN4and -12 used with TRANS4 in the PREDPP library), first-order conditionalestimation (FOCE) with �-ε interaction, and the objective function value (OFV)(a NONMEM-calculated global goodness-of-fit indicator equal to �2 log-likeli-hood value of data) were used to construct and compare plausible models.Unless otherwise specified, a difference in the OFV of �6.63 (�2 distributionwith 1 df; P � 0.01) was considered significant. The R-based model-building aidXpose 6.0 (http://www.r-project.org/) was used for graphic model diagnosis (18).Secondary pharmacokinetic parameters, including the volume of distribution atsteady state (VSS � V1 � V2 � … � Vn), area under the curve (AUC0–�), andelimination half lives (t1/2), for the nonpregnant and pregnant groups wereobtained from post hoc Bayesian prediction in NONMEM using the final modelparameters. Macro constants for the three-compartment model were calculatedfrom the modeled parameters using previously published equations (42).
All volume and clearance terms were scaled allometrically using [ � (bodyweight/70)1.0] and [ � (body weight/70)0.75], respectively (3), and were expressedrelative to bioavailability (/F). Two- and three-compartment models were com-pared, and then zero- and first-order absorption models with and without a lagtime were assessed alone and in combination. The BSV was added to parametersfor which it could be estimated reasonably from the available data. Both expo-nential (proportional) and combined (exponential plus additive) error modelswere tested for residual unexplained variability (RUV). In developing the finalmodels, we investigated the influence of the covariates pregnancy, treatmenttype, fundal height, gestational age, malaria status, blood glucose, and hemoglo-bin on model parameters using Xpose and the generalized additive modelingprocedure function, as well as inspection of correlation plots. Covariate relation-ships found in this way were evaluated within the NONMEM model. Inclusion ofthe covariate required a decrease of �3.84 in the OFV (�2 df � 2; P � 0.05) and
a decrease in the BSV. Correlations among BSV terms and weighted-residuals(WRES) plots were used in model evaluation.
A bootstrap procedure using Perl speaks NONMEM (PSN) (http://psn.sourceforge.net) was used to sample individuals from the original data set withreplacement and to generate 1,000 new data sets that were subsequently analyzedusing NONMEM. The resulting parameters were then summarized as medianand 2.5th and 97.5th percentiles (95% empirical confidence interval [CI]) tofacilitate validation of the final model parameter estimates. In addition, a strat-ified visual predictive check (VPC) was also performed using PSN with 1,000replicate data sets simulated from the original. The resulting 80% predictionintervals (PI) for AZI were plotted with the observed data to assess the predic-tive performance of the model.
Statistical analysis. SigmaStat (version 3.10; Systat Software Inc., Chicago, IL)was used for statistical analysis unless otherwise specified. Data are summarizedas mean � standard deviation (SD) or median and interquartile range (IQR) asappropriate. Student’s t test or the Mann-Whitney U test was used for two-sample comparisons. Categorical data were compared using either the Pearsonchi-square or Fisher’s exact test, and multiple means were compared byrepeated-measures analysis of variance (ANOVA). A two-tailed level ofsignificance of 0.05 was used. Drug concentrations at each time point after day2 were compared to the AUC0–� using Pearson correlation.
RESULTS
Patient characteristics. A total of 31 pregnant and 29 non-pregnant women were recruited between October 2007 andMarch 2008. All subjects took two AZI doses, but two pregnantpatients did not receive either CQ or SP. These women wereexcluded from initial analyses but were included subsequentlyif there was no effect of CQ or SP on AZI pharmacokineticproperties in the other subjects. Baseline characteristics ofthe subjects by pregnancy status and treatment allocation areshown in Table 1. The groups were well matched, except that,consistent with normal physiological changes that occur inpregnancy (4, 17), the pregnant subjects were significantlyheavier and had lower hemoglobin than the nonpregnant sub-jects for each treatment group (P � 0.05). Seven of the preg-nant patients were parasitemic at baseline compared with onlyone of the nonpregnant subjects (P � 0.02).
Efficacy, tolerability, and safety. Three of the seven P. fal-ciparum and one of the two P. vivax cases at baseline receivedAZI-SP. There was an uncorrected adequate parasitologicaland clinical response (APCR) of 100% for both treatments. Afurther eight cases (five of whom were pregnant) became slidepositive for P. falciparum and three (two who were pregnant)for P. vivax late in the 42-day follow-up period. All receivedrecommended antimalarial therapy (15). All cases at baselineand during follow-up were asymptomatic.
Both treatments were well tolerated, and no patient re-quired medical attention because of side effects. Table 2 sum-marizes self-reported symptoms in the first week of follow-up,�90% of which were mild (not influencing usual daily activity)and short-lived (�2 days). Six patients reported mild pretreat-ment symptoms (headache, abdominal pain, pruritus, or dizzi-ness), but these resolved subsequently. Posttreatment prurituswas reported only in the AZI-CQ group (P � 0.052). Althoughnot formally assessed, no significant side effects were volun-teered at assessments after day 7. No patient developed hypo-glycemia (blood glucose � 2.5 mmol/liter) or severe anemia(hemoglobin � 5.0 g/dl). Although postural hypotension (�20mm Hg systolic or �10 mm Hg diastolic fall after standing)occurred eight times in seven pregnant (four from the AZI-CQgroup) and seven times in five nonpregnant (all five in theAZI-CQ group) patients, the differences between groups were
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not significant and there were no associated symptoms. Aftercompletion of the study, one of the study participants had astillbirth. A medical review of her case notes by three indepen-dent physicians concluded that it was unlikely to be the resultof the study medication.
Pharmacokinetic modeling. A three-compartment modelhad a lower OFV than a two-compartment model (8,700.058versus 8,185.104; P � 0.001 by �2 test; df � 2) and a morefavorable distribution of WRES over time. Zero-order fol-lowed by first-order absorption without a lag time provided thelowest OFV and best fit for AZI absorption. The fixed modelparameters were DUR (the duration of the zero-order absorp-tion); ka (the first-order absorption rate constant); CL/F (clear-ance from the central compartment); VC/F, VP1/F, and VP2/F(volumes of distribution of the central, first peripheral, andsecond peripheral compartments, respectively); and Q1/F andQ2/F (intercompartment clearances for VP1/F and VP2/F, re-
spectively). The model structure is shown in Fig. 1. BSV couldbe estimated for DUR, CL/F, VC/F, and VP1/F, while a pro-portional-error model was best for RUV. After testing thevarious covariates, only pregnancy on VC/F produced a signif-icant decrease in the OFV (�2 df � 1; P � 0.05) accompaniedby a decrease in the BSV of VC/F from 111.0% to 99.6%.
The results of the parameter estimates and their relativestandard errors (RSE) are summarized in Table 3 and second-ary parameter estimates in Table 4. All drug concentrationsafter day 2 were strongly correlated with the AUC0–� (r � 0.7;P � 0.001), with 96-h levels showing the strongest association(r � 0.78). The bootstrap results (Table 3) demonstrate arobust estimation of both fixed and random parameters withbias � 4% and � 5%, respectively. Goodness-of-fit plots ofobserved versus population and individual predicted concen-trations and WRES versus time are shown in Fig. 2 and 3. TheVPC results, stratified for pregnancy status, are presented inFig. 4 and show reasonable predictive performance of themodel while demonstrating some difficulty in capturing post-absorption plasma concentrations peaks.
DISCUSSION
The present study is the first pharmacokinetic evaluation ofAZI in pregnant and nonpregnant women living in a malaria-endemic area. We found that a three-compartment model witha combined absorption process best described the dispositionof AZI in our subjects. Both two-compartment (23, 26, 37) andthree-compartment (6, 35) models have been found to bestdescribe AZI plasma concentration-time profiles in other con-texts. Our ability to differentiate the triexponential eliminationof AZI may have been facilitated by the relatively long sam-pling duration. This may also explain why our estimated ter-minal elimination half-lives (78 and 77 h for pregnant andnonpregnant participants, respectively) were longer than thosein most previous studies (range, 27 to 79 h) (6, 7, 13, 23, 35, 37).The overall drug exposure (AUC0–�, 28.7 and 31.8 mg � hliter�1 for pregnant and nonpregnant subjects, respectively)
TABLE 1. Baseline characteristics of the study participants by pregnancy status and treatment allocation
Parameter
Valuea
Pregnant Nonpregnant
AZI-CQ (n � 15) AZI-SP (n � 14) AZI-CQ (n � 14) AZI-SP (n � 15)
Age (yr) 26.9 � 4.1 23.9 � 5.1 25.7 � 5.8 27 � 6.5Wt (kg) 53.5 � 7.1b 56.4 � 7.9a 51.4 � 5.4 51.9 � 4.9Height (cm) 154 � 7.4 154 � 7.3 154 � 6.4 154 � 2.8Axillary temp (°C) 36.4 � 0.7 36.5 � 0.6 36.7 � 0.3 36.4 � 0.3P. falciparum parasitemia 3 (20) 3 (21) 1 (7) 0 (0)P. vivax parasitemia 1 (7) 0 (0) 0 (0) 1 (7)Gestational age (wk) 24 �22–27� 21 �19–24�Gravidity 3 �2–5� 2 �1–4� 1 �0–3� 2 �0–3�Parity 2 �1–4� 1 �0–2� 0 �0–3� 1 �0–3�Respiratory rate (/min) 20 � 1 22 � 5 20 � 2 20 � 1Supine pulse rate (/min) 91 � 10 89 � 7 82 � 10 88 � 7Supine MAP (mm Hg)c 78 � 7 81 � 10 79 � 9 82 � 7Hemoglobin (g/dl) 8.5 � 1.6b 8.2 � 1.2b 9.3 � 1.9 10 � 1.3Blood glucose (mmol/liter) 5.9 � 1.6 5.7 � 0.8 6.2 � 1.1 5.6 � 2.7
a Data are mean � SD, median �IQR�, or number (%).b P � 0.05 versus nonpregnant subjects.c Mean arterial pressure, calculated by adding 1/3 of the pulse pressure (systolic minus diastolic pressure) to the diastolic pressure.
TABLE 2. Side effects reported during the first week afterinitiation of treatment
ParameterValuea
AZI-CQ (n � 29) AZI-SP (n � 29)
Fever 2 (7) 1 (3)Chills 2 (7) 0 (0)Headache 6 (21) 4 (14)Nausea 4 (14) 7 (24)Vomiting 2 (7) 4 (14)Diarrhea 2 (7) 2 (7)Abdominal pain 4 (14) 3 (10)Rash 0 (0) 0 (0)Pruritus 5 (17) 0 (0)Anorexia 1 (3) 2 (7)Insomnia 2 (7) 0 (0)Dizziness 3 (10) 1 (3)Bone or joint pain 1 (3) 1 (3)Otherb 5 (17) 1 (3)
a Data are numbers (%) of patients.b Cough (2), blocked ear (1), “heavy head” (1), and numbness of calf muscles
(1) in the AZI-CQ group and cough (1) in the AZI-SP group.
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was within the range expected from dose-scaled results fromprevious studies in other contexts (26.5 to 46.4 mg � h liter�1)(2, 7, 11, 13, 23, 24, 26, 35, 37), suggesting that the bioavail-ability of AZI is not dose dependent.
Both zero-order (23, 37) and first-order (6, 26) absorptionhave been reported previously for AZI, but neither was appro-priate for our data. A combined absorption process in whichthe drug enters the absorption compartment in a zero-ordermanner and then is absorbed according to first-order kineticsprovided the best model in the present study. This is analogousto the twin processes of (i) gastric emptying of the drug into thesmall intestine (the zero-order process) and (ii) absorption inthe small intestine proportional to the amount present (thefirst-order process). Despite this more complex model, AZI
absorption was still not well characterized in our final model.This has been reported previously (37) but is unlikely to besignificant in the treatment of uncomplicated malaria, whereexposure of the parasite to therapeutic drug concentrationsover several life cycles is more important than that immedi-ately after drug administration.
Plasma AZI concentrations appeared to differ betweenpregnant and nonpregnant women only in the first 48 h afterthe first dose. This was confirmed by the population pharma-cokinetic modeling, in which pregnancy, the only significantcovariate relationship, accounted for an 86% increase in VC/F.Despite significant differences in the secondary parametersVC/F, VP2/F, VSS/F, and t1/2� (first-distribution half-life) be-tween pregnant and nonpregnant subjects, no difference was
FIG. 1. Structural model used in the final pharmacokinetic analysis of plasma azithromycin concentrations in the central compartment versustime. GUT, gastrointestinal tract.
TABLE 3. Model building, final parameter estimates, and bootstrap results from the AZI population pharmacokinetic modeling
Parametera
Value
Base model Final covariatemodel
Bootstrap (n � 1,000)(median �95% CI�)
OFV 7,999.870 7,993.646 7,974.699 �7,756.673–8,201.238�
Pharmacokinetic (estimate �% RSE�)DUR (h) 1.66 �10.4� 1.55 �3.3� 1.56 �1.21–2.01�ka (h�1) 0.513 �3.2� 0.525 �14.8� 0.524 �0.451–0.623�VC/F (liters) 504 �13.9� 384 �17.6� 371 �235–554�Pregnancy on VC/F (liters) 330 �69.4� 318 �48–604�CL/F (liters h�1) 158 �3.9� 158 �6.7� 158 �145–171�VP1/F (liters) 4,080 �8.6� 4,080 �12.5� 4,045 �3,402–4,870�Q1/F (liters h�1) 327 �5.7� 325 �12.7� 326 �288–368�VP2/F (liters) 5,070 �5.6� 5,040 �7.3� 5,070 �4,262–5,730�Q2/F (liters h�1) 67.2 �11.5� 66.4 �12.4� 67.5 �48.5–84.0�
Random (CV % �% RSE�)BSV Vc/F 111.4 �20.9� 99.6 �35.5� 99.0 �72.6–127.6�BSV CL/F 28.3 �24.1� 28.3 �33.1� 27.9 �21.6–34.5�BSV VP1/F 35.8 �27.0� 35.6 �27.2� 34.8 �25.7–45.2�BSV DUR 73.0 �21.4� 76.9 �22� 75.5 �55.4–95.6�RUVProportional error (CV % �% RSE�) 31.3 �9.5� 31.2 �15.1� 30.9 �28.1–33.8�
a CV, coefficient of variation.
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seen in t1/2� (terminal elimination half-life) or AUC0–�. Thissuggests that the drug elimination and overall exposures weresimilar in the two groups. A much shorter AZI half-life (12 h)than in the present study was reported previously in pregnant
women (36), but the study employed a shorter sampling dura-tion (168 versus 336 h) and included pregnant women at ornear term, and the analysis was constrained by relatively sparsesampling.
Because of the need for AZI to be combined with othertherapies (12, 41), we included conventional antimalarial drugscurrently recommended as part of IPTp in PNG and othercountries (10, 20). There were no significant differences in thedisposition of AZI between the AZI-CQ and AZI-SP groups,consistent with a study of the interaction of CQ and AZI inhealthy volunteers (11). We conclude that AZI dose modifi-cation is unnecessary in these combinations. In addition, thelack of an effect of malaria status as a covariate on AZI dis-position suggests that, unlike drugs such as quinine (21), thedose may not have to be adjusted when parasitemia is present.
The most common side effects of AZI, especially with higherdoses, are nausea and vomiting. These symptoms are thoughtto be related to the effect of AZI on the motilin receptor in theupper gastrointestinal tract (33). However, with the exceptionof pruritus, which tended to be associated with AZI-CQ ther-apy, consistent with known CQ effects (1), there were no dif-ferences in the incidences of side effects between the twotreatment groups, and most reported adverse effects were mild.
TABLE 4. Secondary pharmacokinetic parameters derived from post hoc Bayesian estimates for pregnant and nonpregnant study participants(median �IQR�)
ParameterValue
Pregnant (n � 31) Nonpregnant (n � 29) P value
DUR (h) 1.65 �0.94–2.34� 1.75 �1.02–2.38� NSa
ka (h�1) 0.525 �0.525–0.525� 0.525 �0.525–0.525� NSVC/F (liters) 647 �422–995� 249 �157–363� �0.001VP1/F (liters) 3,620 �2,747–3,951� 2,909 �2,296–3,586� NSVP2/F (liters) 3,888 �3,708–4,104� 3,672 �3,456–3,888� 0.034VSS/F (liters) 8,355 �7,460–8,973� 6,875 �6,115–7,526� 0.002it1/2�
b (h) 0.88 �0.57–1.36� 0.39 �0.24–0.56� �0.001t1/2�
b (h) 20.7 �18.3–22.8� 18.8 �15.3–21� NSt1/2�
b (h) 78.2 �74–82.5� 77.1 �71.5–84.5� NSAUC0–� (�g h liter�1) 28,713 �25,913–32,942� 31,781 �28,736–38,012� NS
a NS, not significant.b t1/2�, t1/2�, and t1/2� are the first-distribution, second-distribution, and terminal elimination half-lives respectively.
FIG. 2. Observed versus model predicted concentrations (A) andindividual predicted concentrations (B) for AZI. The solid gray linesare the lines of identity, while the dashed black lines are the linearregression lines of best fit.
FIG. 3. Weighted residuals versus time after dose (log scale) plotfor AZI.
364 SALMAN ET AL. ANTIMICROB. AGENTS CHEMOTHER.
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The AZI dose regimen in both combination therapy groups inthe present study (2.0 g daily for 2 days) was associated with aside effect profile similar to that reported previously after asingle 2.0-g dose (35). Use of the sustained-release formulationof AZI should reduce side effects, including nausea and vom-iting (9). However, this formulation has a bioavailability of82.8% relative to conventional AZI, suggesting that a higherdose will be required to achieve the same drug exposure. Aswell as increasing the cost of AZI treatment, this could meanthat side effects are more frequent with higher-dose sustained-release AZI administration.
Although the present study had limited subject numbers, it isencouraging that both regimens achieved a 100% uncorrectedAPCR. The plasma concentrations of AZI required to achievecure are unknown, as no efficacy trials have included thesedata. However, the high correlation between 96-h drug levelsand AUC0–� in our patients suggests that a day 4 plasmaconcentration could be an appropriate surrogate for overallAZI exposure in efficacy trials in which serial blood sampling isproblematic. It is interesting that prolongation of the in vitroexposure of P. falciparum to 96 h results in substantially in-
creased potency, suggesting that either AZI renders second-generation parasites unable to establish a parasitophorous vac-uole upon host cell invasion or the effect on apicoplast proteinsynthesis inhibits successful development of the progeny ofdrug-treated parasites (40).
Given the need for relatively prolonged parasite exposure totherapeutic plasma concentrations, it is unlikely that the ben-efit of “front loading” of AZI used in treating bacterial infec-tions (23, 24) will be relevant in malaria. However, experiencewith AZI as an antimalarial agent is growing. A Cochranereview of its efficacy is currently under way (43), and promisingresults are being seen when it is used with SP in IPTp, such asmight be given at least twice during pregnancy (20). Thepresent study provides a pharmacokinetic foundation for thefurther investigation of AZI as an antimalarial agent in preg-nancy, particularly in combination IPTp. Further data from thepresent study should also determine whether AZI influencesthe disposition of CQ and SP. Although there was a significantincrease in AZI VC/F in pregnant women, there was no signif-icant change in the AUC0-�, and it is therefore likely that nodose adjustments will be required for pregnant women whenAZI is given in combination with CQ or SP.
ACKNOWLEDGMENTS
We are most grateful to Sr. Valsi Kurian and the staff of AlexishafenHealth Centre for their kind cooperation during the study. We alsothank Christine Kalopo and Bernard (Ben) Maamu for clinical and/orlogistic assistance.
The study was funded by the National Health and Medical ResearchCouncil (NHMRC) of Australia (grant 458555) and was supported andendorsed by the MiP consortium, which is funded through a grant fromthe Bill and Melinda Gates Foundation to the Liverpool School ofTropical Medicine. T.M.E.D. is supported by an NHMRC PractitionerFellowship.
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ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, Apr. 2011, p. 1693–1700 Vol. 55, No. 40066-4804/11/$12.00 doi:10.1128/AAC.01075-10Copyright © 2011, American Society for Microbiology. All Rights Reserved.
Pharmacokinetic Properties of Conventional and Double-DoseSulfadoxine-Pyrimethamine Given as Intermittent
Preventive Treatment in Infancy�
Sam Salman,1 Susan Griffin,2 Kay Kose,2 Nolene Pitus,2 Josephine Winmai,2 Brioni Moore,1Peter Siba,2 Kenneth F. Ilett,1,3 Ivo Mueller,2 and Timothy M. E. Davis1*
School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia, Australia1;Papua New Guinea Institute of Medical Research, Madang, Papua New Guinea2; and Clinical Pharmacology and
Toxicology Laboratory, Path West Laboratory Medicine, Nedlands, Australia3
Received 4 August 2010/Returned for modification 28 December 2010/Accepted 21 January 2011
Intermittent preventive treatment in infancy (IPTi) entails routine administration of antimalarial treatmentdoses at specified times in at-risk infants. Sulfadoxine-pyrimethamine (SDX/PYR) is a combination that hasbeen used as first-line IPTi. Because of limited pharmacokinetic data and suggestions that higher milligram/kilogram pediatric doses than recommended should be considered, we assessed SDX/PYR disposition, ran-domized to conventional (25/1.25 mg/kg of body weight) or double (50/2.5 mg/kg) dose, in 70 Papua NewGuinean children aged 2 to 13 months. Blood samples were drawn at baseline, 28 days, and three time pointsrandomly selected for each infant at 4 to 8 h or 2, 5, 7, 14, or 21 days. Plasma SDX, PYR, and N4-acetylsulfadoxine (NSX, the principal metabolite of SDX) were assayed by high-performance liquid chroma-tography (HPLC). Using population modeling incorporating hepatic maturation and cystatin C-based renalfunction, two-compartment models provided best fits for PYR and SDX/NSX plasma concentration profiles.The area under the plasma concentration-time curve from 0 h to infinity (AUC0–�) was greater with the doubledose versus the conventional dose of PYR (4,915 versus 2,844 �g/day/liter) and SDX (2,434 versus 1,460mg/day/liter). There was a 32% reduction in SDX relative bioavailability with the double dose but no evidenceof dose-dependent metabolism. Terminal elimination half-lives (15.6 days for PYR, 9.1 days for SDX) werelonger than previously reported. Both doses were well tolerated without changes in hemoglobin or hepatorenalfunction. Five children in the conventional and three in the double-dose group developed malaria duringfollow-up. These data support the potential use of double-dose SDX/PYR in infancy, but further studies shouldexamine the influence of hepatorenal maturation in very young infants.
Intermittent preventive treatment in infancy (IPTi) is a strat-egy in which infants in areas in which malaria is endemic aregiven treatment doses of antimalarial drugs at specified times,regardless of clinical and parasitologic status. Because of itsavailability, tolerability, and relatively low cost, sulfadoxine-pyrimethamine (SDX/PYR) has been used as a first-line treat-ment in IPTi programs, especially in Africa. A recent review ofsafety and efficacy data from six trials conducted from 1999 to2007 revealed that, despite the emergence of molecular mark-ers of parasite resistance, SDX/PYR IPTi reduced clinical ma-laria and malaria-related hospital admissions by about one-third and reduced anemia in the first year of life by 15% (23).The duration of effective antimalarial prophylaxis after a doseof SDX/PYR is 4 to 6 weeks (9, 17).
There is evidence that the efficacy of SDX/PYR IPTi is dosedependent. When given as a fixed dose (27), efficacy declineswith age as lower doses (milligrams/kilogram of body weight)are taken (9). In addition, studies of older children aged 2 to 5years with falciparum malaria have found higher clearancerates and larger apparent volumes of distribution for both SDX
and PYR than those in adults (11). Consistent with these data,a population pharmacokinetic (PK) study in children with con-genital toxoplasmosis showed that the elimination half-lives forboth drugs were directly related to body weight, with the con-sequence that younger and thus lighter children had morerapid elimination (37). These studies suggest that the peakplasma concentration and area under the plasma concentra-tion-time curve (AUC) will be reduced in younger children andthat currently recommended doses of SDX/PYR of 25 mg/kgand 1.25 mg/kg, respectively, may be inadequate for full effi-cacy. Indeed, there is evidence that higher blood PYR concen-trations enhance the ability of pediatric patients to clear resis-tant Plasmodium falciparum (19).
In view of these data and calls for doubling of the recom-mended treatment dose in children aged 2 to 5 years (11), weassessed the tolerability, safety, and pharmacokinetic proper-ties of SDX/PYR given in recommended and double recom-mended doses to infants living an area of intense malariatransmission in Papua New Guinea (PNG).
MATERIALS AND METHODS
Study site, sample, and approvals. The present study was conducted at Alex-ishafen Health Centre, Madang Province, on the north coast of Papua NewGuinea (PNG). Infants between the ages of 2 and 13 months from the surround-ing area were eligible for recruitment provided that they (i) did not have featuresof severe malaria or significant nonmalarial illness, (ii) had not been treated withSDX or PYR in the previous 4 weeks, (iii) did not have a known allergy to either
* Corresponding author. Mailing address: Department of Medicine,Fremantle Hospital, P.O. Box 480, Fremantle 6959, Western Australia,Australia. Phone: (618) 9431 3229. Fax: (618) 9431 2977. E-mail:[email protected].
� Published ahead of print on 31 January 2011.
1693
SDX or PYR, and (iv) were available for assessment for the duration of follow-up. Written informed consent was obtained from the parents/guardians of allrecruited infants. The study was approved by the Medical Research AdvisoryCommittee of PNG and the Human Ethics Research Committee at the Univer-sity of Western Australia.
Clinical procedures. At enrollment, a clinical assessment was performed thatincluded a standard baseline symptom questionnaire completed by parents/guardians. A 500-�l finger prick capillary blood sample was taken for preparationof blood smears for microscopy, baseline drug assay, biochemical tests, andhemoglobin concentration (HemoCue, Angelholm, Sweden). Subjects were ran-domized to receive either the recommended dose of SDX/PYR (25/1.25 mg/kgFansidar; Roche, Basel, Switzerland) or a double dose (50/2.5 mg/kg). Table 1shows the dose administered based on body weight. All dosing was directlyobserved, with subsequent monitoring and readministration of the dose if theinfant vomited within 30 min. Infants with a positive blood film were also givena 3-day course of amodiaquine according to PNG national treatment guidelines(33). All drugs were crushed and mixed with either water or breast milk beforeadministration by mouth using a syringe.
All infants were reassessed on days 1, 2, 3, 5, 7, 14, 21, and 28. A hemoglobinconcentration was determined on each occasion and a repeat symptom ques-tionnaire administered at each visit up to day 7. Blood films were repeated on day28 and/or when fever or a recent history of a fever was reported. For pharma-cokinetic analysis, four additional 500-�l capillary blood samples were takenfrom each infant. The times for the first three of these were randomly selectedfor each infant from either 4 to 8 h or 2, 5, 7, 14, or 21 days postdose. A finalsample was taken in all cases on day 28. The exact timing of each blood samplewas recorded. All samples were centrifuged promptly, with red cells and sepa-rated plasma stored frozen at �80°C until assay.
Laboratory methods. Giemsa-stained thick blood smears were examined in-dependently by at least two skilled microscopists who were blinded to dosegroup. Each microscopist viewed �100 fields at �1,000 magnification before aslide was considered negative. Any slide discrepant for positivity/negativity oridentification to the species level was referred to a third microscopist.
Cystatin C (CysC) concentrations were measured by particle-enhanced immu-noturbidimetry (PETIA) using the Tina-quant cystatin C kit run on an Elecsys2010 analyzer (Roche, Indianapolis, IN). Sodium, urea, creatinine, albumin,�-glutamyl transferase, and bilirubin were measured using an Integra 800 ana-lyzer (Roche) when sufficient plasma was available.
Sulfadoxine, sulfamethazine, and pyrimethamine were obtained from Sigma-Aldrich (Castle Hill, Australia), and midazolam hydrochloride was obtainedfrom Pfizer (West Ryde, Australia). N4-acetylsulfadoxine (NSX) was synthesizedaccording to the method of Whelpton et al. (39) and found to have a meltingpoint of 230°C and �99.9% purity by high-performance liquid chromatography(HPLC). Acetonitrile was obtained from Merck (Darmstadt, Germany). Allother chemicals were of analytical or HPLC grade.
For PYR, SDX, and NSX, extraction and separation were performed based onpreviously published HPLC-UV methods (26, 37). The internal standards weremidazolam HCl for PYR and sulfamethazine for SDX and NSX. Analytes wereassayed using UV detection at 270 nm. Chemstation software (version 9; AgilentTechnology, Waldbronn, Germany) was used for analysis of chromatograms.Standard curves were linear from 5 to 1,000 �g/liter, 0.1 to 200 mg/liter, and 0.02to 10 mg/liter for PYR, SDX, and NSX, respectively. Intra- and interday relativestandard deviations (RSDs) were �15% for all analytes at all concentrations.The limits of quantification (LOQ) were 2.5 �g/liter, 0.1 mg/liter, and 0.02mg/liter, and the limits of detection (LOD; determined as a signal-to-noise ratioof 5) were 1 �g/liter, 0.05 mg/liter, and 0.01 mg/liter for PYR, SDX, and NSX,respectively.
Population pharmacokinetic analysis. Loge concentration-versus-time datasets for PYR, SDX, and NSX were analyzed by nonlinear mixed effect modelingusing NONMEM (version 6.2.0; ICON Development Solutions, Ellicott City,MD) with an Intel Visual FORTRAN 10.0 compiler. Linear mammillary modelsubroutines within NONMEM (ADVAN2/TRANS2 and ADVAN4/TRANS4),first order conditional estimation (FOCE) with �-ε interaction, and the objective
function value (OFV) were used to construct and compare plausible models.Unless otherwise specified, a difference in OFV of �6.63 (�2 distribution with 1df, P � 0.01) was considered significant. Due to the small number of samples withlow concentrations, those below the LOD were not included in the analysis, whilelevels between the LOD and LOQ were kept at their measured concentrations.
As the subjects were infants with a range of ages, it was important to incorporatematuration of clearance into the model. Therefore, total clearance (CLT) was de-fined as the sum of hepatic clearance (CLH) and renal clearance (CLR), i.e., CLT �
CLH � CLR. The age-adjusted hepatic clearance, CLH, was determined using asigmoid maximum effect (Emax) model (7) as TVCLH � [PMAHillCL/(PMAHillCL �
MATCL50HillCL)], where TVCLH is the population average value for hepatic clear-
ance, PMA is the postmenstrual age (the age of the infant recorded from the lastmenstrual cycle of the mother during pregnancy rather than birth), HillCL is the Hillcoefficient for hepatic clearance, and MATCL50 is the PMA at which CLH is 50% ofthe mature value. When an accurate PMA could not be obtained, it was estimatedfrom the postnatal age (PNA) and average gestation in PNG (3, 15, 21). CLR wasadjusted to a standardized value for an estimated glomerular filtration rate (eGFR)of 120 ml/min/1.76 m2, i.e., TVCLR � (eGFR/120), where CLR is the adjusted renalclearance, TVCLR is the population average value for renal clearance, and theeGFR was determined from the cystatin C concentration (CysC) as 91.62 � (1/CysC1.123) (20).
Allometric scaling using weight (WT) was also used on all volume and clear-ance terms, which were multiplied by (WT/70) and (WT/70)0.75, respectively.One- and two-compartment models with first order absorption without lag timewere assessed for both SDX and PYR. As few data exist to describe the absorp-tion phase of both drugs, the absorption rate constant (ka) was fixed to thepreviously published value for infants (18). Between-subject variability (BSV)was added to parameters for which it could be estimated reasonably from avail-able data. As loge concentration data were used, an additive model (representingproportional error) was used for residual unexplained variability (RUV).
In the development of the final models, we investigated the influence of thecovariates dosing group, relative dose (milligram/kilogram), PMA, malaria sta-tus, concomitant treatment with amodiaquine, and initial hemoglobin concen-tration using the generalized additive modeling procedure within Xpose (http://xpose.sourceforge.net) and by inspection of correlation plots. Covariaterelationships identified by this procedure were evaluated within the NONMEMmodel, and inclusion of the covariate required a significant decrease in OFVaccompanied by a decrease in the BSV of that parameter. Correlations amongBSV terms and weighted residuals (WRES) plots were also used in modelevaluation.
Once a final model for SDX was obtained, the parameter estimates were fixedand an additional compartment was added in order to model NSX concentra-tions. In order to allow identifiability in the model, the percentage conversion ofSDX to NSX was fixed to 60% based on the product information (35). Theelimination of NSX was assumed to be entirely renal (25).The influence of thecovariates was assessed on new model parameters using the method describedabove.
A bootstrap procedure using Perl-speaks-NONMEM (PSN) (http://psn.sourceforge.net) and the resulting parameters were then summarized as medianand 2.5th and 97.5th percentiles (95% empirical confidence interval [CI]) tofacilitate validation of the final model parameter estimates. In addition, stratifiedvisual predictive checks (VPCs) and numerical predictive checks (NPCs) werealso performed using PSN with 1,000 replicate data sets simulated from theoriginal data set. NPCs stratified according to PMA were assessed by comparingthe actual with the expected number of data points within the 20, 40, 60, 80, 90,and 95% prediction intervals (PI). The resulting 80% PI for drug concentrationswere plotted with the observed data to assess the predictive performance of themodel.
Statistical analysis. As previously reported in a study of SDX/PYR pharma-cokinetics in pregnant versus nonpregnant women (26), and using estimates ofcentrality and variance for pharmacokinetic parameters from previous pediatricstudies (11, 19, 32, 37, 40) and an assumed 20% attrition rate, a sample size of35 in each group in the present study would be expected to show a �30%increase in the magnitude of any pharmacokinetic parameter in the double-dosegroup at � � 0.05 and � � 0.1. SPSS 17.0 (SPSS inc. Chicago, IL) was used forall statistical analysis unless otherwise specified. Data are summarized as mean �
standard deviation (SD) or median and interquartile range (IQR) as appropri-ate. Student’s t test or the Mann-Whitney U test was used for two-samplecomparisons. Categorical data were compared using either Pearson chi-squaredor Fisher’s exact test and multiple means by repeated measures analysis ofvariance (ANOVA). A two-tailed level of significance of 0.05 was used.
TABLE 1. Dosing guide for conventional and double-dose groups
Body wtDosage (mg SDX/PYR)
Conventional dose Double dose
3–5.9 kg 1⁄4 tablet (125/6.25) 1⁄2 tablet (250/12.5)6–11.9 kg 1⁄2 tablet (250/12.5) 1 tablet (500/25)
1694 SALMAN ET AL. ANTIMICROB. AGENTS CHEMOTHER.
RESULTS
Patient characteristics. Seventy infants were enrolled be-tween April 2008 and December 2008, with equal numbers ineach dose group. Baseline subject characteristics are summa-rized in Table 2. The double-dose group received a signifi-cantly higher milligram/kilogram dose than the conventionaldose group (P � 0.001) and was taller by a mean of 4.3 cm (P �0.015). The double-dose group was also older (by a mean of 47days) and heavier (by 0.4 kg) than the conventional dose group,but these differences were not statistically significant (P �0.05).
Tolerability, safety, and efficacy. Both doses were well tol-erated. There were no changes in symptoms in either groupcompared to predose profiles, including an absence of derma-tological conditions. There were no significant changes in he-moglobin, or in plasma urea, creatinine, or CysC, over time. Inthe conventional dose group, there was a significant but tran-sient mean fall in plasma albumin of 2 g/liter at day 2 (from 38to 36 g/liter; P � 0.01), but there were no concomitant in-creases in plasma bilirubin or hepatic enzymes in either group.
Five infants with vivax malaria and one infant with a mixedPlasmodium vivax/P. falciparum infection at enrollment re-sponded to treatment. Three other infants in the conventionaldose group and two in the double-dose group were adminis-tered antimalarial drugs during follow-up at an external healthcare facility, and no blood smears were available for review. Noother subjects became symptomatic during the study. Only twoinfants in the conventional dosing group and one in the dou-ble-dosing group who were aparasitemic at entry had a positiveblood slide on day 28 (all for P. vivax). All were asymptomatic,and each was treated according to PNG national treatmentguidelines.
Pharmacokinetic modeling. There were 248, 255, and 247drug concentration measurements available for pharmacoki-netic modeling for PYR, SDX, and NSX, respectively. Therewere four samples with PYR concentrations between the LODand LOQ and a further four with concentrations below theLOD for PYR. In addition, seven samples were of insufficient
volume for measurement of PYR after the SDX/NSX assay.There were no SDX or NSX concentrations below the LOQ,but NSX concentrations could not be determined in eightsamples due to an unidentified interfering peak. For PYR, atwo-compartment model was superior to a one-compartmentmodel with a lower OFV (�87.081 versus �30.030) and aless-biased weighted residuals versus time (WRES) plot. Themodel parameters were ka, CLH/F, CLR/F, central compart-ment volume of distribution (V2/F), peripheral compartmentvolume of distribution (V3/F), intercompartmental clearance(Q/F), HillCL, and MATCL50. BSV was estimable on CLT/F,V2/F, and Q/F. As the correlation between the variability ofV2/F and Q/F was very close to 1, it was subsequently fixed tounity to assist with successful determination of the covariancematrix. None of the covariates tested improved the modelsignificantly; therefore, the final model contained only the ef-fects of PMA and WT anticipated from maturation and allo-metric scaling, respectively.
The final parameter estimates and the results of the boot-strap procedure for PYR are shown in Table 3. All modelparameters had a bias of �11%. Goodness-of-fit plots for PYRare shown in Fig. 1. NPCs of the data showed good predictiveperformance, as did VPC plots of the observed drug concen-trations and their 80% PI (the 10th and 90th percentile bound-aries) stratified by dosing group (Fig. 2A and B). Post hocparameter estimates are shown in Table 4. There was no dif-ference between the two groups for any of these parametersexcept for AUC from 0 h to infinity (AUC0–�), which wassignificantly higher in the double-dose group (4,915 versus2,844 �g/day/liter). Median steady-state volume (VSS) for the
TABLE 2. Baseline characteristics of study participants
Parameter
Result for study group
Conventionaldose (n � 35)
Double dose(n � 35)
Postmenstrual age, days �median (IQR)� 454 (383–513) 501 (428–532)Sex �no. (%) male� 22 (63) 24 (69)Weight (kg) (mean � SD) 6.58 � 1.31 6.98 � 1.1Height (cm) (mean � SD) 61.8 � 6.5 66.1 � 7.8Axillary temp (°C) (mean � SD) 36.5 � 0.6 36.4 � 0.6
No. (%) with parasitemiaa
P. falciparum 1 (3) 0 (0)P. vivax 3 (9) 3 (9)
Respiratory rate (per min) (mean � SD) 40 � 11 42 � 11Supine pulse rate (per min) (mean � SD) 133 � 14 133 � 15Mean upper arm circumference (cm)
(mean � SD)13.2 � 3.5 13.7 � 2.6
Hemoglobin (g/liter) (mean � SD) 9.5 � 1.3 9.5 � 1.2eGFR (ml/min/1.73 m2) (mean � SD) 80 � 20 84 � 16Sulfadoxine dose (mg/kg) (mean � SD)b 35.6 � 5.6 67.1 � 12.6Pyrimethamine dose (mg/kg) (mean � SD)b 1.8 � 0.3 3.4 � 0.6
a One infant had a mixed P. vivax/falciparum infection.b P � 0.001.
TABLE 3. Final population PK parameters andbootstrap results for PYR
Parametera
Value
Final modelBootstrap
(n � 1,000)�median (95% CI)�
OFV �97.384 �111.812 (�170.137 to �62.348)
PK parameters �estimate(% RSE)�
ka (per h) 0.779 FixedVC/F (liters/70 kg) 222 (4) 221 (202–242)VP/F (liters/70 kg) 64.1 (24) 63.0 (41.8–128.5)Q/F (liters/h/70 kg) 0.0735 (19) 0.0788 (0.0486–0.1470)CLR/F (liters/h/70 kg) 0.416 (64) 0.3820 (0.0621–0.9868)CLH/F (liters/h/70 kg) 0.854 (24) 0.878 (0.466–1.220)MATCL50 (days) 318 (8) 326 (286–367)HillCL 7.39 (43) 7.80 (3.53–35.18)
Random parameters�% CV(% RSE)�
BSV VC/F 13.0 (36) 13.6 (3.6–24.7)BSV CLT/F 27.8 (13) 27.0 (18.2–35.0)BSV Q/F 34.1 (32) 36.4 (17.4–53.4)
Correlations betweenBSV pairs
R (VC/F, CLT/F) 0.533 (69) 0.563 (�0.059 to 0.826)R (VC/F, Q/F) 1 FixedR (CLT/F, Q/F) 0.533 (69) 0.563 (�0.059 to 0.826)
Residual unexplainedvariability (RUV)
Proportional error�% CV (% RSE)�
33.6 (23) 32.6 (26.9–37.4)
a % RSE, percent relative standard error.
VOL. 55, 2011 SULFADOXINE-PYRIMETHAMINE PHARMACOKINETICS IN INFANCY 1695
combined study sample was 27.8 liters, and the half-lives at �and � phases (t1/2� and t1/2�, respectively) were 72.7 and 374 h,respectively.
Initial modeling of SDX revealed that a one-compartmentmodel was appropriate, as there was minimal bias in the WRES
plot that was not improved when a two-compartment model wasfitted. The model parameters were ka, CLH/F, CLR/F, volume ofdistribution (V/F), HillCL, and MATCL50. BSV was able to beestimated on CLT/F and V/F. There was a significant relationshipbetween relative dose (in milligrams/kilogram) and relative bio-availability which conformed to a power function; specifically,individual relative bioavailability � 1 � ([individual relativedose]/[average relative dose]effect parameter). The value of thepower effect parameter was �0.56, indicating that, when the doseis doubled, the bioavailability falls by 32.2%. The final parameterestimates and the results of the bootstrap procedure are shown inTable 5. With the exception of CLR, all parameter estimates hadbiases of �13%. The median bootstrap value for CLR was almostdouble the initial estimate (195%), demonstrating the difficulty indelineating the difference between and estimating the hepatic andrenal clearance using this methodology. Goodness-of-fit plots forSDX are shown in Fig. 3. NPCs of the data showed good predic-tive performance, as did VPC plots of the observed drug concen-trations and their 80% PI stratified by dose group in Fig. 4.
An additional compartment was added to the final SDX PKmodel to incorporate the data for NSX. This resulted in threeadditional model parameters: volume of distribution of NSX(VNSX/F), clearance of NSX (CLNSX/F), and percentage oftotal SDX elimination representing conversion of SDX to NSX(%NSX). As these three parameters cannot be estimated si-multaneously, %NSX was fixed to 60% based on publisheddata (35). The estimates of VNSX/F and CLNSX/F are directly
FIG. 1. Goodness-of-fit plots for PYR showing observed versusmodel predicted concentrations (A) and individual predicted concen-trations (B) (both log scale) and conditional weighted residuals versustime (C). For panels A and B, the solid gray line represents the line ofidentity, while the dashed black line represents the linear regressionline of best fit; in panel C, the solid gray line represents the locallyweighted scatterplot smoothing (LOESS) smoothed fit.
FIG. 2. Visual predicted check plots for PYR showing simulated10th (short dashed line), 50th (dotted line), and 90th (solid line)percentile concentrations and observed concentration (log scale) data(gray open circles) versus time (log scale) for conventional dose(A) and double-dose (B) participants.
1696 SALMAN ET AL. ANTIMICROB. AGENTS CHEMOTHER.
related to %NSX; therefore, the value of these parametersshould be interpreted with caution. However, AUC and t1/2 forNSX remain unchanged for different values of %NSX. VNSX/Fand CLNSX/F were not influenced by any of the available co-variates. Final parameter estimates and results of the bootstrapprocedure are shown in Table 5. Bias was �5% for all NSXparameters, and NPCs and VPCs were performed on the NSXdata set and indicated good predictive performance of themodel (data not shown).
There were some significant differences between conven-tional and double-dose groups in the post hoc parameter esti-mates for both SDX and NSX (Table 6). These included ex-pected differences in the AUC0–� for both SDX and NSX, but
also differences in the half-life and clearance for both drugswhich were not revealed by the model covariate building stage.A higher clearance and lower half-life (t1/2) in the double-dosegroup can be attributed to organ maturation, as these infantswere older than those in the conventional dose group. Themedian t1/2 of NSX for the combined study sample was shorterthan that of SDX (8.9 versus 218 h). The percentage of theAUC0–� of NSX compared to that for SDX was the same forboth dose groups (approximately 5%).
Sigmoid Emax curves of hepatic maturity for SDX and PYRby PMA are shown in Fig. 5. They are closely related toMATCL50 values of 318 days and 271 days for PYR and SDX,respectively. Of the 70 infants, 48 (69%) and 38 (54%) had anestimated hepatic clearance that was �90% of adult values forPYR and SDX, respectively.
DISCUSSION
The present study is the first to investigate the pharmacoki-netics of SDX/PYR in infants living in a setting in whichmalaria is endemic and in which IPTi is appropriate. It is alsothe first to investigate the possibility that a higher dose thanconventionally recommended should be given to achieve ther-apeutic plasma concentrations in this age group, as has beenrecommended for children aged 2 to 5 years (11). SDX/PYRwas well tolerated by all infants, and there was no evidence ofhepatorenal or bone marrow toxicity even at the higher dose.The AUC0–� of both SDX and PYR was significantly higher inthe double-dose group. However, there was a 32% reductionin the relative bioavailability of SDX when the dose was dou-bled, possibly due to saturation of absorption. The percentageof NSX to SDX exposure (AUC) was the same in both groups,suggesting that a double dose does not affect the metabolicclearance of SDX. The pharmacokinetic properties of PYRwere not dose dependent in the present study.
The pharmacokinetic parameters for PYR observed in ourchildren are different from those observed in other pediatricstudies (11, 18, 31, 37, 40). We found a longer t1/2� (15.6 versus2.67 to 4.46 days) and a higher conventional dose AUC (2,844versus 1,052 to 2,607 �g/day/liter). This may reflect the factthat most of our children were well. In addition, we employeda relatively long duration of sampling that facilitated identifi-cation of biexponential elimination, a profile reported previ-ously in studies of adults (26, 28, 38) but not children. Whileone pediatric study sampled out to 42 days, the drug could notbe quantified in 40% of the samples (11). Although the mean
TABLE 4. Post hoc Bayesian predicted PK parameters for PYR for PNG infants given conventional and double doses of SDX/PYR
ParameterMedian result (IQR) for study group
P valuea
Conventional dose (n � 35) Double dose (n � 35)
CLT/F (liters/h) 0.183 (0.13–0.21) 0.199 (0.164–0.229) NSVC/F (liters) 20.2 (17.8–24.1) 22.1 (19–25) NSVP/F (liters) 6.18 (5.32–6.81) 6.49 (5.83–7.03) NSVSS/F (liters) 25.781 (23.319–30.957) 28.8 (24.9–31.8) NSQ/F (liters/h) 0.0118 (0.0073–0.0165) 0.0135 (0.009–0.0196) NSt1/2� (h) 73.7 (67.1–87.7) 70.7 (62.3–82.3) NSt1/2� (h) 391 (300–565) 361 (272–511) NSAUC0–� PYR (�g/day/liter) 2,844 (2,486–3,571) 4,915 (4,311–5,681) �0.001
a Mann-Whitney test. NS, nonsignificant (P � 0.05).
TABLE 5. Final population PK parameters and bootstrap resultsfor SDX and NSXa
Parameter
Value
Final model Bootstrap (n � 1,000)�median (95% CI)�
OFV �521.177 �529.222 (�647.701 to �428.084)
PK parameters �estimate(% RSE)�
ka (per h) 1.23 FixedV/F (liters/70 kg) 24.2 (4) 24.2 (22.5–26.1)CLR/F (liters/h/70 kg) 0.0046 (113) 0.0086 (0.0005–0.0267)CLH/F (liters/h/70 kg) 0.0458 (16) 0.0427 (0.0290–0.0640)MATCL50 (days) 271 (8) 286 (248–360)HillCL 4.07 (52) 4.61 (1.56–15.54)Relative dose on relative
bioavailability (power)�0.56 (14) �0.54 (�0.71 to �0.38)
% NSX (%) 60 FixedVNSX/F (liters/70 kg) 11.7 (10.7) 11.7 (9.4–14.4)CLNSX/F (liters/h/70 kg) 0.758 (5) 0.756 (0.690–0.838)
Random parameters�CV% (% RSE)�
BSV V/F 23.0 (11) 22.2 (17.1–26.5)BSV CLT/F 23.8 (11) 23.4 (17.9–28.3)BSV VNSX/F 42.8 (19) 41.7 (21.0–56.3)BSV CLNSX/F 36.2 (26) 35.6 (25.5–45.1)
Correlations betweenBSV pairs
R (V/F, CLT/F) 0.644 (26) 0.653 (0.439–0.814)R (VNSX/F, CLNSX/F) 0.218 (126) 0.226 (�0.474 to 0.729)
Residual unexplainedvariability�CV% (% RSE)�
Proportional error, SDX 16.5 (11) 16.4 (12.9–20.1)Proportional error, NSX 37.1 (9) 37.0 (30.2–43.1)
a Parameters for NSX modeling obtained after fixing model parameters forSDX are highlighted in bold. % RSE, percent relative standard error.
VOL. 55, 2011 SULFADOXINE-PYRIMETHAMINE PHARMACOKINETICS IN INFANCY 1697
conventional dose PYR AUC in the present study was in therange of previously reported values in adults (1,602 to 3,166�g/day/liter) (11, 16, 22, 28), the latter data may have beenunderestimates because of truncated sampling and/or use of arelatively insensitive assay. In a study of nonpregnant PNGwomen using a sampling profile, assay, and pharmacokinetic
modeling techniques that were similar to those of the presentstudy (26), the mean conventional dose PYR AUC (4,419�g/day/liter) was similar to that in the present double-dosegroup. Together with the available tolerability and safety datafrom the present study, these considerations suggest that dou-ble-dose PYR is appropriate as part of SDX/PYR IPTi.
We found that SDX also had a longer mean elimination t1/2
(9.1 versus 4.1 to 8.6 days) and a higher conventional dosemean AUC (1,460 versus 460 to 932 mg/day/liter) than those ofchildren in other studies (11, 19, 32, 37, 40). However, themean AUC was within the range found in adults (508 to 2,757mg/day/liter) (11, 16, 22, 28), including nonpregnant women(1,386 mg/day/liter) from the same location as the presentstudy (26). Although the difference in AUC compared to otherpediatric populations may be explained, as with PYR, by ourability to detect drug concentrations for longer time postdosethan in previous studies as well as by the relative health of oursubjects, only a few studies have included infants aged �1 year,and these formed a minority of the patients recruited. As oursample includes only children �13 months of age, a limitedmaturation of elimination processes is likely to play a role inthe longer t1/2 and higher AUC observed for both drugs evenin the conventional dose group. Indeed, we found evidence ofa slower maturation of these processes for SDX than PYR.
In the present study, we used plasma CysC rather thancreatinine to estimate GFR. The conventional Schwartz crea-
FIG. 3. Goodness-of-fit plots for SDX showing observed versusmodel predicted concentrations (A) and individual predicted concen-trations (B) (both log scale) and conditional weighted residuals versustime (C). For panels A and B, the solid gray line represents the line ofidentity, while the dashed black line represents the linear regressionline of best fit; in panel C, the solid gray line represents the LOESSsmoothed fit.
FIG. 4. Visual predicted check plots for SDX showing simulated10th (short dashed line), 50th (dotted line), and 90th (solid line)percentile concentrations and observed concentration (log scale) data(gray open circles) versus time (log scale) for conventional dose(A) and double-dose (B) participants.
1698 SALMAN ET AL. ANTIMICROB. AGENTS CHEMOTHER.
tinine-based formula relies upon estimates of body composi-tion (36), whereas CysC-based formulae do not (5), making theestimates more robust. We used the formula derived by Fillerand Lepage (20), as it was derived from a large pediatricsample, and the same PETIA CysC assay used in the presentstudy. CysC concentrations generated by other assays such asparticle-enhanced immunonephelometry may differ from thosefrom PETIA (5). The Filler and Lepage formula is comparableto others based on CysC derived from children (13, 14, 24, 41).
Since hepatic maturation would still be occurring within theage range of our subjects, it was appropriate to include thisphenomenon in our model (1, 8, 12). We used a sigmoid Emax
approach as this has been used previously with a number ofother drugs (2, 4, 6, 8, 34) and our estimates of MATCL50,namely, 315 and 271 days for PYR and SDX, respectively, fellin the range reported in these studies (270 to 380 days). Theestimate of the Hill coefficient for SDX was also consistent(4.07 versus 2.78 to 4.6), but the Hill coefficient for PYR washigher than that previously reported (7.39). Although ourstudy age range captured the process of maturation, most ofour infants had clearances that were �90% of adult values andvery few were �50% (Fig. 5). This limits our ability to char-acterize coefficients of maturation which are likely to be inap-
propriate outside this age range. For example, adult estimatesof t1/2 for SDX (333 h) and PYR (t1/2�, 113 h; t1/2�, 647 h)based on the modeling presented here are higher than thosepreviously reported (11, 16, 22, 26, 28, 38). Future studies ofthis type should include a larger range of ages so that thematuration process from birth to adult activity levels can bedetermined more accurately.
Other studies have provided data relevant to the question ofwhether a higher SDX/PYR dose should be given to infants. Apharmacokinetic evaluation of SDX in children aged 6 months to5 years with malaria found that those aged �24 months had alower AUC0-336 h than their older counterparts (12,500 versus16,900 mg/h/liter) (32). However, all children �24 months of agereceived half the dose of older children regardless of body weightand no average dose by body weight was reported, thus compli-cating interpretation of the data. In a similar study (11), an age-stratified noncompartmental analysis of AUC0–� showed that 1-to 2-year-olds had sufficient drug exposure while children aged 2to 5 years required a double dose. The study had only 11 childrenwithin the 1- to 2-year-old age range, and because only wholetablets were given, the mean dose in this group was almost twicethat of �12-year-olds (50/2.5 versus 27.3/1.36 mg/kg). In a popu-lation-based pharmacokinetic analysis of SDX/PYR in childrenwith congenital toxoplasmosis aged 1 week to 14 years (37), light-er-weight children had a shorter t1/2 and therefore a lower drugexposure. This conclusion was based on the use of allometry, sinceage-based maturation contributed little to the model, perhapsbecause of the small numbers in the younger age groups. Inter-preted within their limitations, these various studies also provideevidence that higher milligram/kilogram SDX/PYR doses are re-quired in younger children, including those �1 year of age.
Relatively recent data from the study area indicate that amo-diaquine-SDX/PYR treatment (until recently the recommendedfirst-line antimalarial therapy for young PNG children) is associ-ated with close to a 90% 28-day adequate clinical and parasito-logic response for both falciparum (PCR-corrected) and vivaxmalaria (29). This is a suboptimal response but still suggests thateither conventional or double-dose SDX/PYR treatment in thepresent study is likely to have contributed to the relatively smallnumber of infections detected during follow-up. Although thepresent study was not designed to assess relative efficacy, espe-cially since interpretation of emergent vivax infections remainsproblematic (10) and given that only one dose was administeredrather than the several scheduled during IPTi, fewer childrenwere treated for symptomatic malaria during follow-up or were
TABLE 6. Post hoc Bayesian predicted PK parameters for SDX and NSX in PNG infants given conventional and double dosing of SDX/PYR
ParameterResult �median (IQR)� for study group
P valuea
Conventional dosing (n � 35) Double dosing (n � 35)
CLT,SDX/F (liters/h) 0.0068 (0.0057–0.0087) 0.0072 (0.0068–0.0105) 0.032VSDX/F (liters) 2.20 (1.95–2.53) 2.23 (1.97–2.64) NSt1/2 SDX (h) 232 (203–252) 207 (179–232) 0.006AUC0–� SDX (mg/day/liters) 1,460 (1,167–1,707) 2,434 (1,881–2,987) �0.001CLNSX/F (liters/h) 0.081 (0.060–0.094) 0.101 (0.081–0.116) 0.012VNSX/F (liters) 1.14 (1.00–1.28) 1.17 (0.959–1.30) NSt1/2 NSX (h) 10.3 (7.86–12.2) 8.69 (6.84–10.6) 0.027AUC0–� NSX (mg/day/liter) 1,796 (1,397–2,154) 2,890 (2,482–3,609) �0.001AUC0–� NSX/AUC0–� SDX (%) 5.0 (4.3–6.5) 5.0 (4.3–6.0) NS
a Mann-Whitney test. NS, nonsignificant (P � 0.05).
FIG. 5. Maturation as a fraction of adult clearance for PYR(dashed line) and SDX (solid line) predicted from the PK modelplotted against PMA. A box plot of the PMA in the recruited subjectsis included to show its distribution in relation to maturation of clear-ance. d, days.
VOL. 55, 2011 SULFADOXINE-PYRIMETHAMINE PHARMACOKINETICS IN INFANCY 1699
slide positive on day 28 in the double-dose group. Indeed, there isevidence from epidemiologic studies utilizing fixed-dose regimens(9, 27) that appropriate milligram/kilogram doses of SDX/PYRshould be used in IPTi programs to ensure adequate levels ofprevention, especially for symptomatic compared to asymptom-atic falciparum malaria (30).
In light of this dose dependency, the fact that no study hasshown �60% protective efficacy during the first year of life (9,23), evidence that higher blood PYR concentrations facilitateparasite clearance in pediatric falciparum malaria (19), and thefact that double-dose SDX/PYR in our subjects was safe, welltolerated, and associated with higher exposure to both drug com-ponents (especially SDX), the present data argue for the potentialuse of double-dose SDX/PYR in infancy. As in recent adult stud-ies of PYR disposition (26), we found that the mean eliminationt1/2 values of PYR and SDX were larger than previously reported,a factor that may contribute to the duration of effective prophy-laxis. Although allometric considerations (shorter half-lives insmaller subjects) may justify higher SDX/PYR dosing in infants,we recommend that consideration must be given to the matura-tion of hepatorenal elimination processes and the possibility thatincreased doses may be inappropriate in very young infants.
ACKNOWLEDGMENTS
We are most grateful to Valsi Kurian and the staff of AlexishafenHealth Centre for their kind cooperation during the study. We alsothank Christine Kalopo and Bernard (“Ben”) Maamu for clinicaland/or logistic assistance. We note with deep regret that Servina Go-morrai, who assisted with patient recruitment and data collection,passed away during the study.
The study was funded by a grant from the IPTi Consortium andutilized facilities developed with support from the National Health andMedical Research Council (NHMRC) of Australia (grant 458555).T.M.E.D. is the recipient of an NHMRC Practitioner Fellowship.
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1700 SALMAN ET AL. ANTIMICROB. AGENTS CHEMOTHER.
ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, Nov. 2011, p. 5306–5313 Vol. 55, No. 110066-4804/11/$12.00 doi:10.1128/AAC.05136-11Copyright © 2011, American Society for Microbiology. All Rights Reserved.
Population Pharmacokinetics of Artemether, Lumefantrine, and TheirRespective Metabolites in Papua New Guinean Children with
Uncomplicated Malaria�
Sam Salman,1 Madhu Page-Sharp,2 Susan Griffin,3 Kaye Kose,3 Peter M. Siba,3Kenneth F. Ilett,1 Ivo Mueller,3† and Timothy M. E. Davis1*
School of Medicine and Pharmacology, University of Western Australia, Fremantle Hospital, Fremantle, Western Australia,Australia1; School of Pharmacy, Curtin University of Technology, Bentley, Australia2; and Papua New Guinea Institute of
Medical Research, Madang, Papua New Guinea3
Received 21 June 2011/Returned for modification 20 July 2011/Accepted 20 August 2011
There are sparse published data relating to the pharmacokinetic properties of artemether, lumefantrine, andtheir active metabolites in children, especially desbutyl-lumefantrine. We studied 13 Papua New Guineanchildren aged 5 to 10 years with uncomplicated malaria who received the six recommended doses of artemether(1.7 mg/kg of body weight) plus lumefantrine (10 mg/kg), given with fat over 3 days. Intensive blood samplingwas carried out over 42 days. Plasma artemether, dihydroartemisinin, lumefantrine, and desbutyl-lumefan-trine were assayed using liquid chromatography-mass spectrometry or high-performance liquid chromatog-raphy. Multicompartmental pharmacokinetic models for a drug plus its metabolite were developed using apopulation approach that included plasma artemether and dihydroartemisinin concentrations below the limitof quantitation. Although artemether bioavailability was variable and its clearance increased by 67.8% witheach dose, the median areas under the plasma concentration-time curve from 0 h to infinity (AUC0–�s) forartemether and dihydroartemisinin (3,063 and 2,839 �g � h/liter, respectively) were similar to those reportedpreviously in adults with malaria. For lumefantrine, the median AUC0–� (459,980 �g � h/liter) was also similarto that in adults with malaria. These data support the higher dose recommended for children weighing 15 to35 kg (35% higher than that for a 50-kg adult) but question the recommendation for a lower dose in childrenweighing 12.5 to 15 kg. The median desbutyl-lumefantrine/lumefantrine ratio in the children in our study was1.13%, within the range reported for adults and higher at later time points because of the longer desbutyl-lumefantrine terminal elimination half-life. A combined desbutyl-lumefantrine and lumefantrine AUC0–�
weighted on in vitro antimalarial activity was inversely associated with recurrent parasitemia, suggesting thatboth the parent drug and the metabolite contribute to the treatment outcome of artemether-lumefantrine.
Artemether (ARM)-lumefantrine (LUM) (AL) is a fixed-dose combination therapy used widely for the treatment ofmalaria (33). ARM is a lipophilic artemisinin derivative that isconverted in vivo to dihydroartemisinin (DHA), an active me-tabolite. Both ARM and DHA have short half-lives (14, 19–21,24, 25, 31) but a rapid effect on parasitemia. LUM is a highlylipophilic drug with a longer half-life (11, 13, 14, 19, 20, 24, 30)which is combined with ARM primarily to prevent late recru-descence. Although the pharmacokinetic (PK) properties ofARM, DHA, and LUM in adults have been well documented(4, 5, 11, 13–15, 19–22, 24, 30), there are scant and inconsistentdata relating to the disposition of desbutyl-lumefantrine(DBL), a potent LUM metabolite (26, 28, 29, 32) that mayinfluence AL’s treatment outcome (32). Reported plasmaDBL-to-LUM concentration ratios after AL dosing in adultsdiffer �10-fold (15, 24), while the pharmacokinetic propertiesof DBL in children are unknown. In addition, although severalstudies have attempted to characterize LUM disposition in
children with malaria (1, 16, 25), methodological issues com-plicate the comparison of child data with adult data. One studyinvolving a limited sampling schedule suggested that AL-treated children with malaria receive an inadequate dose ofLUM relative to that for healthy adults (25), while the otherstudies either used pooled plasma concentrations (1) or used atruncated sampling schedule inadequate to characterize LUMpharmacokinetics (16).
In view of this situation, we have characterized the popula-tion pharmacokinetics of ARM, LUM, and their metabolites inpediatric malaria by using a rich sampling schedule to assesspotential differences in disposition between children and adultsand to add to the limited data on DBL disposition and its rolein AL’s treatment outcome.
MATERIALS AND METHODS
Patients. We recruited children aged 5 to 10 years from Alexishafen HealthCentre, Madang Province, on the north coast of Papua New Guinea. The clinicserves an area where Plasmodium falciparum and Plasmodium vivax are hyper-endemic and Plasmodium ovale and Plasmodium malariae are also transmitted.Children with an axillary temperature of �37.5°C or a history of fever in theprevious 24 h were screened with a Giemsa-stained thick blood film read by anon-site, trained microscopist. Those with a monoinfection of P. falciparum(�1,000 asexual parasites/microliter) or P. vivax, P. ovale, or P. malariae (�250/microliter) were eligible, provided that the child’s parents gave informed con-sent, there were no features of severe malaria (34), they had not taken anyantimalarial drug in the previous 14 days, there was no evidence of another cause
* Corresponding author. Mailing address: University of WesternAustralia, School of Medicine and Pharmacology, Fremantle Hospital,P.O. Box 480, Fremantle, Western Australia 6959, Australia. Phone: 6189431 3229. Fax: 618 9431 2977. E-mail: [email protected].
† Present address: Centre de Recerca de Salut Internacional deBarcelona (CRESIB), Barcelona, Spain.
� Published ahead of print on 29 August 2011.
5306
of fever, and there were no features of malnutrition or other chronic comorbid-ity. The study was approved by the Medical Research Advisory Committee of theDepartment of Health, Papua New Guinea.
Clinical methods. After enrollment, a standardized history was taken and aclinical examination performed. A 3-ml blood sample was taken for blood filmmicroscopy, baseline hemoglobin and blood glucose measurements were taken,and a subsequent drug assay of separated plasma was performed. Urinalysis andaudiometric assessment were performed. Each child was given artemether-lumefantrine (Coartem, Novartis Pharma Ltd., Switzerland) at a dose of 1.7 and10 mg/kg of body weight, respectively, to the nearest tablet. This dose wasrepeated at 8, 24, 36, 48, and 60 h, with the exact time of dosing recorded. Alldoses were given under direct observation with at least 50 ml of cow’s milk(equivalent to 2 g of fat). Further venous blood samples were taken from anindwelling intravenous catheter at 4, 8, 12, 24, 36, 40, 48, 60, 64, 68, and 72 h andthen by venesection on days 4, 5, 7, 14, and 28. All samples were centrifugedpromptly, and red cells and separated plasma were stored frozen at �80°C untilassayed. A detailed clinical assessment including a symptom questionnaire, ablood film, and hemoglobin and blood glucose measurements was repeated ondays 1, 2, 3, and 7, with additional clinical assessment and blood films on days 14,28, and 42.
Laboratory methods. All blood smears taken at baseline and during follow-upwere examined independently by two skilled microscopists in a central labora-tory. Each microscopist viewed 100 fields at �1,000 magnification before a slidewas considered negative. Any slide discrepant for positivity/negativity or specieswas referred to a third microscopist for adjudication.
For drug assays, high-performance liquid chromatography (HPLC)-grade ace-tonitrile (Merck, Kilsyth, Australia), tert-butyl chloride, ethyl acetate, glacialacetic acid, and formic acid (Merck, Darmstadt, Germany), and ammoniumformate (Sigma-Aldrich, Gillingham, United Kingdom) were used. Other sol-vents and chemicals were of analytical grade. Stock solutions (1 �g/liter inmethanol) of ARM (AAPIN Chemicals, Abingdon, United Kingdom), DHA(Sigma, St. Louis, MO), and artemisinin (used as an internal standard; Sigma)were stored and protected from light at �80°C and used to prepare workingdilutions (0.1, 1, and 10 �g/ml). Calibration curves (2 to 200 �g/liter) wereconstructed for DHA and ARM by spiking blank plasma. Quality control (QC)samples were prepared in blank plasma at 10, 20, 50, and 200 �g/liter and alsostored at �80°C prior to use.
ARM and DHA were extracted as previously described (7) but with thefollowing modifications. Briefly, solid-phase extraction (SPE) Bond Elut PHcolumns (Varian Inc., Palo Alto, CA) were preconditioned with 1 ml of methanolfollowed by 1 ml of 1 M acetic acid. Plasma (0.5 ml) was spiked with an internalstandard (artemisinin, 100 �g/liter), loaded onto the SPE column, and drawnthrough with a medium-suction vacuum. The column was then washed twice with1 M acetic acid (1 ml), followed by 20% (vol/vol) methanol in 1 M acetic acid (1ml). The column was dried with a low-suction vacuum for 30 min, and theretained drugs were eluted using 2 ml of tert-butyl chloride–ethyl acetate (80:20[vol/vol]). The eluate was then evaporated under vacuum at 35°C, reconstitutedin 50 �l of the mobile phase, and kept overnight to equilibrate the � and �anomers of DHA (7). Only the � anomer was used for quantification. Theinjection volume was 10 �l.
The liquid chromatography-mass spectrometry (LC-MS) system used was asingle-quad mass spectrometer (Shimadzu, Kyoto, Japan) with electrospray ion-ization (ESI) and atmospheric-pressure chemical ionization (APCI) systems.Assays were performed with 20 mM ammonium formate (pH 5) and acetonitrilein 0.1% formic acid (40:60) at a flow rate of 0.2 ml/min, and chromatographicseparation was undertaken at ambient temperature on a Synergi Fusion-RP C18
column (inside diameter [i.d.], 150 mm by 2.0 mm) coupled with a 5-�m-particleC18 guard column (i.d., 4 mm by 3 mm; Phenomenex, Lane Cove, Australia).Retention times were 4.5, 7.5, and 12.7 min for DHA, artemisinin, and ARM,respectively. Optimized mass spectra were acquired with an interface voltage of4.5 kV, a detector voltage of 1 kV, a heat block temperature of 400°C, and adesolvation gas temperature of 250°C. Nitrogen was used as a nebulizer gas at aflow rate of 1.5 liters/min and as a dry gas at a flow of 10 liters/min. Quantitationwas performed by selected ion monitoring using the dual-ionization sourcemode. The predominant fragmented ions, m/z 221 for ARM and m/z 221 forDHA, were used. For artemisinin, m/z 283 was monitored.
The standard curves were linear (r2 � 0.999). The chromatographic data (the peakarea ratios of DHA to artemisinin and ARM to artemisinin) were processed usingLabSolutions software (version 5; Shimadzu, Japan). No matrix effect (ion suppres-sion/enhancement) was observed under methodologies described elsewhere (23),and the performance of both assays, assessed as intra- and interday relative standarddeviations across relevant concentration ranges, was similar to that published pre-viously (7, 18). Interday accuracies of QC assays were �15% of nominal values on
all occasions. The limits of quantification and detection were, respectively, 2 and 1�g/liter for DHA and 5 and 2 �g/liter for ARM.
LUM and DBL were quantified in plasma using validated high-performanceliquid chromatography with a UV detection assay (HPLC-UV) and a validatedultra-high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay, respectively, as previously described (32). The linear range forLUM was 20 to 20,000 ng/ml; interday variability was 4.94%, 4.93%, 7.16%, and11.23% and intraday variability was 2.83%, 4.41%, 4.11%, and 9.55% at 20,000,2,000, 200, and 20 ng/ml, respectively. For DBL, the linear range was 0.5 to 100ng/ml; interday variability was 3.36%, 3.47%, 9.98%, and 6.74% and intradayvariability was 2.47%, 3.46%, 8.16%, and 3.48% at 50, 10, 1, and 0.5 ng/ml,respectively. As an LC-MS/MS method was used for DBL, matrix effects wereassessed where between-subject variability was 3.37%, 4.47%, and 9.43% at 50,10, and 1 ng/ml, respectively.
Pharmacokinetic modeling. Loge (natural log) plasma concentration-time datasets for LUM with DBL and for ARM with DHA were analyzed by nonlinearmixed-effect modeling using NONMEM (version 6.2.0; Icon Development So-lutions, Ellicott City, MD) with an Intel Visual Fortran 10.0 compiler. Thefirst-order conditional-estimation with interaction (FOCE-I) method was usedfor the LUM-DBL model, and the Laplacian with interaction method was usedfor ARM-DHA. The minimum objective-function value (OFV) and weighted-residual (WRES) plots were used to choose suitable models during model build-ing. As FOCE-I estimation was used, conditional weighted residuals were con-sidered in addition to WRESs in the initial stages of model building (17).However, as they were similar, WRESs were considered suitable for furthermodel building. Concentrations were modeled in �g/ml, with a conversion factorfor all metabolite parameters included in the model to account for the differencein molecular weight between the parent drug and the metabolite. Allometricscaling was used a priori, with volume terms multiplied by (weight/70)1.0 andclearance terms by (weight/70)0.75 (3). Residual variability (RV) was estimated asan additive error for the loge-transformed data. Models were parameterizedusing the absorption rate constant (ka), central volume of the distribution (VC/F,where F is bioavailability), clearance (CL/F), and peripheral volume of thedistribution(s) (VP/F) and its respective intercompartmental clearance(s) (Q/F).
For the LUM-DBL model, plasma LUM concentrations were initially modeledusing inbuilt 2- and 3-compartment model structures with first-order absorption anda fixed lag time of 2 h (22) (Advan 4 and 12). Once a suitable (3-compartment) LUMmodel had been determined, the DBL data set was added and modeled simultane-ously. User-defined linear mammillary models (Advan 5) were constructed by testing1-, 2-, and 3-compartment models with and without first-pass LUM metabolism. Asno data exist regarding the degree of in vivo DBL conversion from LUM, this was setto 100% to allow identifiability. Therefore, all clearance and volume terms for DBLare relative to LUM bioavailability (FLUM) as well as the degree of metabolicconversion from LUM (Fmet-DBL). The term F*DBL (representing FLUM timesFmet-DBL) will be used for simplicity.
As 45% and 12% of plasma ARM and DHA concentrations, respectively, werebelow the limit of quantification (BLQ), we used a published method known toproduce reliable pharmacokinetic parameters in this situation (9, 10). Themethod (known as M3) (2) models continuous and categorical data simultane-ously. Concentrations above the limit of quantification (LOQ) are included asconventional continuous data, while those BLQ are treated as categorical, andthe likelihood (probability) that they are BLQ was maximized with respect tomodel parameters. This allows BLQ observations to contribute to the determi-nation of the OFV and the finalizing of the model structure.
Initially, plasma ARM concentrations were assessed using 1- and 2-compart-ment models with first-order absorption (Advan 2 and 4) to obtain a suitablestructure. The ka for ARM was fixed to 1 h�1 (31), as the data did not supportits estimation. Once a suitable (2-compartment) ARM model had been deter-mined, the DHA data set was added and modeled simultaneously using a user-defined linear mammillary model (Advan 5). For DHA, 1- and 2-compartmentmodels were assessed and the conversion of ARM to DHA was consideredcomplete for identifiability purposes. Therefore, all clearance and volume termsfor DHA are relative to ARM bioavailability (FARM) as well as the degree ofmetabolic conversion from ARM (Fmet-DHA). The term F*DHA (representingFARM times Fmet-DHA) will be used for simplicity.
Once the model structure was established, interindividual variability (IIV),interoccasion variability (IOV), and their correlations were estimated. Relation-ships between model parameters and the covariates of age, sex, baseline para-sitemia, and baseline hemoglobin were identified using correlation plots andsubsequently evaluated within NONMEM. Inclusion of the covariate relation-ship required a decrease in OFV of �6.63 (�2 distribution with 1 df, P � 0.01),accompanied by a decrease in the IIV of that parameter.
VOL. 55, 2011 ARTEMETHER-LUMEFANTRINE PHARMACOKINETICS IN CHILDREN 5307
Model evaluation. A bootstrap using Perl-speaks-NONMEM (PSN) with 1,000samples was performed, and the parameters derived from this analysis weresummarized as the median and 2.5th and 97.5th centiles (95% empirical confi-dence interval [CI]) to facilitate evaluation of the final-model parameter esti-mates. Runs were included in the bootstrap analysis regardless of their minimi-zation status. In addition, visual predictive checks (VPCs) were performed with1,000 data sets simulated from the final models. The observed 10th, 50th, and90th percentiles were plotted with their respective simulated 95% CIs to assess thepredictive performance of the model. For the ARM-DHA model, the observedfraction of BLQ observations was compared with the median and 95% predictionintervals (PIs) of BLQ observations from these simulated data sets (9).
The applicability of the final population models to younger patients from thepresent sample was assessed using a numerical predictive check. Day 7 plasmaLUM concentrations (18) from children aged 0.5 to 5 years from a previous studywere compared with the simulated data from the final models. The actual andsimulated numbers of data points above and below the 20%, 40%, 60%, 80%,90%, and 95% simulated prediction intervals were compared.
Statistical analysis. Changes in hemoglobin, glucose, and audiometric dataover time were assessed using the Wilcoxon signed-rank test. The areas underthe concentration-time curves from 0 h to infinity (AUC0–�s) of DBL and LUMwere compared between subjects with or without recurrent parasitemia using theMann-Whitney U test. A two-tailed level of significance of 0.05 was consideredsignificant for all comparisons.
RESULTS
Clinical characteristics and course. The baseline character-istics of the 13 recruited children are summarized in Table 1.Eleven had a monoinfection (9 P. falciparum, 2 P. malariaeinfections) on confirmatory expert microscopy, while 2 had amixed P. falciparum/P. vivax infection. AL treatment was welltolerated, and reported symptoms were mild/moderate, short-lived (�3 days), and consistent with clinical features of uncom-plicated malaria. Times to initial fever and parasite clearancewere �48 h in all cases.
By the 28th day of follow-up, three children had developedslide-positive P. vivax (two had P. vivax at enrollment) and twochildren had developed P. falciparum (one had P. falciparum atenrollment) parasitemia. By the 42nd day of follow-up, fivechildren had been diagnosed with P. vivax (including the twowho had P. vivax at enrollment) and three with P. falciparum(including the one who had P. falciparum at enrollment). Thesedata are consistent with the uncorrected PCR results of aprevious, larger comparative treatment trial in younger chil-dren performed at the same location (18). The recurrent P.
vivax parasitemia could have resulted from (i) a recrudescentinfection in those infected with this parasite before treatment,(ii) the acquisition of a new P. vivax infection after treatment,or, since no primaquine therapy was administered, (iii) theappearance of P. vivax from hypnozoites present in the liver atstudy entry. P. falciparum parasitemia detected during fol-low-up could have represented recrudescence or reinfection.
The mean hemoglobin concentration was significantlyhigher on day 28 than at enrollment (10.7 versus 8.9 g/liter, P �0.01). There was no significant change in blood glucose overthe first 3 days of enrollment or in audiometric findings over 28days (data not shown).
Pharmacokinetic modeling. LUM and DBL plasma concen-tration-time curves are shown in Fig. 1. A 3-compartmentmodel proved superior to a 2-compartment model for LUM,with a lower OFV and reduced bias in the WRES plot. Theaddition of two compartments and the inclusion of first-passmetabolism provided the best model once the DBL data sethad been added. Therefore, the final model comprised 3 com-partments for LUM and 2 compartments for DBL. The struc-tural model parameters were ka, VC/FLUM, VP1/FLUM, VP2/FLUM, CL/FLUM, Q1/FLUM, Q2/FLUM, the percentagecontribution of first-pass metabolism to DBL metabolic con-version (FP), VC/F*DBL, VP/F*DBL, CL/F*DBL, and Q/F*DBL.Interindividual variability was able to be estimated for ka, CL/FLUM, CL/F*DBL, VC/F*DBL, and FLUM, as was interoccasionvariability for FLUM (the population value of FLUM remainedfixed to 1). The variability in FLUM values was smaller betweenindividuals than it was between doses in the same individual(20 versus 67%). Once IIV and IOV terms were added, in-spection of the WRES plot revealed a bias due to the absorp-tion profile of the final dose. Estimation of a separate ka for the6th and final dose (kaD6) improved the bias and reduced theOFV (�7.519, P � 0.01). None of the covariates tested im-proved the model. Residual variability (20.8% and 20.9% forLUM and DBL, respectively) was low.
The final-model parameter estimates and the bootstrap re-sults are summarized in Table 2. Bias was �10% for structural-and random-model parameters. Figures 2 and 3 show good-
TABLE 1. Baseline characteristics of study participants
Characteristic Value (%)a
Age (yr) ....................................................................................... 7.7 � 1.4Male............................................................................................. 8 (62)Wt (kg) ........................................................................................19.0 � 3.5Ht (cm)........................................................................................ 112 � 9Axillary temp (°C)......................................................................36.8 � 1.0
Infection typesP. falciparum parasitemia...................................................... 9 (69)P. falciparum/P. vivax parasitemia........................................ 2 (15)P. malariae parasitemia ......................................................... 2 (15)
Respiratory rate/min.................................................................. 28 � 9Supine pulse rate/min................................................................ 102 � 16Mean upper arm circumference (cm) ..................................... 16 � 1Hemoglobin (g/liter) .................................................................. 8.9 � 1.6
a Data are numbers (percentages), means � standard deviations, or medians �interquartile ranges (n � 13).
FIG. 1. Time-concentration plots showing concentrations of LUM(E) and DBL (ƒ) in �g/liter on a log10 scale. Curves of the medianconcentrations for LUM (solid black line) and DBL (dashed blackline) are also shown.
5308 SALMAN ET AL. ANTIMICROB. AGENTS CHEMOTHER.
ness-of-fit plots and VPCs, respectively. The half-lives andAUCs of LUM and DBL are shown in Table 3. The firstdistribution, second distribution, and terminal eliminationhalf-lives for LUM had median values of 10.4, 46.6, and 126 h,respectively, while DBL had a median distribution half-life of19.7 h and a median terminal elimination half-life of 141 h.Overall, the metabolite-to-parent-drug ratio was 1.13% (ob-tained from AUC0–�s), but there was a higher ratio at latertime points. Day 7 LUM concentrations obtained fromyounger children were consistent with predictions based on thefinal model, which resulted in the expected numbers of obser-vations above and below the 20, 40, 60, 80, 90, and 95% sim-ulated PIs. When the same data for DBL were compared,there was an excess of points above the 20, 40, 60, 80, and 90%PIs and a lack of points below the 20 and 40% PIs, especiallyat a younger age, demonstrating that the day 7 DBL levels inthe younger children were higher than expected from themodel.
Initial modeling of ARM-DHA data sets proved difficult,given the large proportion of BLQ data (45% and 12% forARM and DHA, respectively). Once these data were incorpo-rated into the model using the M3 method from Ahn et al. (2),more-acceptable models were obtained. The dispositions ofARM and DHA were best described by a 2-compartmentmodel for ARM and a 1-compartment model for DHA. Thestructural-model parameters were ka, VC/FARM, VP/FARM, CL/FARM, Q/FARM, VC/F*DHA, and CL/F*DHA. As with LUM, theIIV and IOV of FARM were estimated, and the variabilitybetween doses was larger than between individuals (84.1 versus38.1%). The IIV of CLARM was also estimated. A relationshipbetween CLARM and dose number was included and demon-strated that for each subsequent dose of ARM, CLARM in-creased by 67.8% relative to its value after the first dose. Thisrelationship was accompanied by a decrease in the OFV
(�82.774, P � 0.001) and a reduction in the RVs of both ARMand DHA. No other covariate relationship improved themodel. After the inclusion of IIV/IOV terms and the covariaterelationship, the RVs were still high, at 51.6% and 53.3% forARM and DHA, respectively.
The final-model parameter estimates and the bootstrap re-sults are summarized in Table 4. As the covariance step wasnot successful, NONMEM-derived relative standard errorscould not be obtained. Bias was �11% for structural andrandom parameters, except the IIV for FARM, which had anegative 48% bias. Figures 4 and 5 show goodness-of-fit plotsand VPCs, respectively. The VPCs show all observed 10th,50th, and 90th percentiles within their simulated 95% CIs andthe fraction of BLQ data at each time point within its 95% CIfor both ARM and DHA. Secondary parameters for studyparticipants are shown in Table 3. The AUC0–�s and half-livesof ARM decreased with each dose, while the median DHA-to-ARM ratio increased.
Relationship between drug exposure and treatment out-come. The LUM AUC0–� in children with recurrent parasitemiaon days 28 (n � 5) and 42 (n � 8) tended to be lower than thatin children who remained aparasitemic at these times (P � 0.057and 0.086, respectively). There were no differences in the AUC0–�
for DBL (P � 0.46 and 0.89, respectively). However, a combinedAUC0–�, with DBL weighted four times more than LUM (con-
TABLE 2. Final population pharmacokinetic estimates andbootstrap results for LUM and DBL
Parameter Mean(RSE %)a Bootstrap median (95% CI)
Objective-function value �586.510 �601.901 (�668.687 to �559.564)
Structural-model parameterska (/h) 0.461 (20) 0.442 (0.285 to 0.644)CL/FLUM (liters/h/70 kg) 7.29 (9) 7.21 (5.55 to 9.04)VC/FLUM (liters/70 kg) 227 (12) 225 (147 to 284)Q1/FLUM (liters/h/70 kg) 1.52 (16) 1.57 (0.96 to 2.32)VP1/FLUM (liters/70 kg) 115 (19) 109 (57 to 214)Q2/FLUM (liters/h/70 kg) 0.743 (13) 0.805 (0.208 to 1.27)VP2/FLUM (liters/70 kg) 164 (8) 168 (97 to 240)kaD6 (/h) 1.20 (52) 1.14 (0.50 to 3.68)FP (%) 6.29 (15) 6.45 (4.36 to 9.84)CL/F*DBL (liters/h/70 kg) 701 (10) 694 (561 to 851)VC/F*DBL (liters/70 kg) 51,100 (10) 51,200 (42,200 to 61,430)Q/F*DBL (liters/h/70 kg) 439 (19) 424 (305 to 632)VP/F*DBL (liters/70 kg) 68,400 (14) 68,000 (51,800 to 88,600)
Random-model parametersIIV in FLUM (%) 19.8 (42) 18.9 (2.5 to 29.3)IIV in ka (%) 55.4 (44) 55.8 (17.1 to 92.2)IIV in CL/FLUM (%) 17.7 (20) 16.9 (6.8 to 23.7)IIV in CL/F*DBL (%) 26.2 (26) 26.0 (10.4 to 37.6)IIV in VC/F*DBL (%) 34.1 (22) 33.3 (17.4 to 47.8)IOV in FLUM (%) 67.0 (9) 66.4 (53.4 to 77.7)RV for LUM (%) 20.8 (7) 20.3 (17.4 to 22.5)RV for DBL (%) 20.9 (7) 20.6 (17.5 to 23.0)
a RSE percentages are the NONMEM-produced values from the covariance step.
FIG. 2. Population (E) and individual (F) predicted data versusobserved data for LUM (A) and DBL (B) concentrations (�g/liter)from the final model. The lines of identity are also shown.
VOL. 55, 2011 ARTEMETHER-LUMEFANTRINE PHARMACOKINETICS IN CHILDREN 5309
sistent with its greater antimalarial potency in vitro [26, 28, 29,32]), was significantly lower in children with recurrent parasitemiaon day 28 than in aparasitemic children (P � 0.028) and was ofborderline significance on day 42 (P � 0.063).
DISCUSSION
In the present study of Papua New Guinean children withuncomplicated malaria treated with a conventional AL regi-men, rich data sets of plasma concentrations of LUM, ARM,and their active metabolites measured during an extended fol-low-up period were successfully analyzed using populationpharmacokinetic modeling that allowed for a high proportionof BLQ plasma ARM and DHA concentrations. Our analysesincluded the first compartmental PK analysis of plasma DBLlevels. We found that current dose recommendations for AL inchildren result in a LUM AUC similar to that achieved inadults despite children receiving a higher average mg/kg dosethan a 50-kg adult. However, the subgroup of children weigh-ing 12.5 to 15 kg receives the lowest mg/kg dose and may be atrisk of being underdosed.
Three studies, all from Africa, have examined LUM phar-macokinetics after AL treatment in children. The first andsimplest compared crushed tablets and a dispersible formula-tion by using a pooled analysis of single blood samples taken atone of six time points during a 14-day period from 726 children
FIG. 3. Visual predictive check showing the observed 50th (F),10th (197), and 90th (E) percentiles with the simulated 95% CIs forthe 50th (solid black line), 10th (dotted gray lines), and 90th (dashedgray lines) percentiles for LUM (A) and DBL (B) concentrations(�g/liter on a log10 scale) from the final model.
TABLE 3. Secondary pharmacokinetic parameters derived from post hoc Bayesian estimates for study participantsa
Parameter LUM DBLARM
DHADose 1 Dose 6 All doses
t1/2�b (h) 10.4 (10.3–11.8) 19.7 (18.4–22.5) 0.62 (0.60–0.64) 0.16 (0.12–0.33) 0.80 (0.76–0.82)
t1/2�b (h) 46.6 (44.8–48.2) 141 (135–150) 16.4 (15.7–16.8) 11.9 (11.2–13.2)
t1/2�b (h) 123 (120–127)
AUC0–�c
(�g � h/liter)459,980 (391,330–632,730) 5,434 (4,394–8,542) 983 (371–1,770) 164 (145–254) 3,063 (2,357–4,513) 2,839 (1,812–3,488)
AUCmetabolite/AUCparentdrug (%)
1.13 (0.93–1.55) 36.8 (36.8–36.8) 186 (91.8–268) 92.7 (59.2–94.3)
a Data are medians (interquartile ranges).b For LUM, t1/2�, t1/2� and t1/2� are the first-distribution, second-distribution, and terminal-elimination half-lives, respectively, while for DBL and ARM, t1/2� and t1/2�
represent the distribution and terminal-elimination half-lives, respectively, and for DHA, t1/2� represents the terminal-elimination half-life.c Represents either the AUC0–� for all six doses together or the AUC0–� for individual doses as if they were given alone.
TABLE 4. Final population pharmacokinetic estimates andbootstrap results for ARM and DHA
Parameter Mean(RSE %a)
Bootstrap median(95% CI)
Objective-function value 159.853 177.255 (77.606–249.014)
Structural-model parametersCL/FARM (liters/h/70 kg) 102 (27) 96.3 (57.0–167.0)VC/FARM (liters/70 kg) 193 (62) 172 (40–506)Q/FARM (liters/h/70 kg) 49.6 (47) 45.8 (19.7–111.1)VP/FARM (liters/70 kg) 1,070 (59) 1,220 (593–3,011)ka (/h) 1 (fixed) 1 (1–1)VC/F*DHA (liters/70 kg) 440 (40) 417 (69–826)CL/F*DHA (liters/h/70 kg) 277 (26) 275 (140–443)% increase in CL/FARM
for each subsequentdose (%)
67.8 (31) 73.3 (40.5–125)
Random-model parametersIIV in FARM (%) 38.1 (72) 19.7 (0.3–58.6)IOV in FARM (%) 84.1 (38) 84.2 (52.2–113.6)IIV in CL/FARM (%) 84.0 (33) 75.8 (49.1–108.2)RV for ARM (%) 51.6 (12) 50 (37–61)RV for DHA (%) 53.3 (20) 61 (42–83)
a RSE percentages are derived from the bootstrap.
5310 SALMAN ET AL. ANTIMICROB. AGENTS CHEMOTHER.
�12 years of age (1). The LUM AUC for both formulationswas higher than in the present study (574,000 and 636,000,respectively, versus 459,980 �g � h liter�1). In the second study(25), six blood samples were taken from children aged 5 to 13years, starting when the last AL dose was given, and the LUMAUC60–� was calculated using noncompartmental analysis.When we used our final models to generate an AUC60–�, it washigher (257,010 versus 210,000 �g � h liter�1). Based on theirdata, the authors reported that children have lower levels ofexposure to LUM than adults using recommended AL doseschedules (25). A third study of children aged 1 to 10 yearsutilized a population approach (16), but there was no samplingbeyond 72 h and no secondary pharmacokinetic parameterswere provided. A comparison with LUM disposition in thepresent study was, therefore, not possible.
Comparisons of LUM AUCs between studies in adults arealso difficult, as some report the AUC from the first dose, whileothers use the AUC60–�. Table 5 summarizes the available datafor both measures of drug exposure. There is a differencebetween LUM exposure in healthy adults and that in subjectswith malaria, but the AUCs for nonpregnant adults, pregnantadults, and children with malaria are similar. Current AL doserecommendations for children ensure that those weighing 15
to 35 kg receive a 35%-higher average mg/kg dose than a 50-kgadult, but those weighing 12.5 to 15 kg receive a lower mg/kgdose. The AUC data support the higher average mg/kg dosefor children and suggest that those weighing 12.5 to 15 kgshould receive 2 tablets rather than 1 to avoid underdosingwhile not exceeding the highest recommended mg/kg dose(Fig. 6). As LUM exposure, measured as either the AUC orday 7 levels, has previously been shown to be a prime deter-minate of efficacy (12, 27), it is important that underdosing isavoided.
The three studies of AL in children also measured plasmaARM-DHA concentrations (1, 16, 25). The first was not ableto calculate AUCs from pooled concentration data due to asparse sampling schedule (1). The second employed a limitedsampling schedule starting from the last AL dose (25), and the
FIG. 4. Population (E) and individual (F) predicted data versusobserved data for ARM (A) and DHA (B) concentrations (�g/liter)from the final model. The lines of identity are also shown. The dashedgray lines represent the LOQs of ARM (A) and DHA (B).
FIG. 5. Visual predictive check showing the observed 50th (F), 10th(197), and 90th (E) percentiles with the simulated 95% CIs for the 50th(solid black line), 10th (dotted gray lines), and 90th (dashed gray lines)percentiles for ARM (A) and DHA (B) concentrations (�g/liter on a log10scale) from the final model. The fractions of BLQ observations from thedata (E connected with a dotted black line) with the simulated 95%prediction intervals are also shown for both ARM and DHA.
VOL. 55, 2011 ARTEMETHER-LUMEFANTRINE PHARMACOKINETICS IN CHILDREN 5311
AUCs were therefore lower than those of the present study(168 versus 217 �g � h liter�1 for ARM and 382 versus 402�g � h liter�1 for DHA). The population approach used in thethird study (16) produced models of the dispositions of ARM(two compartments) and DHA (one compartment) that weresimilar to those of the present study. Those authors reported asimilar increase in CL/FARM with each dose (57% versus67.8% in the children in our study) and higher RVs (61%versus 51.6% and 82% versus 53.3% for ARM and DHA,respectively), the latter observation likely a reflection of thefact that many plasma concentrations were close to or belowthe LOQ. As no secondary PK parameters were provided, acomparison of AUCs could not be performed. However, thehalf-lives of ARM, estimated from the pharmacokinetic pa-rameters provided, were longer than those in the children inour study (0.89 versus 0.62 h and 32.0 versus 16.4 h for distri-bution and elimination, respectively, of the first dose), while theelimination half-life of DHA was shorter (0.38 versus 0.80 h).
The AUCs for ARM and DHA in the present study weresimilar to those reported previously in adults with malaria (21,24) but higher than those in healthy adults (14, 19, 20). Ourterminal elimination half-life for ARM was longer than thosereported in these studies (16.4 versus 1.5 to 3.9 h), while forDHA, it was shorter (0.80 versus 1.2 to 2.1 h). The adult studiesused noncompartmental methods to determine these half-lives, and this may account for the differences. Nevertheless,based on these comparisons, exposure to ARM and DHA inchildren is adequate with current AL dose recommendations.
Few studies have evaluated the disposition of DBL, an activemetabolite of LUM. Our DBL/LUM ratio (1.13%) falls be-tween values reported in previous treatment studies (0.33%and 5.2%) (15, 24). The lower value (0.33%) was from a studyof nonimmune Colombian adults with malaria that sampled to168 h and reported the AUC0–168. The higher value (5.2%) wasfrom a study of pregnant Thai women with malaria in whichsampling started after the last dose and the AUC60–� wasreported. The difference between these values can, at least inpart, be explained by the study designs, as the metabolite-to-parent-drug percentage calculated from the AUC60–� in thepresent study is more than double that for the AUC0–168 (1.96versus 0.76%). However, it is likely that ethnicity and preg-nancy contribute to the difference. Age may also influence the
metabolic conversion of LUM to DBL, as our PK model waseffectively able to predict concentrations of LUM, but notDBL, in young children. It is uncertain whether malaria itselfalso influences the ratio, since it was 0.45%, within the range ofvalues from studies of malaria after a single dose of AL in 22healthy adults (G. Lefevre, personal communication).
As reported previously (24), DBL had a longer terminalelimination half-life than LUM in the present study (141 versus123 h), and therefore, the DBL/LUM ratio will increase withtime. Although the ratios found in available studies are low,the in vitro potency of DBL is between 2.2 and 7.2 times that ofLUM (26, 28, 29, 32) and it may therefore contribute to thetherapeutic outcome. We found that a combined weightedLUM-DBL AUC was likely to be lower than the AUC of eitherLUM or DBL alone in subjects with recurrent parasitemia atdays 28 and 42. This supports the suggestion that DBL mayinfluence AL’s treatment outcome (32).
Although the variable bioavailabilities of ARM and LUMhave been previously reported (13), they have not been previ-ously quantified in children. Given the significant increase inthe number of fed versus fasted healthy volunteers (22), it isrecommended that AL be administered with fat in order toimprove absorption. Based on a study in healthy adults whoreceived a single dose of AL, 1.2 g of fat (equivalent to 35 mlof full-cream milk) is required to achieve 90% of the maximalLUM bioavailability (4). These results may not be directlyapplicable to the children with malaria in our study, as theyingested 2 g of fat with each dose, but there was still significantbetween-dose variability in the bioavailabilities of both LUM(67.0%) and ARM (84.1%). We were unable to identify factorsthat may be responsible for these observations.
In the analysis of the ARM-DHA data set, there was asignificant number of BLQ plasma concentrations. This is anissue encountered in pharmacokinetic analyses of a variety ofother antimalarial drugs (6, 8, 16). Traditional approaches tothis problem, such as excluding BLQ data from the analysis orsetting them to a specific value (such as 0 or 50% of the LOQ),have been shown to bias the pharmacokinetic parameters, even
FIG. 6. Doses of lumefantrine and artemether in mg/kg given tochildren weighing 5 to 35 kg under current (solid black line) andsuggested (dashed gray line) dosing regimens. The horizontal dottedblack line represents the dose in mg/kg recommended for a 50-kgadult.
TABLE 5. Summary of studies reporting theAUC for lumefantrinea
Population sampleAUC60–� or AUC60–t
(�g � h/liter)(reference�s�)
AUC0–� or AUC0–t(�g � h/liter) (reference�s�)
Healthy adults 383,000–456,000 (14, 19) 1,242,000–2,730,000b (11, 14)Nonpregnant adults
with malaria335,000–758,000 (5, 13, 15, 22)
Pregnant womenwith malaria
252,000 (24) 472,000 (30)
Children withmalaria
210,000 (16) 572,000–636,000 (1c)
Children in presentstudy
257,000 459,980
a AUCs are either medians or means and are reported either to the last datapoint (t) or to infinity.
b As subjects in the study by Bindschedler et al. (11) received only a singledose, the reported AUC has been multiplied by 6.
c This study used a pooled approach from single observations of each subjectto calculate the AUC.
5312 SALMAN ET AL. ANTIMICROB. AGENTS CHEMOTHER.
when only 10% of the data are BLQ (2, 9, 10, 35). Our ap-proach was to use a method within NONMEM shown to havelittle bias in situations with up to 40% of data BLQ in apopulation analysis (10). This method treats BLQ data pointsas categorical data and maximizes the likelihood that theirvalues are truly below the LOQ (2). Although the implemen-tation of this method has previously been difficult and time-consuming, changes to NONMEM and more-efficient dataprocessing have increased its accessibility. The benefits of thismethod when demonstrated in relatively simple models arelikely to apply to more-complex models with parent drugs andmetabolites. We were unable to obtain relative standard errors(RSEs) for our parameters in this model, as the covariancestep was unsuccessful, a common problem when this method isused (9, 10). However, this does not impact the reliability ofthe results obtained, and other methods of model evaluation(such as bootstrap and VPC analyses) can still be used.
Our novel data relating to DBL pharmacokinetics andDBL’s favorable pharmacodynamic effects suggest that futureefficacy and pharmacokinetic studies of LUM should includeDBL assays to further elucidate its role. We have also shownthat analytical techniques that utilize BLQ data to refine phar-macokinetic parameter estimates can be applied in this situa-tion. Extended sampling and a population pharmacokineticapproach allow flexibility in deriving secondary parameters, animportant consideration when comparisons with publishednonstandard measures, such as time-limited AUCs, are of in-terest. Our data confirm that current AL dose recommenda-tions produce ARM, DHA, and LUM exposures in childrenthat are similar to those in adults with malaria. However, smallchildren weighing 12.5 to 15 kg are at risk of being underdosed,and AL doses could be doubled without exceeding the current,weight-based, maximum mg/kg dose in this patient group.
ACKNOWLEDGMENTS
We thank the children and their parents/guardians for their partic-ipation. We are most grateful to Valsi Kurian and the staff of Alex-ishafen Health Centre for their kind cooperation during the study. Wealso thank Jovitha Lammey, Christine Kalopo, and Bernard (“Ben”)Maamu for clinical and/or logistical assistance and Harin Karunajeewafor assistance with protocol design.
The National Health and Medical Research Council (NHMRC) ofAustralia funded the study (grant 634343). T.M.E.D. is supported byan NHMRC Practitioner Fellowship.
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VOL. 55, 2011 ARTEMETHER-LUMEFANTRINE PHARMACOKINETICS IN CHILDREN 5313
Pharmacokinetic Comparison of Two Piperaquine-ContainingArtemisinin Combination Therapies in Papua New Guinean Childrenwith Uncomplicated Malaria
Sam Salman,a Madhu Page-Sharp,b Kevin T. Batty,b Kaye Kose,c Susan Griffin,c Peter M. Siba,c Kenneth F. Ilett,a Ivo Mueller,c andTimothy M. E. Davisa
School of Medicine and Pharmacology, University of Western Australia, Fremantle Hospital, Fremantle, Western Australia, Australiaa; School of Pharmacy, Curtin Universityof Technology, Bentley, Australiab; and Papua New Guinea Institute of Medical Research, Madang, Papua New Guineac
Pharmacokinetic differences between piperaquine (PQ) base and PQ tetraphosphate were investigated in 34 Papua New Guineanchildren aged 5 to 10 years treated for uncomplicated malaria with artemisinin-PQ (ART-PQ) base or dihydroartemisinin-PQ(DHA-PQ) tetraphosphate. Twelve children received ART-PQ base (two daily doses of 3 mg of ART and 18 mg of PQ base asgranules/kg of body weight) as recommended by the manufacturer, with regular clinical assessment and blood sampling over 56days. PQ concentrations in plasma samples collected from 22 children of similar ages with malaria in a previously publishedpharmacokinetic study of DHA-PQ tetraphosphate (three daily doses of 2.5 mg of ART and 20 mg of PQ tetraphosphate as tab-lets/kg of body weight) were available for comparison. The disposition of ART was also assessed in the 12 children who receivedART-PQ base. Plasma PQ was assayed by high-performance liquid chromatography with UV detection, and ART was assayedusing liquid chromatography-mass spectrometry. Multicompartment pharmacokinetic models for PQ and ART were developedusing a population-based approach. ART-PQ base was well tolerated, and initial fever abatement and parasite clearance wereprompt. There were no differences between the two treatments in the values for the PQ area under the concentration-time curvefrom time zero to infinity (AUC0 –�), with medians of 49,451 (n � 12) and 44,556 (n � 22) �g · h/liter for ART-PQ base andDHA-PQ tetraphosphate, respectively. Recurrent parasitemia was associated with lower PQ exposure. Using a two-compartmentART model, the median AUC0 –� was 1,652 �g · h/liter. There was evidence of autoinduction of ART metabolism (relative bio-availability for the second dose, 0.27). These and previously published data suggest that a 3-day ART-PQ base regimen should befurther evaluated, in line with World Health Organization recommendations for all artemisinin combination therapies.
The most recent World Health Organization (WHO) recom-mendations for the treatment of uncomplicated malaria include
a 3-day course of dihydroartemisinin plus piperaquine (DHA-PQ) asa first-line artemisinin (ART) combination therapy (ACT) (48). Var-ious formulations of DHA-PQ are marketed in tropical countries(Duo-cotecxin, Combimal, and P-Alaxin; http://www.actwatch.info/resources/drugs_home03_search.asp) or are in development (Eura-rtesim) (20), and all employ PQ tetraphosphate as the DHA partnerdrug. DHA is a semisynthetic derivative of ART, and its productionadds to the manufacturing cost, but, unlike ART, it does not exhibitautoinduction of metabolism. In addition, although the tetraphos-phate salt of PQ has greater water solubility and therefore may havebetter oral bioavailability, incorporation of the lipid-soluble PQ baseshould also simplify production.
Artequick (Artepharm Co. Ltd., Guangzhou, China) is an ACTthat contains ART in place of DHA and PQ base rather than PQtetraphosphate. This combination is formulated as tablets but alsoas granules for pediatric use. It is marketed in Cambodia and somesub-Saharan African countries. The current manufacturer’s rec-ommendation is for Artequick to be given as a 2-day regimen,which contrasts with the 3 days recommended for all ACTs by theWHO (48). Although the tolerability, safety, efficacy, and phar-macokinetics (PK) properties of DHA-PQ tetraphosphate havebeen widely investigated in children and adults (27, 28, 36, 44, 49),there are limited data relating to the efficacy and tolerability ofART-PQ base (37, 46) and no studies of the pharmacokinetics ofthis novel combination in malaria-infected patients. Concernshave been raised regarding possible underdosing in children for a
number of antimalarial drugs (8, 33), including PQ (27, 36, 49).Although children have been included in studies of PQ pharma-cokinetics (28, 44), only one pharmacokinetic study of ART hasspecifically enrolled pediatric patients (40).
We have evaluated the population pharmacokinetics ofART-PQ base (Artequick) in children from Papua New Guinea(PNG) with uncomplicated malaria and compared the data withthose of a previously published study of DHA-PQ tetraphosphate(Duo-cotecxin; Beijing Holley-Cotec, Beijing, China) in the samecategory of patients (29). The primary aims of the present studywere to investigate pharmacokinetic differences between PQ baseand PQ tetraphosphate and to describe the population pharma-cokinetics of ART in PNG children. Secondary aims were to pro-vide preliminary data relating pharmacokinetic factors to recur-rent parasitemia and to use both pharmacokinetic and efficacydata to suggest improved dose regimens for these combinations.
Received 28 November 2011 Returned for modification 19 January 2012Accepted 26 March 2012
Published ahead of print 2 April 2012
Address correspondence to Timothy M. E. Davis, [email protected].
Copyright © 2012, American Society for Microbiology. All Rights Reserved.
doi:10.1128/AAC.06232-11
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MATERIALS AND METHODSPatients. Assessment and recruitment of children for the present andpublished DHA-PQ tetraphosphate studies were as described previously(29). Briefly, all subjects were children aged 5 to 10 years presenting toAlexishafen Health Centre, Madang Province, on the north coast of PNG.The clinic serves an area where Plasmodium falciparum and P. vivax arehyperendemic and where P. ovale and P. malariae are also transmitted(13). Children with an axillary temperature � 37.5°C or a history of feverin the previous 24 h were screened with a Giemsa-stained thick blood filmread on site by a trained microscopist. Those with a monoinfection of P.falciparum (�1,000 asexual parasites �l of whole blood) or of P. vivax, P.ovale, or P. malariae (�250 asexual parasites �l of whole blood) wereeligible provided that the child’s parents gave informed consent, therewere no features of severe malaria (47), they had not taken any antima-larial drug in the previous 14 days, there was no evidence of another causeof fever, and there were no features of malnutrition or other chroniccomorbidity. Although the locations, populations, and enrollment proce-dures used in the two studies were identical, the DHA-PQ group wasenrolled between August 2005 and January 2006 whereas the ART-PQbase group was enrolled from March 2008 to May 2008. The study wasapproved by the PNG Institute of Medical Research Institutional ReviewBoard and the Medical Research Advisory Committee of the PNG Depart-ment of Health.
Clinical methods. In the present study of ART-PQ base, a standard-ized history was taken and a clinical examination was performed. A 3-mlvenous blood sample was taken for baseline blood film microscopy, forhemoglobin and blood glucose, and for subsequent drug assays of sepa-rated plasma. Each child was treated with granules of ART-PQ base (Arte-quick) according to body weight (approximately 3 mg of ART and 18 mgof PQ base/kg of body weight/day). This dose was repeated at 24 h, asrecommended by the manufacturer, with the exact time of each doserecorded. All doses were given under direct observation. The full contentsof each sachet were mixed with at least 50 ml of cow’s milk (equivalent to2 g of fat), as fat has been reported to increase the bioavailability of PQtetraphosphate (25, 41). The volume of milk used was based on previousexperience with its palatability and association with nausea in PNG chil-dren as well as on the amount of fat found to maximize the absorption oflumefantrine, another highly lipophilic antimalarial drug, in healthyadults (5).
Further venous blood samples were taken from an indwelling intrave-nous catheter at 1, 2, 4, 12, 24, 28, 36, and 48 h and then by venesection ondays 3, 5, 7, 14, 28, 42, and 56. All samples were centrifuged promptly andred cells and separated plasma stored frozen at �80°C until assayed. De-tailed clinical assessment, including a symptom questionnaire and deter-mination of blood film, hemoglobin, and blood glucose data, was re-peated on days 1, 2, 3, and 7, with additional clinical assessment anddetermination of blood film data on days 14, 28, 42, and 56. All bloodsmears taken at baseline and during follow-up were examined indepen-dently by at least two skilled microscopists in a central laboratory. Eachmicroscopist viewed 100 fields at �1,000 magnification before a slide wasclassified as negative. Any slide discrepant for positivity or negativity orfor species identification was referred to a third microscopist for adjudi-cation.
The clinical procedures followed for the DHA-PQ group have beenpreviously described (29) and were similar to those followed for theART-PQ base group. Differences in the previous study included (i) ad-ministration of 3 days of DHA-PQ tetraphosphate tablets at a dose of 2.5mg of ART and 20 mg of PQ tetraphosphate/kg of body weight daily(equivalent to 11.5 mg of PQ base/kg of body weight daily), (ii) drugadministration with water, and (iii) blood sampling and clinical follow-uponly until day 42.
Laboratory methods. A PQ tetraphosphate reference standard wasobtained from Yick-Vic Chemicals and Pharmaceuticals, Ltd. (HongKong, China). Chloroquine (CQ) diphosphate and authentic ART werefrom Sigma-Aldrich (St. Louis, MO), and artemether (ARM) was from
AAPIN Chemicals Ltd. (Abingdon, United Kingdom). Solid-phase ex-traction (SPE) Bond Elut PH columns were purchased from VarianInc. (Palo Alto, CA). High-performance-liquid-chromatography(HPLC)-grade methanol was obtained from Merck Pty. Ltd. (Kilsyth,Australia), and liquid-chromatography—mass-spectrometry (LC-MS)-grade ammonium formate was from Sigma-Aldrich (Gillingham,United Kingdom). All other solvents and chemicals were of analyticalgrade.
For the ART-PQ base group, PQ in plasma was analyzed by high-performance liquid chromatography as described for the originalDHA-PQ group (29) with minor modifications. Briefly, plasma wasspiked with CQ as an internal standard, alkalinized, and extracted into 8ml of hexane-isoamyl alcohol (99:1). Baseline samples were assayed forCQ prior to quantification of PQ to ensure no interference with the inter-nal standard. After centrifugation, the supernatant was back extractedinto 100 �l of 0.1 M HCl, aspirated, and recentrifuged. Aliquots of 80 �lwere injected into a Phenomenex C6-phenyl column (Phenomenex, Tor-rance, CA) with a mobile phase of 11% acetonitrile– 0.1 M phosphatebuffer (pH 2.5) pumped at 1 ml/min. Retention times were 2.5 and 7.3min for PQ and CQ, respectively, and PQ and CQ were detected at 340nm. The linear assay range was 2 to 1,000 �g/liter, and the intraday relativestandard deviations (RSDs) were 10.8%, 8.2%, and 9.4% and the interdayRSDs were 11.6%, 4.4%, and 6.7% at 5, 100, and 1,000 �g/liter, respec-tively. The limits of quantification and detection were 2 �g/liter and 1�g/liter, respectively.
For ART, the extraction procedure used a 1-ml C18 SPE column aspreviously described (9), with the following modifications. Briefly, theSPE column was preconditioned with 1 ml of methanol followed by 1 mlof 1 M acetic acid. Plasma samples (0.5 ml) were spiked with an internalstandard (ARM; 1,000 �g/liter), loaded onto the preconditioned SPE col-umn, and drawn through using a medium vacuum. The column was thenwashed with 1 M acetic acid (1 ml used in each of two successive washes)followed by 20% (vol/vol) methanol in 1 M acetic acid (1 ml). The columnwas dried under low-vacuum conditions for 30 min, and retained drugswere eluted with 2 ml of t-butyl chloride:ethyl acetate (80%:20% [vol/vol]). The eluate was evaporated in a vacuum evaporator at 35°C and thenreconstituted in 50 �l of the mobile phase, and 5-�l aliquots were injectedinto the LC-MS system.
The LC-MS system used was a single quadrupole mass spectrometer(model 2020; Shimadzu, Kyoto, Japan) consisting of a binary pump(model 20AD), vacuum degasser, thermostated autosampler (model SIL20ACHT), thermostated column compartment (model CTO 20A), pho-todiode detector (model SPD M 20A), and mass analyzer (model MS2020) with both electrospray ionization (ESI) and atmospheric pressureionization (APCI) systems. Analyses were performed in isocratic modewith a mobile phase of 20 mM ammonium formate (pH 4.8):methanol(20:80) pumped at a flow rate of 0.2 ml/min. Chromatographic separationwas undertaken at 30°C on a Synergy fusion-RP C18 column (150-mmlength by 2.0-mm inner diameter) coupled with a 5-�m-pore-size C18
guard column (Phenomenex, Lane Cove, Australia) (4-mm length by3-mm inner diameter). Retention times were 4.2 min and 7.5 min for ARTand ARM, respectively. Optimized mass spectra were acquired with aninterface voltage of 4.5 kV, a detector voltage of 1 kV, a heat block tem-perature of 400°C, and a desolvation gas temperature of 250°C. Nitrogenwas used as a nebulizer gas at a flow rate of 1.5 liter/min and a dry-gas flowrate of 10 liter/min.
Quantification was performed by selected ion monitoring (SIM), us-ing the DUIS mode, in which ACPI and ESI are used simultaneously. Allstandard curves were linear, with r2 � 0.999. Chromatographic data (peakarea ratio of ART/ARM) were processed using the LAB Solution softwarepackage (version 5; Shimadzu, Japan). Responses from analysis of samplescontaining three different ART concentrations (5, 200, and 2,000 �g/liter) and one ARM concentration (1,000 �g/liter) spiked into fiveseparate plasma samples were used to determine matrix effects (ionsuppression/enhancement), absolute recovery, and process efficiency
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(32), which were between 90% and 98%, 82% and 93%, and 86% and91%, respectively. The assay intraday RSDs were 9.3, 7.2, and 3.7% andinterday RSDs were 9.5, 7.1, and 6.5% at 5, 200, and 2,000 �g/liter,respectively. The limits of quantification and detection for ART were2.5 and 1 �g/liter, respectively.
Pharmacokinetic modeling. Loge plasma concentration-time datasets for PQ and ART were analyzed by nonlinear mixed effects modelingusing NONMEM (version 6.2.0; ICON Development Solutions, EllicottCity, MD) with an Intel Visual FORTRAN 10.0 compiler. The PQ plasmaconcentration-versus-time data from a previously published study ofDHA-PQ performed by our group, which were originally analyzed using apatient rather than a population approach (29), were pooled with the PQconcentration data from the present study. The first-order conditionalestimation (FOCE) with interaction estimation method was used. Theminimum values of the objective function (OFV) and conditionalweighted residuals (CWRES) plots were used to choose suitable modelsduring the model-building process. Allometric scaling was employed apriori, with volume terms multiplied by (WT/70)1.0 and clearance termsby (WT/70)0.75 (3), where WT represents total body weight. Residualvariability (RV) data were estimated as additive errors for the log-trans-formed data. Secondary pharmacokinetic parameters, including the areaunder the concentration-time curve from time zero to infinity (AUC0 –�)and elimination half-life (t1/2), were obtained for the participants frompost hoc Bayesian predictions using NONMEM and the final model pa-rameters. Base models were parameterized using ka (absorption rate con-stant), VC/F (central volume of distribution), CL/F (clearance), and VP/Fand Q/F (peripheral volumes of distribution[s] and their respective inter-compartmental clearance[s]).
For the PQ data set, two- and three-compartment models (ADVAN 4and 12) with first-order absorption with and without lag time were tested.Since inspection of the time-concentration curves indicated that there wassignificant variability in the absorption phase, a transit compartmentmodel was also tested (39). In this model, the dose passes through a seriesof transit compartments before entering the absorption compartment inorder to model the delay often associated with drug absorption. A singlerate constant (ktr) represents entry and exit for all transit compartments.Using a previously described implementation of the transit compart-ment model in NONMEM (39), the number of transit compartments(NN) and the mean transit time [MTT � (1 � NN)/ktr] were estimatedas continuous variables. For the ART data set, 1- and 2-compartmentmodels (ADVAN 2 and 4) with first-order absorption with and with-out lag time were evaluated. Once the structure of the models wasestablished, interindividual variability (IIV), interoccasion variability(IOV), and correlations between IIV terms were estimated, where sup-ported by the data.
As two different formulations of PQ with different water/lipid solubil-ities were used, potential differences in their relative levels of bioavailabil-ity were assessed. The difference in relative bioavailability levels betweenfirst and subsequent doses of PQ and ART was also investigated. For PQ,this was achieved by estimating the differences between the relative bio-availability levels of the first dose of PQ phosphate (fixed to 1) and the twodoses of PQ base as well as the two subsequent doses of PQ phosphate.Similarly, for ART, the relative bioavailability of the first dose was fixed to1 and potential differences between this and subsequent doses were as-sessed. The inclusion of an extra parameter to account for differences inrelative bioavailability was considered only if accompanied by a signifi-cant (�6.63; P � 0.01) fall in the OFV and an improvement in the CWRESplot. Differences in absorption parameters (ka, NN, and MTT) betweenthe two groups were also assessed within NONMEM. As described below,the effect size (percent) of the difference was estimated. To maintain theextra parameter estimating this difference, a significant (�6.63; P � 0.01)fall in the OFV was required. Differences between clearance and volumeterms for the two formulations were not assessed, as the idea of differencesbetween a salt and base formulation of the same drug is biologically im-plausible.
Finally, relationships between model parameters and the covariatesage, sex, log (baseline parasitemia), and fever were identified throughinspection of scatter plots and box plots of post-hoc values for individualsobtained from IIV distributions versus covariate and were subsequentlyevaluated within NONMEM. The effect size (percent) of categorical data(sex, fever) was assessed, while both linear and power relationships wereevaluated for continuous covariates (age, log [baseline parasitemia]).For effect size, the individual parameter value � population parametervalue � (1 � effect parameter � covariate value [0 or 1]). For linearrelationships, the individual parameter value � population parametervalue � [1 � effect parameter � (covariate value for individual/aver-age value of covariate)]. For power relationships, the individual pa-rameter value � population parameter value � [(covariate value forindividual/average value of covariate)effect parameter]. A stepwise for-ward inclusion and backward elimination method was used, with asignificance of P � 0.05 required for inclusion of a covariate relation-ship and P � 0.01 to retain a covariate relationship.
As CQ was used as the internal standard in the PQ assay, the potentialimpact of residual CQ in the plasma of the children on pharmacokineticparameters was assessed through simulation. A previous study in a similargroup of children resident in the same study area demonstrated thatapproximately 50% had a measurable plasma CQ concentration whenhospitalized (12). Using plasma CQ concentrations from a previouspharmacokinetic study of Madang children (29), we simulated condi-tions such that (i) half of the children had, at random, received atreatment course of CQ finishing 14 days prior to the study (just beforethe exclusion period for such treatment) and (ii) only children fromone of the treatment groups received CQ treatment 14 days prior to thestudy. The latter simulation represents the worst-case scenario interms of the effect of residual CQ on the comparative pharmacokineticproperties of the two PQ formulations through exogenous augmenta-tion of the internal standard.
Model evaluation. Initially, plots of observed versus individual andpopulation predicted values and of time versus CWRES were assessed. Abootstrap analysis using Perl-speaks-NONMEM with 1,000 samples wasperformed (for NQ, this was stratified according to dose regimen), andthe parameters derived from this analysis were summarized as themedian and 2.5th and 97.5th percentiles (with 95% empirical confi-dence intervals [CI]) to facilitate evaluation of final model parameterestimates. In addition, prediction-corrected visual predictive checks(pcVPCs) (11) and numerical predictive checks (NPCs) were per-formed with 1,000 data sets simulated from the final models, and thesewere stratified according to treatment group for PQ. The observed10th, 50th, and 90th percentiles were plotted with their respectivesimulated 95% CIs to assess the predictive performance of the model.NPCs were assessed by comparing the actual with the expected num-bers of data points within the 20%, 40%, 60%, 80%, 90%, and 95%prediction intervals (PI). These were also stratified according to treat-ment group for the PQ model.
Statistical analysis. Comparisons between the baseline characteristicsand secondary pharmacokinetic parameters of the subjects in theDHA-PQ and ART-PQ base studies were assessed using the Mann-Whit-ney U test for continuous variables and the Fisher exact test for categoricalvariables. A two-tailed level of significance of 0.05 was considered signif-icant for all comparisons.
RESULTSClinical characteristics and course. The baseline characteristicsfor all the children in the study are summarized in Table 1. Ofthose who received ART-PQ base, 11 had a monoinfection with P.falciparum and 1 had a monoinfection with P. vivax. One child waslost to follow-up after day 14. ART-PQ base treatment was welltolerated; reported symptoms were mild and short-lived (�2days) and consistent with clinical features of uncomplicated ma-laria. Initial fever clearance occurred in �24 h in all cases, and
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parasite clearance occurred in �48 h in all but one child, in whomit occurred within 72 h. The child with P. vivax at enrollmentcleared parasitemia promptly and remained slide negative for the56 days of follow-up. Of the 11 children with P. falciparum, 1developed slide-positive P. falciparum on day 28, another on day42, and 2 more by day 56. As PCR was not performed, it was notpossible to determine if these represented recrudescence or rein-fection. Only one child with P. falciparum at entry became slidepositive for P. vivax, on day 56. The mean hemoglobin concentra-tion, where data were available, increased as a result of treatmentregardless of malaria status during follow-up, with mean (95% CI)increases from baseline of 1.9 (0.40 to 3.3) (n � 9), 1.1 (0.15 to 2.5)(n � 11), and 1.5 (0.20 to 2.5) (n � 10) g/dl on days 28, 42, and 56,respectively (P � 0.027, P � 0.19, and P � 0.041). No cases ofhypoglycemia were recorded.
Pharmacokinetic modeling. There were 298 and 174 individ-ual plasma PQ concentrations available from the DHA-PQ (n �22) and ART-PQ base (n � 12) studies, respectively. No drugconcentrations were below the limit of quantification during the56-day follow-up period. A 3-comparment model fitted the databetter than a 2-compartment model, with a significant decrease inthe OFV (�OFV � �109.232, P � 0.001). Although the additionof a lag time improved the model significantly (�OFV � �31.059,P � 0.001), the absorption phase was poorly described, with first-order absorption determined with or without lag time. Therefore,a transit compartment model was tested where the number oftransit compartments (NN) and the mean transit time (MTT)through the transit compartments were estimated as continuousvariables. The transit compartment model was significantly betterthan a model with lag time, resulting in a 37.173-point reductionin the OFV (P � 0.001). Further testing of the combined data setswith models in which the absorption processes of the two formu-lations of PQ differed (for example, use of a lag-time model for PQbase and a transit compartment model for PQ tetraphosphate)were also tested and offered no advantage over the use of asingle-transit-compartment model. A three-compartmentmodel remained superior to a two-compartment model withthe use of a transit compartment absorption determination(�OFV � �57.937, P � 0.001).
The structural model parameters were ka, NN, MTT, VC/FPQ,
VP1/FPQ, VP2/FPQ, CL/FPQ, Q1/FPQ, and Q2/FPQ. There was poorprecision for the estimate of ka (RSE % � 100) as well as a high(�0.95) correlation between ka and MTT. Therefore, with thedata available in this study, these two parameters could not beestimated simultaneously and ka was set to the same value as ktr,i.e., equal to (1 � NN)/MTT. Interindividual variability was esti-mable for MTT, CL/FPQ, VC/FPQ, andVP1/FPQ. Correlation be-tween IIV terms was estimated for CL/FPQ and VC/FPQ and forVC/FPQ andVP1/FPQ. The IOV for FPQ was also estimable and wasaccompanied by significant falls in OFV (�OFV � �69.12, P �0.001) and RV (35% to 29%). There was no significant differencebetween the relative bioavailabilities of the two formulations orbetween the subsequent doses of PQ base or tetraphosphate andthe first dose. Although inspection of the concentration-timecurves appeared to indicate a difference between the two formu-lations in their absorption phases, when differences in NN andMTT were evaluated, they did not improve the model. Likewise,none of the tested covariates improved the model.
The impact of residual CQ proved to be minimal as assessedusing the simulations, with population pharmacokinetic parame-ter estimates differing by �9%. When all participants in the sameformulation group were presumed to have taken CQ 14 days priorto the start of the study, there was still no significant differencebetween the population pharmacokinetic parameter estimates forthe two PQ formulations.
The final model parameter estimates and the bootstrap resultsfor both PQ formulations are summarized in Table 2. Bias was�10% for all fixed and random model parameters. With the ex-ception of IIV in CL/FPQ, all parameters were reasonably well es-timated, with relative standard errors of �33%. The correlationbetween CL/FPQ and VC/FPQ displayed a wide 95% CI (�0.186 to0.710). Figures 1 and 2 show goodness-of-fit plots and pcVPCs,respectively. The pcVPCs showed wide 95% confidence intervalsfor the 10th, 50th, and 90th percentiles due to relatively smallnumbers of children. The actual 10th, 50th, and 90th percentilesfell into their respective 95% CI ranges for all time points for bothgroups. The stratified NPCs demonstrated good predictive perfor-mance, with the expected number of points above and below the20%, 40%, 60%, 80%, 90%, and 95% PIs. The half-life, totalAUC0 –�, and dose-adjusted AUC0 –� values are shown in Table 3.
TABLE 1 Baseline characteristics of study participants
Parameter
Duo-cotecxin values(29) (historical)(n � 22)a
Artequick values(present study)(n � 12)a P value
Age (yr) 6.9 � 1.4 7.1 � 1.5 0.790b
Sex (% male) 17 (86) 8 (66) 0.687c
Weight (kg) 19.1 � 3.8 18.3 � 3.1 0.986b
Axillary temp (°C) 37.2 � 1.2 36.3 � 0.7 0.034b
No. (%) with P. falciparum parasitemia 19 (86) 11 (92) 1.00c
Parasite density (per �l of whole blood) 13,360 [6,900–51,650] 26,270 [3,480–35,30] 0.736b
No. (%) with P. vivax parasitemia 2 (9.1) 1 (8) 1.00c
No. (%) with P. malariae parasitemia 1 (4.5) 0 (0) 1.00c
Hemoglobin (g/dl) 8.6 � 1.8 9.3 � 2.1 0.168b
Total PQ base dose (mg/kg) 35.3 � 4.4 38.3 � 5.8 0.136b
Total DHA dose (mg/kg) 7.7 � 1.0Total ART dose (mg/kg) 6.4 � 1.0a Data represent number (%), mean � SD, or median [interquartile range {IQR}], as indicated.b Mann-Whitney U test.c Fisher’s exact test.
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There were no significant differences between the two PQ com-pounds in any of these secondary parameters. The first distribu-tion, second distribution, and terminal elimination t1/2 values forall participants had median values of 4.5, 36.0, and 512 h, respec-tively. The median PQ AUC0 –� values for the Artequick and Duo-cotecxin formulations were 49,451 �g · h/liter and 44,556 �g ·h/liter, respectively.
Of the 96 ART drug concentrations (ART-PQ base group, n �12) that were available for analysis, six (6.25%) were below thelimit of quantification but above the limit of detection. As theserepresented a small proportion of the data, they were included attheir measured values. All 12 children has measurable levels ofART to 48 h. Initial modeling of the ART data set demonstratedthat a two-compartment model was significantly better than aone-compartment model (�OFV � �73.417, P � 0.001) and thatthe absorption phase was best represented by first-order absorp-tion without a lag time. Therefore, the structural model parame-ters were ka, VC/FART, VP/FART,CL/FART, and Q/FART. The IIV ofVC/FART was estimable, as was the IOV on FART. The data sup-ported the estimation of a relative bioavailability term for the sec-ond dose of ART (F2,ART), with its addition resulting in a signifi-cant fall in the OFV (�OFV � �24.029, P � 0.001). Thebioavailability of the second dose was 0.270 relative to the first. Nosignificant covariate relationships were identified.
The final model parameter estimates and the bootstrap resultsfor ART are summarized in Table 4. Bias was �10% for all fixedand random parameters. ka was not well estimated, with a relative
standard error of 55% and a 4-fold range in the nonparametric95% CI. Figures 3 and 4 show goodness-of-fit plots and pcVPCs,respectively. The pcVPC showed that all observed 10th, 50th, and90th percentile values were within their simulated 95% CIs. Dueto the small numbers used in the analysis, these CIs were wide andoverlapping. The NPC demonstrated good predictive perfor-mance, with the expected number of points above and below the20%, 40%, 60%, 80%, 90%, and 95% PIs. The t1/2 and the AUC0 –�
for each dose as well as the total AUC0 –� for the study participantsare shown in Table 5. The median distribution and terminal elim-ination t1/2 values were 1.55 and 7.43 h, while the median totalAUC0 –� value was 1,652 �g · h/liter.
Relationship between drug exposure and treatment out-come. In the eight children whose samples became slide positivefor P. falciparum by day 42 (two in the ART-PQ base group and sixin the DHA-PQ group), the AUC0 –� value for PQ was signifi-cantly lower than that seen with those who remained free of P.falciparum infection (median, 39,297 versus 49,776 �g · h/liter;P � 0.0060). Clearance and terminal elimination t1/2 values werenot significantly different; however, these children received alower total dose of PQ (median, 31.4 versus 35.7 mg/kg of PQbase; P � 0.11). When adjusted for dose, the differences in theAUC0 –� values were no longer significant between those childrenwith and without slide positivity results for P. falciparum by day 42
TABLE 2 Final population pharmacokinetic estimates and bootstrapresults for piperaquinea
ParameterMean(RSE %)
Bootstrap median[95% CI]
Structural and covariatemodel parameters
MTT (h) 1.27 (11) 1.25 [1.12–1.58]NN 4.20 (19) 3.70 [2.77–5.36]CL/FPQ (liter/h/70 kg) 40.1 (7) 40.7 [36.6–45.1]VC/FPQ (liter/70 kg) 2,580 (13) 2,550 [1,996–3,142]Q1/FPQ (liter/h/70 kg) 113 (21) 119.0 [84.3–166.0]VP1/FPQ (liter/70 kg) 2,760 (24) 3,440 [2,750–5,510]Q2/FPQ (liter/h/70 kg) 52.4 (15) 52.9 [43.8–67.1]VP2/FPQ (liter/70 kg) 21,600 (8) 22,300 [19,300–25,320]
Random model parametersIOV in FPQ (%) 46 (14) 42 [36–54]IIV in CL/FPQ (%) 16 (53) 16 [5–29]IIV in VC/FPQ (%) 53 (33) 45 [31–71]IIV in VP1/FPQ (%) 68 (32) 64 [16–93]IIV in MTT (%) 43 (13) 42 [34–52]Correlation coefficient
(CL/FPQ, VC/FPQ)0.33 0.272 [–0.186–0.710]
Correlation coefficient(VC/FPQ, VP1/FPQ)
0.85 0.874 [0.381–1.00]
Residual variability (%) 29 (5) 29 [27–32]a Parameters are NN (number of transit compartments), MTT (mean transit time),CL/FPQ (clearance), VC/FPQ (central volume of distribution), VP1/FPQ and VP2/FPQ
(peripheral volumes of distribution), Q1/FPQ and Q2/FPQ (intercompartmentalclearance between VP1/FPQ and VC/FPQ and between VP2/FPQ and VC/FPQ
respectively), and F1,Artequick (bioavailability of the first dose of Artequick relative to thefirst dose of Duo-cotecxin). RSE (relative standard error) values were calculated frombootstrap results. OFV in final model, �329.926; bootstrap OFV (median [95% CI]),�316.869 [�416.930 to 285.019].
FIG 1 (A) Population (Œ) and individual (�) predicted versus observedplasma piperaquine concentrations (in micrograms per liter on a log10 scale)for the final model. The line of identity is also shown. (B) Conditional weightedresiduals versus time (log scale) for piperaquine final model.
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(median, 1.16 versus 1.42 �g · h/liter per mg/kg of PQ base; P �0.14). There was a significant positive correlation betweenAUC0 –� values and day 7 drug levels that did not reach signifi-cance (r � 0.70; 95%CI, 0.48 to 0.84; P � 0.001). Unlike AUC0 –�
values, day 7 levels were not significantly lower in those childrenwhose samples showed P. falciparum slide positivity (n � 8) com-pared to those whose samples did not (n � 26) (median, 44.1versus 48.0 �g/liter; P � 0.22). Similar results were evident whenthe two children from the DHA-PQ group whose samples devel-oped slide positivity for P. vivax by day 42 were included in theanalysis (data not shown). For the child who took �48 h to clear
initial parasitemia, the AUC0 –� values for ART and PQ werewithin the ranges of those of the other patients.
DISCUSSION
The development of ACTs has seen a variety of different combi-nations, formulations, and dose regimens introduced into clinicaluse without a detailed assessment of tolerability, safety, pharma-cokinetics, and efficacy. One recently marketed ACT, Artequick,appears to be relatively inexpensive to produce but uses compo-nent drugs that have not been investigated extensively, especiallyin a pediatric setting. The present pharmacokinetic and prelimi-
FIG 2 Visual predictive check showing observed 50th (�), 10th ({), and 90th (Œ) percentiles with simulated 95% CIs for the 50th (solid black line), 10th (graydotted lines), and 90th (dashed gray lines) percentiles for plasma piperaquine concentrations (micrograms per liter on a log10 scale) versus time (h) for Artequick(A) and Duo-cotecxin (B) from the final model. The observed data are superimposed as gray crosses. The inset shows data for the first 96 h.
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nary efficacy study in PNG children adds to the available data (30,37), suggesting that there would be benefits in extending the Arte-quick manufacturer’s recommended 2-day regimen to 3 days, asthis would increase PQ exposure and thus limit late recurrence ofparasitemia. However, the selection of a relatively low dose of ART(3 mg/kg of body weight versus the 10 to 20 mg/kg dose conven-tionally recommended), a drug that induces its own metabolism,may have implications for efficacy, especially in patients with lim-ited immunity to malaria or in geographical areas where artemis-inin resistance has started to develop (18).
Children in the DHA-PQ tetraphosphate group were given amean of 35.3 mg/kg PQ base over 3 days (29) compared with 38.3mg/kg PQ base over 2 days in the present children treated withART-PQ base. Overall, the exposures to PQ were similar for thetwo formulations, and no differences in the post hoc pharmacoki-netic parameters were identified. Although this suggests that thetablet and granule formulations have similar bioavailabilities and
that the small amount (2 g) of fat we administered with each dosewas unlikely to influence exposure to PQ, it is not possible todifferentiate the influences of food and formulation with the cur-rent study design. Two of three studies involving healthy adults
TABLE 3 Secondary pharmacokinetic parameters for piperaquinederived from post hoc Bayesian estimates for study participants and day7 plasma piperaquine levels
Parametera
Median [IQR]
P valuebPQ (Duo-cotecxin)n � 22
PQ (Artequick)n � 12
t½� (h) 4.44 [3.43–5.30] 4.52 [3.76–6.41] 0.48t½� (h) 36.1 [33.0–45.2] 35.3 [28.1–58.7] 0.82t½� (h) 513 [503–574] 512 [497–566] 0.82Day 7 plasma
piperaquine level(�g/liter)
39.3 [34.9–45.9] 42.0 [34.6–55.6] 0.56
AUC0–� (�g · h/liter) 49,451 [40,507–52,438] 44,556 [33,215–51,873] 0.36AUC0–� (�g · h/liter)/
total PQ dose(mg/kg)
1.27 [1.06–1.50] 1.37 [1.09–1.65] 0.40
a t½�, t½�, and t½� represent the first distribution half-live, second distribution half-live, and terminal elimination half-live, respectively.b Mann-Whitney U test.
TABLE 4 Final population pharmacokinetic estimates and bootstrapresults for artemisinin (n � 12)a
ParameterMean(RSE %)
Bootstrap median[95% CI]
Structural model parameterska (per h) 1.67 (55) 1.62 [1.01–4.40]CL/FART (liter/h/70 kg) 124 (12) 125 [99–157]VC/FART (liter/70 kg) 590 (30) 533 [318–874]Q/FART (liter/h/70 kg) 43.7 (38) 46.4 [19.5–79.4]VP/FART (liter/70 kg) 435 (26) 456 [259–696]
F2,ART � relative bioavailabilityof 2nd dose
0.270 (17) 0.275 [0.192–0.368]
Random model parametersIOV in FART (%) 43 (27) 39 [15–58]IIV in CL/FART (%) 12 (29) 12 [4–18]Residual variability (%) 33 (11) 32 [26–38]
a Parameters are ka (absorption rate constant), CL/FART (clearance), VC/FART (centralvolume of distribution), VP/FART (peripheral volume of distribution), Q/FART
(intercompartmental clearance between VP/FART and VC/FART), and F2,ART (relativebioavailability of 2nd dose of ART). RSE (relative standard error) values were calculatedfrom bootstrap results. OFV in final model, �63.562; bootstrap OFV (median [95%CI], �73.838 [�110.720 to 43.043].
FIG 3 (A) Population (Œ) and individual (�) predicted versus observedplasma artemisinin concentrations (micrograms per liter on a log10 scale) forthe final model. The line of identity is also shown. (B) Conditional weightedresiduals versus time for artemisinin final model.
FIG 4 Visual predictive check showing observed 50th (�), 10th (�), and 90th(Œ) percentiles with simulated 95% CIs for the 50th (solid black line), 10th(gray dotted lines), and 90th (dashed gray lines) percentiles for plasma arte-misinin concentrations (micrograms per liter on a log10 scale) versus time (h)from the final model. The observed data are superimposed as gray crosses.
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found that fat-containing foods increased exposure to PQ tetra-phosphate (31, 34, 41), but the volunteers in those studies con-sumed relatively large quantities of fat (17 to 54 g). Consistentwith the present results, 6.4 g of fat did not increase the exposureto PQ tetraphosphate in adults with malaria (4). However, PQbase is less water soluble than PQ tetraphosphate, and exogenouslipids are known to increase the solubility of lipophilic drugs andthus affect the extent of absorption (35). PQ base may behavesimilarly to lumefantrine, another highly lipophilic drug, and mayrequire a smaller amount of fat to maximize absorption (5). Gran-ule formulations have been reported to increase bioavailabilityrelative to tablets (15), which is consistent with the increased sur-face area available for dissolution compared to tablets, but ourdata suggest that this is not a major effect in the case of Artequick.Future studies evaluating the effect of food and drug formulationon the disposition of PQ base in malaria could help refine doseregimens for therapies employing drugs such as Artequick.
A model with three compartments and a transit sequence priorto absorption best represented the PQ concentration-time dataset. Most previous studies have used a two-compartment model(28, 31, 44). One study in healthy adults found that, although athree-compartment model represented the postadministrationprofile better, there were insufficient data to support its use over atwo-compartment model (38). The mean elimination t1/2, a pa-rameter influenced by the duration of sampling (45), was 512 h, avalue within the previously reported range of 224 to 667 h (1, 14,25, 28, 31, 34, 38, 44). Since there was substantial variability in theabsorption phase of the plasma PQ concentration profile, a transitcompartment model was tested and proved better than simplerabsorption models that used lag time, as has been found in studiesof other drugs (39).
It has recently been suggested that children should be given ahigher dose of PQ than adults due to lower day 7 plasma concen-trations (36) and reduced efficacy (27, 36, 49). This is supportedby comparative pharmacokinetic studies in children and adultsthat found that children had a higher clearance (28) or a lower PQexposure at critical times during the illness (44). These concernshave also been raised for other antimalarial drugs (8, 33) andreflect pharmacokinetic effects due to the effects of body size, mat-uration, and organ function (3). Although only children aged be-tween 5 and 10 years were included in the present study, we foundthat recurrence of parasitemia was associated with a lower PQAUC resulting from a lower milligram/kilogram dose, consistentwith other studies of DHA-PQ tetraphosphate (16). As PCR wasnot performed, these cases may represent either recrudescence
(treatment failure) or reinfection (failure of posttreatment pro-phylaxis).
The dose-adjusted PQ exposures in our children were similarto that found in Caucasian and Vietnamese healthy adults (14, 38,41) and Thai adults with malaria (4) but were from three to sixtimes lower than those found in studies of Vietnamese and Chi-nese healthy adults, respectively (25, 31), suggesting that there arePQ pharmacokinetic differences between populations. Currentlyrecommended PQ tetraphosphate doses are 18 mg/kg/day (10 mg/kg/day PQ base) for 3 days (48). A higher average daily dose of PQbase in the Artequick group (19 mg/kg/day) was well toleratedwhen given for 2 days, and the same dose given on the third daymight both satisfy WHO recommendations for duration of ACTand address the issue of the need for higher milligram/kilogramdoses in children.
The use of CQ as an internal standard for PQ is potentiallyproblematic for samples taken from an area of malaria endemicitywhere CQ is widely available and used empirically for treatment offever. Utilizing CQ usage and pharmacokinetic data from otherstudies in children from the Madang area (12, 29), we investigatedwhether the 14-day exclusion for prior antimalarial treatment wassufficient to limit such potential confounding. Even in the worst-case scenario, there was only a small effect on the estimated PKparameters, and that effect did not produce falsely significant dif-ferences between the results obtained for the two formulations.Although this is reassuring, it would be best if future similar stud-ies employed an alternative internal standard.
A number of published studies have evaluated the pharmaco-kinetics of ART in healthy adults (6, 7, 10, 19, 23, 43) and adultswith malaria (2, 9, 21, 22, 24, 26, 42), but only one has includedchildren with malaria (40). In the latter Vietnamese study, 23 chil-dren aged 2 to 12 years were given 5 days of ART dosed accordingto body weight (approximately 10 mg) and 31 adults received 500mg of ART daily for 5 days. Sparse sampling was used to charac-terize ART population pharmacokinetics in plasma by the use ofNONMEM after the first and final doses, with two samples col-lected from each patient on day 1 and a single sample collected onday 5 from some patients. A one-compartment model was used,with clearance and volume terms for children and adults esti-mated separately. The median weights and ages of the childrenwere lower in the present study (18.3 versus 20 kg and 7.1 versus 9years, respectively). Although our value for ka was comparable tothat in the Vietnamese study (2.0/h versus 1.7/h), a two-compart-ment model provided a better fit in the present study, with distri-bution and elimination t1/2s of 1.9 h and 8.3 h, respectively, com-pared to a t1/2 of 1.8 h in the previous study (40). This differencemay reflect the longer sampling duration in the present study(24 h versus 8 h postdose), which enabled the identification of asecond exponential phase in the elimination of ART.
The elimination t1/2 of ART has been reported to be between1.4 and 4.8 h in noncompartmental (2, 6, 7, 9, 21, 22, 26, 42, 43)and compartmental (10, 19, 23, 24, 40) analyses. The present anal-ysis supports a biexponential disposition for ART, while most pre-vious compartmental analyses have reported a monoexponentialdisposition. A shorter sampling duration may be responsible forthis difference, as sampling was confined to �10 h after the lastdose in all but one of the studies reported to date. In addition,assay sensitivity may also contribute by limiting quantification ofART to those samples taken �12 h after dose administration (10,19). One study of healthy adults given a single dose of ART (6) also
TABLE 5 Secondary pharmacokinetic parameters for artemisininderived from post hoc Bayesian estimates for study participants
Parametera
ART (Artequick) values(median [IQR])(n � 12)
t½� (h) 1.55 [1.49–1.60]t½� (h) 7.43 [7.22–7.68]AUC (�g · h/liter) � first dose 1,347 [1,065–1,594]AUC (�g · h/liter) � second dose 312 [253–438]AUC0–� (�g · h/liter) � total 1,652 [1,333–2,177]a t½� and t½� represent the distribution half-life and terminal elimination half-life,respectively. AUCs for each dose were calculated using the standard pharmacokineticsformula to determine the relative contribution of each dose to the total AUC0 –� value.
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reported a biexponential disposition and found longer distribu-tion (2.61 h versus 1.55 h) and shorter elimination (4.34 h versus7.43 h) t1/2s compared to those determined in the present study.Although this study sampled blood to 24 h postdose, ART couldbe quantified in samples only up to 8 h.
The present median AUC0 –� of the first dose of ART (1,347�g · h/liter) was within the range reported for healthy adults(1,190 to 2,690 �g · h/liter) (6, 7, 10, 19, 43) but well below that ofadults with malaria (2,601 to 2,780 �g · h/liter) (2, 9, 26) whoreceived 500 mg of ART. Our children received a lower dose ofART (3.2 mg/kg/day), and, when adjusted for the relative doseadministered, the AUC0 –� for the first dose was above those seenin adults (2, 9, 26). The autoinduction of ART metabolism hasbeen well characterized, with a primary effect on the bioavailabil-ity of subsequent doses rather than on systemic clearance (23). It islikely, therefore, that this represents an increase in the activity ofgut wall rather than liver metabolism.
We found a difference in the PK of ART for the second dosethat was explained by a lower relative bioavailability of 0.27 com-pared to the first dose. In comparisons of the AUCs of differentdoses in previous studies, the relative bioavailability after 4 to 7days was between 0.13 and 0.29 (7, 9, 22, 26, 40, 43). One study ofAfrican adults with malaria who received 500 mg of ART daily for3 days and a single dose of mefloquine (42) measured ART insaliva and found that relative bioavailability was lower (0.45) onlyon the third day when mefloquine was given after the last dose ofART. However, when mefloquine was given on the first day, at thesame time as the first dose of ART, the relative bioavailabilities ofboth the second and third doses of ART were lower, at 0.23 and0.25, respectively.
Our data are in agreement with a rapid mean autoinductiontime of 1.9 h as estimated using a semiphysiological model forART (23), indicating that all doses after the first had a lower rela-tive bioavailability. If a third daily dose of ART-PQ base were to begiven, its relative bioavailability would also be low. The rapid ini-tial parasite clearance in Artequick-treated children seen in thepresent study despite relatively low and short-lived plasma ARTconcentrations may reflect the level of malarial immunity in thisgeographical area of intense transmission (17). It is likely thatrelatively low ART doses, even if given over 3 days rather than 2,would not be as effective where transmission and consequent im-munity are less or where artemisinin resistance has started to de-velop (18).
Compared to 3 days of DHA-PQ tetraphosphate administra-tion, the efficacy of 2 days of administration of Artequick in adultswas equivalent in one study (46) and inferior in another (37). A3-day Artequick regimen (3.2 and 16.0 mg/kg/day of ART and PQbase, respectively) has been found to be both well tolerated andmore effective than a 2-day regimen (30). Our preliminary datasuggest that the efficacy of 2 days of Artequick administrationappeared similar to that of 3 days of Duo-cotecxin administrationin PNG children. However, the weight of evidence from previousstudies (30, 37), the low proportion of ART in Artequick and itsautoinduction at a time when the specter of artemisinin resistancehas emerged (18), and the issue of potential PQ underdosing inchildren all support further evaluation of a theoretically more ef-ficacious 3-day Artequick regimen, as recommended by the WHOfor all ACTs (48).
ACKNOWLEDGMENTS
We are most grateful to Valsi Kurian and the staff of Alexishafen HealthCentre for their kind cooperation during the study. We also thank JovithaLammey, Christine Kalopo, and Bernard (“Ben”) Maamu for clinicaland/or logistic assistance. Harin Karunajeewa is acknowledged for hispivotal role in coordinating the original DHA/PQ tetraphosphate study.We thank Artepharm Co. Ltd. for kind provision of Artequick.
The National Health and Medical Research Council (NHMRC) ofAustralia funded the study (grant 634343). T.M.E.D. is supported by anNHMRC Practitioner Fellowship.
We have no conflicts of interest to declare.
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37. Pyar KP, Myint WW, Kyaw MP, Zin T, Than M. 2009. Efficacy andsafety of artemisinin-piperaquine (Artequick) compared to dihydroarte-misinin-piperaquine (Artekin) in uncomplicated falciparum malaria inadults. Myanmar Health Sci. Res. J. 21:78 – 82.
38. Röshammar D, Hai TN, Friberg Hietala S, Van Huong N, Ashton M.2006. Pharmacokinetics of piperaquine after repeated oral administrationof the antimalarial combination CV8 in 12 healthy male subjects. Eur. J.Clin. Pharmacol. 62:335–341.
39. Savic RM, Jonker DM, Kerbusch T, Karlsson MO. 2007. Implementa-tion of a transit compartment model for describing drug absorption inpharmacokinetic studies. J. Pharmacokinet. Pharmacodyn. 34:711–726.
40. Sidhu JS, et al. 1998. Artemisinin population pharmacokinetics in chil-dren and adults with uncomplicated falciparum malaria. Br. J. Clin. Phar-macol. 45:347–354.
41. Sim IK, Davis TM, Ilett KF. 2005. Effects of a high-fat meal on the relativeoral bioavailability of piperaquine. Antimicrob. Agents Chemother. 49:2407–2411.
42. Svensson US, Alin H, Karlsson MO, Bergqvist Y, Ashton M. 2002.Population pharmacokinetic and pharmacodynamic modelling of arte-misinin and mefloquine enantiomers in patients with falciparum malaria.Eur. J. Clin. Pharmacol. 58:339 –351.
43. Svensson US, et al. 1998. Artemisinin induces omeprazole metabolism inhuman beings. Clin. Pharmacol. Ther. 64:160 –167.
44. Tarning J, et al. 2008. Population pharmacokinetics of piperaquine aftertwo different treatment regimens with dihydroartemisinin-piperaquine inpatients with Plasmodium falciparum malaria in Thailand. Antimicrob.Agents Chemother. 52:1052–1061.
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46. Trung TN, Tan B, Van Phuc D, Song JP. 2009. A randomized, controlledtrial of artemisinin-piperaquine vs dihydroartemisinin-piperaquinephosphate in treatment of falciparum malaria. Chin. J. Integr. Med. 15:189 –192.
47. World Health Organization Communicable Diseases Cluster. 2000.Severe falciparum malaria. Trans. R. Soc. Trop. Med. Hyg. 94(Suppl. 1):S1–S90.
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Artemisinin-Naphthoquine Combination Therapy for UncomplicatedPediatric Malaria: a Pharmacokinetic Study
Kevin T. Batty,a,b Sam Salman,c Brioni R. Moore,c John Benjamin,d Sook Ting Lee,c Madhu Page-Sharp,a Nolene Pitus,d
Kenneth F. Ilett,c Ivo Mueller,e,f Francis W. Hombhanje,g Peter Siba,d and Timothy M. E. Davisc
School of Pharmacy, Curtin University, Bentley, Western Australia, Australiaa; Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia,Australiab; School of Medicine and Pharmacology, University of Western Australia, Crawley, Western Australia, Australiac; Papua New Guinea Institute of Medical Research,Madang, Papua New Guinead; Infection and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Victoria, Australiae; Center de Recerca en SalutInternacional de Barcelona (CRESIB), Barcelona, Spainf; and Centre for Health Research, Divine Word University, Madang, Papua New Guineag
Artemisinin-naphthoquine (ART-NQ) is a coformulated antimalarial therapy marketed as a single-dose treatment in Papua NewGuinea and other tropical countries. To build on limited knowledge of the pharmacokinetic properties of the components, espe-cially the tetra-aminoquinoline NQ, we studied ART-NQ disposition in Papua New Guinea children aged 5 to 12 years with un-complicated malaria, comparing a single dose (15 and 6 mg/kg of body weight) administered with water (group 1; n � 13), a sin-gle dose (22 and 9 mg/kg) with milk (group 2) (n � 17), and two daily doses of 22 and 9 mg/kg with water (group 3; n � 16). Theplasma NQ concentration was assayed by high-performance liquid chromatography, and the plasma ART concentration wasassayed using liquid chromatography-mass spectrometry. Population-based multicompartment pharmacokinetic models forNQ and ART were developed. NQ disposition was best characterized by a three-compartment model with a mean absorptionhalf-life (t1/2) of 1.0 h and predicted median maximum plasma concentrations that ranged as high as 57 �g/liter after the seconddose in group 3. The mean NQ elimination t1/2 was 22.8 days; clearance relative to bioavailability (CL/F) was 1.1 liters/h/kg; andvolume at steady state relative to bioavailability (Vss/F) was 710 liters/kg. Administration of NQ with fat (8.5 g; 615 kJ) versuswater was associated with 25% increased bioavailability. ART disposition was best characterized by a two-compartment modelwith a mean CL/F (4.1 liters/h/kg) and V/F (21 liters/kg) similar to those of previous studies. There was a 77% reduction in thebioavailability of the second ART dose (group 3). NQ has pharmacokinetic properties that confirm its potential as an artemisininpartner drug for treatment of uncomplicated pediatric malaria.
Available data relating to the pharmacokinetics of the antima-larial drug naphthoquine phosphate (NQ) are limited and
inconsistent. Initial reports suggested that NQ has a high oralbioavailability (�90%) and a half-life (t1/2) of 41 to 57 h (50). In amore recent study with healthy Chinese men in which NQ wasgiven alone or coformulated with artemisinin (ART) (41), theelimination t1/2 of NQ was substantially longer, at 250 to 300 h.This volunteer study also showed that the area under the concen-tration-time curve (AUC) for NQ exhibited an unusual relation-ship between the formulation and coadministered fat. The meanvalues were similar for the fasted group receiving NQ mono-therapy and the fed group receiving combination ART-NQ ther-apy but more than double this for the fasted volunteers given thefixed combination (41). The fact that the highest bioavailabilitywas in the fasting state appears in contrast to the effect of fat on theabsorption of related drugs, such as lumefantrine and piperaquine(3, 11, 24, 46), while the apparently beneficial effects of coformu-lation on bioavailability were difficult to explain (41).
It has been shown that NQ is a P-glycoprotein substrate andthat NQ efflux is saturable (12), suggesting that absorption couldbe nonlinear at high doses. However, the Chinese volunteer studyof ART-NQ found dose-proportional increases in the maximumconcentration in plasma (Cmax) and AUC for NQ at doses between200 and 600 mg (41). The maximum individual value for Cmax wasjust over 100 �g/liter in this study (41), but a Cmax as high as 245�g/liter has been reported after a 600-mg dose in adults (50).
The Chinese volunteer study of NQ and ART-NQ reported at1/2 for ART of 3.6 to 4.0 h, a clearance relative to bioavailability(CL/F) of approximately 1.5 liters/h/kg, and a volume of distribu-
tion relative to bioavailability (Vz/F) of 8 liters/kg (41). In con-trast, a number of previous studies with healthy adult volunteers(4, 16, 18, 26) and patients with uncomplicated Plasmodium fal-ciparum malaria (1, 5, 15, 25, 45) have reported lower mean valuesfor t1/2 (2.0 to 2.7 h [mean, 2.3 h]), higher mean values for CL/F(5.1 to 9.3 liters/h/kg [mean, 6.7 liters/h/kg]), and a higher meanV/F (16.4 to 35.5 liters/kg [mean, 27 liters/kg]). The reportedmean CL/F and V/F for ART in children were even greater, at 14.4liters/h/kg and 38 liters/kg, respectively (45). The Chinese studydid, however, show that the AUC for ART increased with coad-ministered fat (41), consistent with most past reports (14).
Because of inconsistencies between the few published studiesof NQ pharmacokinetics and a lack of pharmacokinetic data forchildren, we conducted two pharmacokinetic studies of ART-NQin children from Papua New Guinea with uncomplicated malaria.An initial pilot study (study 1), carried out before the manufac-turer had produced a pediatric dosing schedule and utilizing aconservative dose regimen (calculated in milligrams per kilogramof body weight based on the dose for adults), was designed toprovide preliminary pharmacokinetic data relating to NQ dispo-
Received 29 November 2011 Returned for modification 3 January 2012Accepted 1 February 2012
Published ahead of print 13 February 2012
Address correspondence to Timothy M. E. Davis, [email protected].
Copyright © 2012, American Society for Microbiology. All Rights Reserved.
doi:10.1128/AAC.06250-11
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sition in children, while the second study (study 2) aimed to char-acterize the pharmacokinetics of NQ as well as ART in more detailwhen these drugs were given at the manufacturer’s recommendeddose with fat (milk) or as a two-dose regimen.
MATERIALS AND METHODSPatients and clinical methods. Full details of the studies have been pro-vided in a separate report (7). In brief, children aged 5 to 10 years whopresented with an axillary temperature of �37.5°C or a history of fever inthe previous 24 h and who were slide positive for malaria (�1,000 asexualP. falciparum parasites/�l of whole blood or �250 Plasmodium vivax par-asites/�l) were eligible provided that they had no complications or con-comitant illness, no prior treatment with study drugs, and no history ofallergy to ART or aminoquinoline drugs. Each child’s parents or guard-ians gave written informed consent. Approvals were obtained from thePapua New Guinea Institute of Medical Research Institutional ReviewBoard and the Medical Research Advisory Committee of the Papua NewGuinea Health Department.
At enrollment, a history was taken and a full physical examination wasperformed. An intravenous cannula was inserted, and a baseline venousblood sample was drawn. In study 1, all children were administered ARCOtablets (50 mg NQ plus 125 mg ART; Kunming Pharmaceuticals, Kun-ming, China) orally as a single dose of 2 to 4 whole tablets with water(group 1). The dose was based on body weight according to those recom-mended by the manufacturer in milligrams per kilogram for adults (33)and represented dose ranges of 5.0 to 7.5 mg/kg for NQ phosphate and12.5 to 16.8 mg/kg for ART. Subjects were not required to fast prior to, orafter, treatment. If the child vomited within 1 h, the same dose was read-ministered and the time of readministration recorded.
In study 2, children were randomized by a computer-generated se-quence to receive ARCO tablets (50 mg NQ plus 125 mg ART) orally basedon body weight as recommended by the manufacturer for children (33) aseither (i) a single dose of 3 to 6 tablets given with 250 ml full-cream cow’smilk (containing 8.5 g fat) with dose ranges of 6.1 to 9.5 mg/kg for NQ and15.3 to 23.8 mg/kg for ART (group 2) or (ii) the same dose given withwater on two occasions 24 h apart (group 3). Each child was kept fastingunder observation, and if he/she vomited within 1 h, the same dose wasreadministered and the time of readministration recorded.
Group 1 patients had additional venous blood samples drawn throughthe cannula at 1, 2, 4, 8, 12, 18, 24, 48, and 72 h and by venesection at 4, 7,14, 28, 42, and 56 days. Blood was collected into lithium heparin tubes andwas centrifuged at 1,800 � g for 5 min, and the separated plasma wasstored at �80°C until analysis for the NQ concentration within 8 monthsof collection. These children were reassessed clinically at 4 and 24 h and ondays 2, 3, 7, 14, 28, and 56 (7). Group 2 and 3 patients had further 2.5-mlblood samples for drug assay taken at 1, 2, 4, 8, 12, 18, 24, 48, and 72 hthrough the sampling cannula and by venesection at 4, 7, 14, 28, and 42days. Group 3 patients received a second ART-NQ dose with water at 24 h.Posttreatment clinical and other monitoring for groups 2 and 3 was sim-ilar to that performed for group 1 (7).
Analytical methods. Naphthoquine diphosphate was obtained fromZYF Pharm Chemicals, Shanghai, China; tramadol hydrochloride andartemisinin were from Sigma-Aldrich Chemicals, St. Louis, MO; and arte-mether was from AApin Chemicals Ltd., Abingdon, Oxon, United King-dom. All general laboratory chemicals were of analytical grade (Sigma-Aldrich Chemicals, St. Louis, MO; Merck Chemicals, Darmstadt,Germany).
The concentration of NQ in plasma was analyzed using a validatedhigh-performance liquid chromatography (HPLC) assay, based on estab-lished analytical methods for chloroquine, piperaquine, and mefloquine(13, 30). Briefly, plasma samples (500 �l) were spiked with tramadol as theinternal standard (500 ng), alkalinized with a 2% (wt/vol) sodium tet-raborate solution (1 ml), and extracted into 8 ml hexane– ethyl acetate(80:20) by shaking for 10 min. The samples were then centrifuged at1,300 � g for 10 min. The supernatant (7.5 ml) was back-extracted into
0.1 ml of 0.1 M HCl by shaking for 5 min, followed by centrifugation asdescribed above. The HCl layer was transferred to 1.5-ml microcentrifugetubes and was recentrifuged at 1,300 � g for 25 min to evaporate traces oforganic solvent, after which 70 �l was injected onto the HPLC. Analyteswere separated on a Luna C18 HPLC column (length, 100 mm; innerdiameter [i.d.], 4.6 mm; particle size, 3 �m; Phenomenex, Australia) inseries with an octadecyl C18 guard column (length, 4 mm; i.d., 3 mm;Phenomenex, Australia) at 30°C with a mobile phase of 18% (vol/vol)acetonitrile in 50 mM KH2PO4 buffer (pH 2.5) pumped at 1 ml/min. Theapproximate retention times (tR) for NQ and tramadol were 9.4 min and6.8 min, respectively, and the analytes were detected by UV absorbance at222 nm (Fig. 1). The linear calibration range for each assay was 1 to 100�g/liter, and quality control (QC) samples (5 �g/liter, 20 �g/liter, and 100�g/liter) were included in each batch. The intraday relative standard de-viations (RSDs) of NQ were 8.9, 3.1, and 4.5% at 5 �g/liter, 20 �g/liter,and 100 �g/liter, respectively (n � 5), while interday RSDs were 7.7, 5.2,and 3.4% at 5 �g/liter, 20 �g/liter, and 100 �g/liter, respectively (n � 15).Intraday and interday accuracy ranges were 92 to 106% and 91 to 98%,respectively. The limit of quantification and limit of detection were 1�g/liter and 0.5 �g/liter, respectively. Mean levels of recovery of NQ fromplasma were 88%, 98%, and 96% at 5 �g/liter, 20 �g/liter, and 100 �g/liter, respectively.
The concentration of ART in plasma was analyzed using liquid chro-matography with mass spectrometry detection (LC-MS) based on an es-tablished assay for artemether (36). Stock solutions of ART and arte-mether (the internal standard) were prepared separately (1 g/liter inmethanol) and were stored in the dark at �80°C. Working standard so-lutions were prepared from the primary stock at 1, 10, and 100 mg/liter.Two sets of 5-point calibration curves were constructed (5 to 200 �g/literfor the lower concentrations and 200 to 2,000 �g/liter for the higher con-centrations) by spiking into blank plasma. Samples above the standardcurve were reanalyzed following appropriate dilution. QC samples wereprepared in blank plasma as described above at concentrations of 5, 200,and 1,000 �g/liter and were stored at �80°C prior to use for each batchanalyzed.
The extraction procedure used a 1-ml C18 solid-phase extraction(SPE) column (Bond Elut PH; Varian Inc., Palo Alto, CA) as describedpreviously (6), with minor modifications. Briefly, the SPE column waspreconditioned with 1 ml of methanol followed by 1 ml of 1 M acetic acid.Plasma samples (0.5 ml) were spiked with the internal standard (arte-mether; 1 �g), loaded onto the preconditioned SPE column, and drawnthrough by using a medium vacuum. The column was then washed with 1M acetic acid (1 ml; two washes), followed by 20% (vol/vol) methanol in1 M acetic acid (1 ml). The column was dried under a low vacuum for 30min, and retained drugs were eluted with 2 ml of t-butyl chloride– ethylacetate (80:20%, vol/vol). The eluate was evaporated in a vacuum evapo-rator at 35°C and was reconstituted in 50 �l of the mobile phase, and 5-�laliquots were injected into the LC-MS system.
The single-quadrupole LC-MS system (model 2020; Shimadzu,Kyoto, Japan) comprised a binary pump (20AD), a vacuum degasser, anautosampler with a thermostat (SIL-20AC HT), a column compartmentwith a thermostat (CTO 20A), a photodiode detector (SPD M 20A), and amass analyzer (MS 2020) with both electrospray ionization (ESI) andatmospheric pressure chemical ionization (APCI) systems. Analysis wasperformed in the isocratic mode with 20 mM ammonium formate (pH4.8)–methanol (20:80) at a flow rate of 0.2 ml/min. Chromatographicseparation was undertaken at 30°C on a Synergy Fusion-RP C18 column(length, 150 mm; i.d., 2 mm; particle size, 4 �m) coupled with a C18 guardcolumn (length, 4 mm; i.d., 2 mm; particle size, 5 �m; Phenomenex,Australia). The retention times were 4.3 min and 7.9 min for ART andartemether, respectively (Fig. 2). Optimized mass spectra were acquiredwith an interface voltage of 4.5 kV, a detector voltage of 1 kV, a heat blocktemperature of 400°C, and a desolvation gas temperature of 250°C. Nitro-gen was used as the nebulizer gas at a flow rate of 1.5 liter/min and a dry gasflow of 10 liter/min.
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Both ART and artemether standard solutions were first scannedfrom m/z 100 to 500 in ESI and APCI positive mode, as well as in thecombined ESI-and-APCI (DUIS) mode, to identify the abundance ofions corresponding to the respective drugs. The base peak intensities of
all three modes were compared and showed that the DUIS mode gavethe highest signal intensity. Therefore, quantitation was performed byselected ion monitoring (SIM) using the DUIS mode. For ART, theparent molecule [M � H]� (m/z 283) was used for quantitation, while
FIG 1 HPLC-UV (222 nm) chromatograms showing naphthoquine (N) (tR, 9.4 min) and the internal standard, tramadol (T) (tR, 6.8 min). (A) Spiked plasmaused in the calibration curve (20 �g/liter naphthoquine); (B) a patient’s blank predose sample (with the internal standard) showing no endogenous interference;(C) a typical sample (25 �g/liter naphthoquine).
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for artemether, the predominant fragmented ion (m/z 221) was mon-itored (44).
All standard curves were linear (r2, �0.999). Chromatographic data(peak area ratio of ART to artemether) were processed using LAB Solution(version 5; Shimadzu, Japan). Responses from the analysis of three ARTconcentrations (5, 200, and 2,000 �g/liter) spiked into five separate
plasma samples were used to determine matrix effects (ion suppression/enhancement), absolute recovery, and process efficiency (37, 43). Threesets of matrix solutions were prepared. Set 1 comprised blank plasmaspiked first and then extracted; set 2 comprised blank plasma extractedfirst and then spiked; and set 3 comprised pure solutions of the analyte.The matrix effect, process efficiency, and absolute recovery were ex-
FIG 2 LC-MS chromatograms showing artemisinin (ART) (tR, 4.3 min) and the internal standard (IS), artemether (tR, 7.9 min). (A) Spiked plasma used in thecalibration curve (200 �g/liter artemisinin); (B) a patient’s blank predose sample (with the IS) showing no endogenous interference; (C) a typical sample (136�g/liter artemisinin).
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pressed as percentages. The matrix effect was calculated as (set 2 re-sponse � 100)/(set 3 response), the process efficiency as (set 1 response �100)/(set 3 response), and the absolute recovery as (set 1 response �100)/(set 2 response). The mean matrix effects � standard deviations(SD) for ART were 94% � 12% (range, 79 to 105%), 90% � 13% (range,75 to 106%), and 91% � 2% (range, 88 to 92%) at 5, 200, and 2,000�g/liter, respectively. The mean process efficiencies � SD for ART were93% � 18% (range, 73 to 121%), 84% � 11% (range, 75 to 102%), and82% � 4% (range, 80 to 89%) at 5, 200, and 2,000 �g/liter, respectively.The mean absolute recoveries � SD for ART were 88% � 10% (range, 78to 101%), 86% � 9% (range, 77 to 102%), and 90% � 7% (range, 87 to101%) at 5, 200, and 2,000 �g/liter, respectively. The mean matrix effect,process efficiency, and absolute recovery � SD for the internal standard,artemether, were 98% � 10% (range, 87 to 113%), 88% � 4% (range, 82to 92%), and 91% � 6% (range, 81 to 96%) at 1,000 �g/liter. The intradayRSDs for the assay were 9.3, 7.2, and 3.7% at 5, 200, and 2,000 �g/liter,respectively (n � 5), while the interday RSDs were 9.5, 7.1, and 6.5% at 5,200, and 2,000 �g/liter, respectively (n � 15). Interday accuracies deter-mined from the QC samples for each assay batch at 5, 200, and 1,000�g/liter were 108% � 7% (range, 86 to 114%), 103% � 6% (range, 93 to109%), and 107% � 8% (range, 86 to 115%), respectively (n � 16). Thelimits of quantification and detection for ART were 2.5 and 1 �g/liter,respectively.
Pharmacokinetic and statistical analyses. The pharmacokineticproperties of NQ were assessed using noncompartmental analysis (Ki-netica, version 4.4.1; Thermo LabSystems Inc., Philadelphia, PA) forgroup 1 subjects, and the data (not shown) were used to refine the studydesign for groups 2 and 3. All NQ data were subsequently pooled andanalyzed by population pharmacokinetic methods, as were ART data,which were available for groups 2 and 3.
In the population pharmacokinetic analysis, loge concentration-timedata sets for NQ and ART were analyzed by nonlinear mixed-effect mod-eling using NONMEM (version 6.2.0; Icon Development Solutions, Elli-cott City, MD) with an Intel Visual Fortran (version 10.0) compiler. NQdata were available for all three groups, while ART data were availableonly for groups 2 and 3. Linear mammillary model subroutines withinNONMEM, first-order conditional estimation (FOCE) with �-� interac-tion, and the objective function value (OFV; a NONMEM-calculatedglobal goodness-of-fit indicator equal to �2 times the log of the likeli-hood) were used to construct and compare plausible models. Unless oth-erwise specified, a difference in the OFV of �3.84 (�2 distribution with 1df; P � 0.05) was considered statistically significant. Secondary pharma-cokinetic parameters, including the volume of distribution at steady-state(Vss; calculated as the sum of volumes of individual compartments), thearea under the curve from 0 h to infinity (AUC0 –�), and the eliminationt1/2 for the participants, were obtained from post hoc Bayesian predictionin NONMEM using the final model parameters. Macro constants for thethree-compartment model were calculated from the modeled parametersusing previously published equations (49). Cmax and the time to Cmax
(Tmax) were estimated by predicting the concentrations of NQ and ARTfor each individual at 6-min intervals to capture the postdose peak.
Allometric scaling to a standard adult body weight (WT) was used apriori with all volume terms scaled using �(WT/70)1.0 and all clearanceterms scaled using �(WT/70)0.75 (2). Between-subject variability (BSV)was added to parameters for which it could be estimated from the avail-able data. An additive error model was used for residual unexplainedvariability (RUV), approximating proportional error as loge concentra-tion data were used. In the development of the final models, the influenceof the following covariates on the various model parameters was investi-gated: dose group, dose occasion, relative dose (in milligrams per kilo-gram), gender, spleen grade, malaria status (by slide positivity), baselinelog10 parasitemia, age, fever, and initial hemoglobin concentration. Covariaterelationships identified using the generalized additive modeling procedurewithin Xpose (29) and by inspection of correlation plots of � versus a cova-riate were evaluated within NONMEM. The potential effect of these cova-riates, particularly the dose group and occasion, on bioavailability wasalso considered in cases where similar relationships was identified for allvolume and clearance terms, given that these were relative to bioavailabil-ity. The effect size (expressed as a percentage) of categorical data wasassessed, while both linear and power relationships were evaluated forcontinuous covariates. Linear relationships were calculated as follows: indi-vidual parameter value � population parameter value � {1 � effect param-eter � [(covariate value for individual) � (median value of covariate)]}.Power relationships were calculated as follows: individual parametervalue � population parameter value � [(covariate value for individual)/(median value of covariate)effect parameter]. A stepwise forward inclusionand backward elimination method with a significance level (P value) of�0.05, accompanied by a decrease in the BSV of the parameter, was re-quired for the inclusion of a covariate relationship, and a P value of �0.01was required to retain a covariate relationship. For relationships involvingbioavailability, a decrease in the BSV of any volume or clearance wasrequired. Correlations among BSV terms were also investigated, and con-ditional weighted residuals (CWRES) plots were assessed in arriving at afinal model. Two- and three-compartment models for NQ and one- andtwo-compartment models for ART were compared with first-order ab-sorption, with and without a lag time.
A bootstrap procedure in Perl-speaks-NONMEM (PsN), stratified ac-cording to dose group and weight, was used to sample individuals fromthe original data set and to generate 1,000 new data sets that were subse-quently analyzed using NONMEM. The resulting parameters were thensummarized as the median and 2.5th and 97.5th percentiles (95% empir-ical confidence interval [CI]) to facilitate evaluation of the final modelparameter estimates. In addition, prediction-corrected visual predictivechecks (pcVPCs) (9) were performed using PsN with 1,000 replicate datasets simulated from the original data set. The observed 10th, 50th, and90th percentiles were plotted with their respective simulated 95% confi-dence intervals to assess the predictive performance of the model (9)Because a number of covariate effects were found in the model-buildingprocess for NQ, numerical predictive checks (NPCs), stratified accordingto those covariates, were performed and were assessed by comparing theactual with the expected number of data points within the 20, 40, 60, 80,90, and 95% prediction intervals (PI).
TABLE 1 Demographic data for children given artemisinin-naphthoquine for the treatment of uncomplicated P. falciparum malaria
Characteristica Group 1 Group 2 Group 3
No. of children 13 17 16Gender 6 male, 7 female 11 male, 6 female 12 male, 4 femaleAge (yr) 7.1 � 1.8 7.7 � 2.0 6.7 � 1.6Wt (kg) 18.0 � 3.7 18.9 � 5.2 16.8 � 3.2Ht (cm) 110 � 10 117 � 12 110 � 9Parasitemia (no. of parasites/�l of blood)b upon admission 14,757 (5,189–41,966) 6,674 (2,264–19,674) 29,416 (12,290–70,406)Naphthoquine dose (mg/kg) 6.3 � 0.9 8.8 � 1.4 2 � (9.5 � 0.9)Artemisinin dose (mg/kg) 15.7 � 2.3 22.0 � 3.6 2 � (23.8 � 2.2)a Data are means � SD unless otherwise indicated.b Geometric mean (95% confidence interval) for children with parasitemia.
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Data analysis and representation were performed using SigmaPlot,version 11 (Systat Software Inc., San Jose, CA). Data are means � SDunless otherwise indicated. Student’s t test (for parametric data) or theMann-Whitney U test (for nonparametric data) was used for two-samplecomparisons as appropriate with a significance level (P value) of �0.05.
RESULTS
Thirteen of 15 group 1 children completed all essential require-ments for the pharmacokinetic component of the study. All ofthese children had P. falciparum infections at baseline, and onehad a mixed infection with P. vivax at low density (160 para-sites/�l of blood). Four children in group 2 and four in group 3were considered to have low-grade parasitemia on screening mi-croscopy at the study site but were subsequently found to be slidenegative on confirmatory expert microscopy. All group 2 and 3children recruited were included in the pharmacokinetic study.Demographic data are summarized in Table 1.
The content of NQ in the ARCO tablets was determined bydissolving each tablet (n � 5) in 500 ml water by using sonication(twice, for 5 min each time) and measuring the concentrations in8 aliquots. The ART content was determined after dissolving eachtablet (n � 6) in 250 ml methanol and following the same proce-dure. The mean NQ and ART contents of the ARCO tablets usedin the study were 49 � 5 mg (nominal potency, 50 mg NQ) and129 � 3 mg (nominal potency, 125 mg ART), respectively.
Naphthoquine pharmacokinetics and pharmacodynamics.The plasma concentration-time profiles for NQ are shown in Fig.3. For pooled data from the three groups, a three-compartmentmodel proved superior to a two-compartment model, with alower OFV (�404.855 versus �388.736; P, �0.01) and no bias inthe CWRES plot in the initial stages of modeling. Because therewas no evidence of model misspecification by use of a three-com-partment model with first order-absorption with a lag time, more-complex models were not tested. The structural model parameters(where C refers to the central compartment and P1 and P2 to thetwo peripheral compartments) were the absorption rate constant(ka), lag time, CL/F, VC/F, VP1/F, VP2/F, and Q1/F and Q2/F (in-tercompartment clearances between VP1/F and VC/F and betweenVP2/F and VC/F, respectively). Estimates for the BSV of ka, VC, VP2,CL, and Q2 and the correlation between some BSV pairs (ka andVC/F, VC/F and CL/F, CL/F and VP2/F, and VP2/F and Q2/F) couldbe obtained (Table 2). Significant covariate relationships wereadded in the following order [given as covariate–parameter (rela-tionship type)]): fever–predicted F (negative categorical), firstdose for group 3–predicted F (negative categorical), and hemoglo-bin–VC/F (positive linear). Although children in group 2 wereestimated to have an approximately 50% lower ka, this relation-ship did not satisfy the significance requirements for inclusion inthe final model (0.01 � P � 0.05). Fever (axillary temperature,�37.3°C) and the first NQ dose in group 3 were associated with32% and 26% decreases in bioavailability, respectively. Every1-g/dl increase in the hemoglobin level increased VC/F by 16%.Slide positivity at baseline and log10 parasitemia were not signifi-cant covariates in the model. The residual error for the model was24% (Table 2).
Goodness-of-fit and CWRES plots for NQ are shown in Fig. 4.The results of the parameter estimates and the bootstrap resultsare summarized in Table 2, and post hoc Bayesian parameter esti-mates with derived secondary pharmacokinetic parameters aregiven in Table 3. The bootstrap demonstrated reasonable esti-
mates of structural and covariate effect parameters with a bias of�10% for all parameters except VP1, for which the bias was 19%.Random parameters had a bias of �7%. The AUC was signifi-cantly higher in group 3 (two doses) than in groups 1 and 2 (P �0.001) and was higher in group 2 than in group 1. The predictedCmax was �200 �g/liter for all children, apart from one group 3child with a value of 270 �g/liter after the second dose. When theAUC was normalized for the total relative dose (in milligrams per
FIG 3 Concentration-time plots for NQ in plasma for group 1 (A), group 2(milk) (B), and group 3 (water and double dose) (C) patients. (Insets) Plasmaconcentration-time data from 0 to 100 h after the dose.
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kilogram), there was no longer any significant difference betweenthe groups. The pcVPC for NQ, shown in Fig. 5, demonstrate thereasonable predictive performance of the model. NPCs stratifiedaccording to dose group (three strata), hemoglobin (three strata),and fever (two strata) showed good predictive performance, withthe expected number of data points above and below most predic-tion intervals (data not shown).
Since the dose-corrected pharmacokinetic parameters wereconsistent across the three groups (Table 3), data were pooled toprovide estimates for the total of 46 patients. Overall, the mean �SD CL/F, Vss/F, t1/2�, t1/2�, and t1/2� for NQ were 1.30 � 0.45liters/h/kg, 805 � 256 liters/kg, 8.2 � 3.8 h, 98 � 16 h, and 518 �94 h, respectively.
Of the 13 (of 15) group 1 patients included in the pharmaco-kinetic analysis, 7 developed recurrent parasitemia during the 42-day follow-up period (7). One had a PCR-confirmed recrudes-cence of P. falciparum; four had reinfections with P. falciparum;and two had an emergence of P. vivax. In group 2, there was onlyone emergent P. vivax recurrence and no P. falciparum recurrence,while there were no episodes of slide positivity during follow-up ingroup 3. The AUC0-� and day 7 concentrations of NQ were sig-nificantly lower for the children with any parasitemia during fol-low-up than for those who remained free of malaria infection (P,0.001 and 0.005, respectively). However, the NQ dose in milli-grams per kilogram was also significantly lower (P, 0.001), and thedifference in AUC0-� was no longer significant when corrected fordose (P, 0.97), indicating that the lower dose, rather than individ-
ual pharmacokinetic differences, was responsible. Day 7 NQ con-centrations correlated significantly with AUC0-� overall (r, 0.91;P, �0.001) and in each of the three groups (r, �0.79; P, �0.001).
Artemisinin pharmacokinetics. Raw plasma ART concentra-tion-time data are presented in Fig. 6. A two-compartment modelwas superior to a one-compartment model for ART, with a lowerOFV (255.146 versus 122.637; P, �0.01) and an improvedCWRES. Since there was no evidence of model misspecification byuse of a two-compartment model with first-order absorption anda lag time, more-complex models were not tested. The structuralmodel parameters for ART were ka, lag time, CL/F, VC/F, VP/F,and Q/F (intercompartmental clearance for VP/F). Estimates ofthe BSV of CL, VC/F, ka, and lag time could be made, and a fullcovariance matrix was obtained. The correlation between CL/Fand V/F was �0.99 and was fixed at 1. Since the CWRES plotrevealed that plasma ART concentrations after the second dosewere lower than expected, the effect of dose occasion on observedF was tested as a negative categorical relationship. The addition ofthis relationship reduced the OFV by 46.626 (P, �0.001) and re-duced the residual error of the model by 7%. The second ART dosehad 77% lower bioavailability than the first. No other covariaterelationships were identified. The residual error in the final modelwas 51% (Table 4).
Goodness-of-fit and CWRES plots for ART are shown in Fig. 7.The results of the final parameter estimates and the bootstrapresults are summarized in Table 4, and post hoc Bayesian param-eter estimates with derived secondary pharmacokinetic parame-
TABLE 2 Population pharmacokinetic parameters and bootstrap results for NQ in children with uncomplicated P. falciparum malaria
Parameter Mean (RSEa [%]) in the final model Bootstrap median (95% CI)
Objective function value �687.786 �712.006 (�817.316 to �615.107)
Structural model parameterska (h�1) 1.1 (22) 1.0 (0.7 to 1.6)Lag time (h) 0.7 (7) 0.7 (0.6 to 0.8)VC/F (liters/70 kg) 12,500 (15) 12,200 (9,503 to 14,958)VP1/F (liters/70 kg) 15,500 (19) 17,000 (11,843 to 83,415)VP2/F (liters/70 kg) 17,200 (8) 16,000 (10,343 to 21,600)CL/F (liters/h/70 kg) 51.9 (6) 51.5 (30.1 to 58.7)Q1/F (liters/h/70 kg) 40.6 (9) 48.2 (24.2 to 113.0)Q2/F (liters/h/70 kg) 398 (13) 407 (318 to 536)
Covariate effect parameters (%)Decrease in predicted F with fever 31.8 (21) 31.8 (18.3 to 47.1)Decrease in predicted F with 1st dose in group 3 26.3 (33) 27.7 (9.3 to 40.9)Increase in VC/F per g/dl hemoglobin 16.4 (66) 14.9 (3.2 to 19.1)
Random model parametersBSV (%)
ka 104 (14) 103 (80 to 131)VC/F 77 (9) 77 (63 to 90)CL/F 32 (13) 31 (23 to 57)VP2/F 37 (17) 40 (25 to 59)Q2/F 52 (41) 50 (6 to 84)
Correlation coefficientka, VC/F 0.20 0.25 (�0.08 to 0.58)VC/F, CL/F 0.47 0.46 (0.04 to 0.72)CL/F, VP2/F 0.50 0.51 (0.09 to 0.86)VP2/F, Q2/F 0.20 0.18 (�0.62 to 0.91)
RUV (%) 24 (7) 24 (21 to 26)a RSE, relative standard error.
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ters are given in Table 5. The bootstrap demonstrated reasonable12/$12.00 Antimicrobial Agents and Chemotherapy p.2472–2484estimates of structural and random parameters with bi-ases of �8% and �10%, respectively, except for VP/F, where therewas a positive bias of 27%. No significant differences in secondaryparameters were found between the groups, although there was sub-stantial variability within each group. The pcVPC for ART is shown inFig. 8 and demonstrates the reasonable predictive performance of themodel.
The dose-corrected, first-dose data indicated that the medianAUC for ART was 5% higher in group 3 than group 2. However,these and other pharmacokinetic parameters for the two groupswere not significantly different (Table 5); hence, the data werepooled for total patient group estimates. Overall, mean (�SD)CL/F, Vss/F, and t1/2� for ART were 4.1 � 2.0 liters/h/kg, 21 � 10liters/kg, and 2.7 � 0.3 h, respectively. Although the best pharma-cokinetic model was a two-compartment model with a t1/2� of6.7 � 0.5 h, this may be a spurious finding, due to the limitedconcentration-time data in the present study design and the 27%bias in the bootstrap for VP/F.
DISCUSSION
The present study has provided the first pediatric pharmacoki-netic data for NQ and additional ART disposition data to comple-ment the few available for this age group. NQ given in the form ofART-NQ fixed combination therapy was promptly absorbed(mean absorption t1/2, 1.0 h) and reached a predicted Cmax of�200 �g/liter in all but one child even after the second dose ingroup 3. The mean elimination t1/2 of NQ (524 h) was longer thanestimates in early reports (41 to 57 h) (50) and in the recent Chi-nese adult volunteer study (156 to 299 h) (41). There was someevidence of a modest increase in NQ bioavailability when it wasadministered with a small amount of fat, in contrast to the sub-stantial food-associated reduction in NQ bioavailability in Chi-nese adults (41). The CL/F (1.1 liters/h/kg) and V/F (71 liters/kg)for NQ in our study were lower than the results reported forhealthy Chinese adults (7.0 liters/h/kg and 2,277 liters/kg) (41),but no other data are presently available for direct comparison. Inthe case of ART, the mean CL/F and V/F (4.1 liters/h/kg and 21liters/kg, respectively) were comparable to those in most previousstudies (means, 6.7 liters/h/kg and 27 liters/kg, respectively) (1, 4,5, 15, 16, 18, 25, 45).
The long elimination t1/2 and high V/F of NQ in our childrenwere consistent with those for most other quinolines and relateddrugs in clinical use (22, 30, 40). Pharmacokinetic modeling indi-cated that a three-compartment model best described the dispo-sition of NQ in the present study. This finding is consistent withsimilar pharmacokinetic studies involving chloroquine (21, 23,32, 52) and piperaquine (48). A number of studies of quinolineand related antimalarial drugs have shown biphasic drug concen-tration-time profiles that can be analyzed using a two-compart-ment model (10, 19, 27, 30, 39, 40, 46, 47, 53). Improved pharma-cokinetic study design, including more-frequent sampling oflonger duration, as well as lower limits of quantification for theanalytical techniques, may explain why recent studies such as oursreveal more-complex elimination kinetics. Indeed, the relativelyshort NQ elimination t1/2 in the Chinese volunteer study (41)could be explained by a short sampling period (216 h) as well as bythe use of noncompartmental methods. In relation to the latterpoint, we found an elimination t1/2 of 298 h by noncompartmentalmethods in group 1 patients versus 547 h in compartmental pop-ulation analyses of pooled NQ data.
The effect of fat on NQ bioavailability in the present studyneeds interpretation in light of the study design. In the prelimi-nary pharmacokinetic study in group 1 children, there was norequirement for fasting before or after drug administration. It islikely that these children consumed some fat around the timeART-NQ was given, even though the dose was administered withwater. Group 3 children, who were required to fast throughoutand were given the dose with water, had a 26% lower relativebioavailability than children in group 1 and also group 2, in whichART-NQ was administered with milk. This evidence of a modestpositive effect of fat on bioavailability contrasts with the observa-tion that the AUC and t1/2 of NQ were approximately 50% lowerafter food (60% lipid; 2,400 kJ) in healthy Chinese adults (41),suggesting an increased CL and/or reduced oral bioavailability.Studies with quinolines and related drugs have shown increasedabsorption with high-fat meals (3, 11, 46), but a standard Viet-namese meal (17 g fat; 2,000 kJ) had little effect on the pharmaco-kinetic properties of piperaquine (24). It is possible that relatively
FIG 4 (A) Population predicted (Œ) and individual predicted (�) versusobserved plasma NQ concentrations (�g/liter; log scale) for the final model.The line of identity is shown. (B) Conditional weight residuals versus time (logscale) for the final NQ model.
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high fat meals such as that used by Qu et al. (41) might interferewith the absorption of NQ from the gastrointestinal tract, but ourexperience is that amounts of fat given in milk greater than thatused in the present study (�8.5 g; �615 kJ) have a high likelihoodof inducing significant nausea in an unwell child with malaria.This observation and our NQ pharmacokinetic data do not sug-gest that food-associated underdosing will be problematic in chil-
dren. The improved NQ bioavailability after the second dose rel-ative to that after the first dose in group 3 may relate to clinicalimprovement reflecting parasite clearance, as has been seen withlumefantrine (20).
Fever was independently associated with reduced NQ bioavail-ability, consistent with pharmacokinetic studies in other contexts(8, 35). There was an independent association between hemoglo-bin and VC that might suggest NQ accumulation in red blood cells,but assessment of partitioning was beyond the scope of the presentstudy. As is the case for a range of other drugs, including anti-infectives (51), coadministration of milk reduced the rate of NQabsorption.
Qu et al. (41) reported CL/F values (mean, 2.7 liters/h/kg) foradults given ART-NQ that were lower than those obtained in pre-vious studies of ART pharmacokinetics (1, 4, 5, 15, 16, 18, 25, 45)and with ART monotherapy, while Sidhu et al. (45) found a sig-nificantly higher value for this parameter (14.4 liter/h/kg) whenART was given to children with uncomplicated P. falciparum ma-laria. While the former observation is difficult to explain, an ap-parently high CL/F may relate to underestimation of the ARTAUC. Almost all previous studies have used noncompartmentalanalysis or one-compartment models to determine the pharma-cokinetic parameters for ART. A two-compartment model was,however, the best fit for the ART concentration-time data in thepresent study, probably reflecting the fact that our limits of quan-tification (2.5 �g/liter) and detection (1 �g/liter) were consider-ably lower than those in previous studies (4 to 20 �g/liter) (4, 15,16, 25, 45). A prolonged elimination phase may have been unde-tected in past studies, thus truncating the AUC. Our assay sensi-tivity led, in part, to an unexpected limitation of the present study,namely, a lack of sampling �24 h postdose. Based on the estab-lished pharmacokinetic properties of ART, we anticipated that
TABLE 3 Post hoc Bayesian parameter estimates and derived secondary pharmacokinetic parameters for NQ in children with uncomplicated P.falciparum malaria
Parametera Group 1 (n � 13) Group 2 (n � 17) Group 3 (n � 16)
ka (h�1)b 1.3 (0.9–1.6) 0.7 (0.4–1.0) 1.7 (0.6–2.2)CL/F (liters/h) 17.3 (15.3–21.8) 19.5 (16.2–25.1) 16.6 (14.9–19.2)VC/F (liters) 2,115 (1,735–2,753) 3,494 (1,817–7,818) 3,610 (1,383–7,030)VP1/F (liters) 3,986 (3,432–4,318) 3,986 (3,321–4,871) 3,543 (3,183–3,903)VP2/F (liters) 4,392 (3,602–5,208) 4,662 (3,816–5,347) 4,370 (3,633–4,760)Vss/F (liters) 10,464 (9,366–13,888) 13,161 (10,485–14,053) 12,001 (9,390–14,882)t½� (h)c 6.8 (4.4–9.2) 8.2 (5.7–9.7) 7.3 (5.5–12.0)t½� (h)c 109 (92–121) 115 (103–126) 118 (104–130)t½� (h)c 525 (490–544) 500 (455–629) 595 (525–624)AUC0–� (�g · h/liter)d 5,935 (4,776–6,551) 7,104 (5,954–7,914) 15,385 (13,200–18,486)AUC1/dose (�g · h/liter per mg/kg) 917 (822–1,158) 728 (611–1,004) 813 (629–999)Relative bioavailabilitye 1.00 (1.00–1.00) 1.00 (0.68–1.00) 0.75 (0.75–0.87)Observed day 7 level (�g/liter)d 7.0 (4.9–8.3) 8.1 (7.3–9.8) 17.9 (12.0–22.9)
Dose 1Predicted Cmax1 (�g/liter) 40.6 (32.6–45.5) 33.9 (14.7–52.7) 22.9 (14.1–49.1)Predicted Tmax1 (h) 3.1 (2.7–3.7) 4.6 (3.7–7.1) 3.3 (2.4–4.8)
Dose 2Predicted Cmax2 (�g/liter) 57.0 (42.2–138)Predicted Tmax2 (h) (h) 27.3 (26.7–28.3)
a Data are medians (interquartile ranges).b P, 0.053 and 0.094 for the comparison between groups 2 and 1 and groups 2 and 3, respectively.c t½�, t½�, and t½� are the first distribution, second distribution, and terminal elimination half-lives, respectively.d P, �0.01 for comparisons between groups 2 and 1 and groups 3 and 1.e P, �0.01 for comparison between groups 3 and 1.
FIG 5 Prediction-corrected VPC plots for NQ in children with uncompli-cated P. falciparum malaria, showing the observed 50th percentile (�) and the10th and 90th percentiles (Œ) with the simulated 95% CIs for the 50th percen-tile (solid black line) and the 10th and 90th percentiles (dashed gray lines).(Inset) Plasma concentration-time data from 0 to 100 h after the dose.
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plasma ART concentrations would not be detectable beyond 24 h.If prolonged sampling had been performed, it would have alloweda more definitive multicompartment pharmacokinetic character-ization.
In a Vietnamese study, coingestion of food was reported to beassociated with a nonsignificant 20% reduction in the ART AUCafter oral ART administration to healthy adults (16). In contrast,Qu et al. (41) reported that the AUC and t1/2 of ART were approx-imately 75% higher after coadministration of food with ART-NQcombination therapy, suggesting increased bioavailability and apossible reduction in CL. Our data are consistent with the earlierstudy of Dien et al. (16), in that we also found a nonsignificant 5%lower AUC for ART after ingestion of food (milk) compared withadministration with water. There were no significant differenceswhen the dose group was added as a covariate in the populationpharmacokinetic model, further evidence that fat has no clinicallymeaningful effect on the pharmacokinetics of ART.
Although the importance of developing pediatric formulationsof antimalarial drugs has been emphasized (34), it is not clear howthe manufacturer’s pediatric ART-NQ dose recommendationshave been developed. Using either a weight-based equation (28)(dose for a child [mg] � dose for an adult [mg] � [weight of achild/weight of an adult]0.75) or a body surface area (BSA) equa-
tion (28, 42) (dose for a child [mg] � dose for an adult [mg] �[BSA of a child/BSA of an adult]), where the regular adult dose ofNQ is 400 mg, adult weight is assumed to be 50 kg, and adult BSAis 1.73 m2, the adult dose of 8 mg/kg would scale up to �10 mg/kgin children. Our initial, conservative mean dose of 6.3 mg/kg NQfor group 1 as part of ART-NQ was associated with a relativelyhigh late-treatment failure rate (7). The regimens used for groups2 and 3 (means, 9.0 and 9.5 mg/kg NQ per dose) were based on themanufacturer’s recommendations of 6.5 to 9.5 mg/kg for childrenweighing as much as 40 kg (33, 38), doses that still fall short of theallometrically scaled dose of �10 mg/kg.
Efficacy against asexual parasite forms over 42 days of fol-low-up for groups 2 and 3 was 100%, but prolonged gametocytecarriage was observed in some patients (7). The latter observation,together with concerns regarding the emergence of artemisininresistance in areas of endemicity with a history of subtherapeuticdrug use (17), the implication that higher individual pediatricdoses than those recommended by the manufacturer can be used,and the safety of the two-dose ART-NQ regimen employed forgroup 3 (7), supports an argument for a 3-day ART-NQ regimenin line with WHO recommendations for all artemisinin combina-tion therapies (54). We have used the final NQ model to simulateCmax after three ART-NQ doses given with milk to 1,000 childrenwith characteristics similar to those of the present subjects. Themedian Cmax values (95% prediction intervals) after three consec-utive daily doses were 36 (19 to 76), 69 (44 to 128), and 89 (61 to152) �g/liter, respectively, with an absolute range up to 350 �g/liter after the third simulated dose. A predicted Cmax of �300
TABLE 4 Population pharmacokinetic parameters and bootstrap resultsfor ART in children with uncomplicated P. falciparum malaria
Parameter
Mean (RSEa
[%]) in thefinal model Bootstrap median (95% CI)
Objective function value 85.171 73.924 (�60.386–178.368)
Structural model parameterska (h�1) 1.8 (110) 1.8 (0.6–6.5)Lag time (h) 0.7 (31) 0.7 (0.4–0.9)VC/F (liters/70 kg) 1,160 (31) 1,140 (625–1,520)VP/F (liters/70 kg) 166 (37) 211 (96.1–1,270)CL/F (liters/h/70 kg) 178 (12) 176 (141–216)Q/F (liters/h/70 kg) 14.2 (103) 15.3 (6.6–52.3)
Covariate effect parameter: %decrease in predictedF with 2nd dose
77.0 (9) 78.6 (63.3–89.9)
Random model parametersBSV
CL/F (%) 57 (25) 56 (43–67)Lag time (%) 23 (169) 21 (5–57)ka (%) 139 (81) 141 (72–230)VC/F to CL/F (ratio) 0.995 (28) 0.989 (0.760–1.671)
Correlation coefficientCL/F, lag time 0.571 0.565 (0.201–0.997)CL/F, ka 0.0225 0.011 (�0.550–0.628)Lag time, ka �0.340 �0.343 (�0.956–0.319)CL/F, VC/F 1 Fixed
RUV (%) 51 (31) 50 (37–65)a RSE, relative standard error.
FIG 6 Plasma ART concentration-time plots for group 2 (milk) (A) andgroup 3 (water and double dose) (B) patients.
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�g/liter occurred in a small minority of subjects in the simulation.The group 3 child with a predicted Cmax of 270 �g/liter had anuncomplicated clinical course in the present study, and a Cmax of245 �g/liter in an adult was not reported to be associated withtoxicity (50), but careful tolerability and safety monitoring wouldneed to be carried out if a three dose regimen were implemented.
A further argument for multiple-dose ART-NQ relates to thedisposition of the ART component. The conventional dosage reg-imen for orally administered ART of 10 to 20 mg/kg on the firstday, followed by 500 mg daily for 4 days (14), has been questioneddue to the autoinduction of ART metabolism that, as in the pres-ent study, progressively and substantially reduces the bioavailabil-ity of subsequent doses but does not increase CL (25). The 15- to24-mg/kg dose of ART used in the present study could, therefore,be an appropriate part of a 3-day ART-NQ regimen based on thesingle dose now recommended by the manufacturer.
The present study had limitations, in part because of the pres-ent paucity of pharmacokinetic and other data relating toART-NQ (especially when group 1 was recruited) but also becauseof the context of a pediatric study in the rural tropics. The sam-pling schedule could have included more time points after the
second dose in group 3, but relatively robust estimates for modelparameters could still be derived. It was unfortunate that no pureP. vivax malaria cases were recruited, but the fact that there wasonly one late P. vivax infection in groups 2 and 3 suggests that thelong NQ t1/2 helps prevent the emergence of this infection, whichis seen after other therapies for P. falciparum malaria in this area,including artemether-lumefantrine (31).
In conclusion, when normalized by body weight, the pharma-cokinetic parameters for ART in children are comparable to those
FIG 7 (A) Population predicted (Œ) and individual predicted (�) versusobserved plasma ART concentrations (�g/liter; log scale) for the final model.The line of identity is shown. (B) Conditional weight residuals versus time (logscale) for the final ART model.
TABLE 5 Post hoc Bayesian parameter estimates and derived secondarypharmacokinetic parameters for artemisinin in children withuncomplicated P. falciparum malariaa
Parameter Group 2 (n � 17) Group 3 (n � 16)
ka (h�1) 2.0 (0.7–3.5) 1.1 (0.8–4.1)CL/F (liters/h) 82.1 (76.2–74.8) 66.9 (61.5–62.4)VC/F (liters) 348 (246–449) 279 (165–324)Q/F (liters/h) 5.13 (4.47–5.96) 4.69 (4.33–5.05)VP/F (liters) 42.7 (35.6–52.2) 37.9 (34.1–41.8)Vss/F (liters) 388 (289–482) 315 (202–362)t½� (h) 2.8 (2.7–3.0) 2.7 (2.5–2.8)t½� (h) 6.8 (6.2–7.0) 6.6 (6.5–6.9)AUC1 (�g · h/liter) (dose 1) 5,127 (3,631–8,237) 6,770 (5,249-10,235)AUC1/dose (�g · h/liter per
mg/kg)267 (170–340) 281 (202–417)
AUC2 (�g · h/liter) (dose 2) 1,557 (1,207–2,354)AUC0–� (�g · h/liter) 8,327 (6,457–12,590)
Dose 1Predicted Cmax1 (�g/liter) 843 (522–1,353) 1,105 (736–1,398)Predicted Tmax1 (h) 2.1 (1.6–2.9) 2.5 (1.5–3.0)
Dose 2Predicted Cmax2 (�g/liter) 269 (179–345)Predicted Tmax2 (h) 26.6 (26.0–27.4)
a Data are medians (IQR). All between-group comparisons were statisticallynonsignificant.
FIG 8 Prediction-corrected VPC plots for ART in children with uncompli-cated P. falciparum malaria, showing the observed 50th percentile (�) and the10th and 90th percentiles (Œ) with the simulated 95% CIs for the 50th percen-tile (solid black line) and the 10th and 90th percentiles (dashed gray lines).
Batty et al.
2482 aac.asm.org Antimicrobial Agents and Chemotherapy
obtained in most previous studies with adults, but CL/F washigher than that in data recently reported when ART-NQ wascoadministered to healthy adults (41). In contrast, CL/F and V/Ffor NQ were lower in the present study, and the terminal elimina-tion t1/2 was longer, at a mean of 21.8 days. Although the predictedbioavailability of the first dose of NQ was lower in a fasted state,this is unlikely to translate into clinically meaningful effects. Thepresent pharmacokinetic characterization, as well as associatedtolerability, safety, and preliminary efficacy data (7), may justifyusing the currently recommended single dose of ART-NQ for 3days for children with uncomplicated malaria.
ACKNOWLEDGMENTS
We thank the children and their parents/guardians for their participation.We are also most grateful to Sister Valsi Kurian and the staff of AlexishafenHealth Centre for their kind cooperation during the study and to MicheleSenn and the staff of the Papua New Guinea Institute of Medical Researchfor clinical and logistic assistance. Valuable technical support was pro-vided by Michael Boddy and John Hess, School of Pharmacy, Curtin Uni-versity.
This study was funded by the National Health and Medical ResearchCouncil (NHMRC) of Australia (grant 634343). S.T.L. was the recipient ofa Cranmore Undergraduate Scholarship through the Faculty of Medicine,Dentistry, and Health Science, University of Western Australia, andT.M.E.D. is supported by an NHMRC Practitioner Fellowship.
F.W.H. has received research funding from Kunming Pharmaceuti-cals, the manufacturer of ARCO.
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25. Hassan Alin M, Ashton M, Kihamia CM, Mtey GJ, Bjorkman A. 1996.Multiple dose pharmacokinetics of oral artemisinin and comparison of itsefficacy with that of oral artesunate in falciparum malaria patients. Trans.R. Soc. Trop. Med. Hyg. 90:61– 65.
26. Hien TT, et al. 2011. Orally formulated artemisinin in healthy fastingVietnamese male subjects: a randomized, four-sequence, open-label,pharmacokinetic crossover study. Clin. Ther. 33:644 – 654.
27. Hung TY, et al. 2004. Population pharmacokinetics of piperaquine inadults and children with uncomplicated falciparum or vivax malaria. Br. J.Clin. Pharmacol. 57:253–262.
28. Johnson TN. 2008. The problems in scaling adult drug doses to children.Arch. Dis. Child. 93:207–211.
29. Jonsson EN, Karlsson MO. 1999. Xpose—an S-PLUS based populationpharmacokinetic/pharmacodynamic model building aid for NONMEM.Comput. Methods Programs Biomed. 58:51– 64.
30. Karunajeewa HA, et al. 2008. Pharmacokinetics and efficacy of piper-aquine and chloroquine in Melanesian children with uncomplicated ma-laria. Antimicrob. Agents Chemother. 52:237–243.
31. Karunajeewa HA, et al. 2008. A trial of combination antimalarial thera-pies in children from Papua New Guinea. N. Engl. J. Med. 359:2545–2557.
32. Karunajeewa HA, et al. 2010. Pharmacokinetics of chloroquine andmonodesethylchloroquine in pregnancy. Antimicrob. Agents Chemother.54:1186 –1192.
33. Kunming Pharmaceutical Corporation. 2006. Instruction for use of com-pound naphthoquine phosphate tablets. Product information brochure.Kunming Pharmaceutical Corporation, Kunming, Yunnan Province,China.
34. Kurth F, et al. 2010. Do paediatric drug formulations of artemisinincombination therapies improve the treatment of children with malaria? Asystematic review and meta-analysis. Lancet Infect. Dis. 10:125–132.
35. Mackowiak PA. 1989. Influence of fever on pharmacokinetics. Rev. Infect.Dis. 11:804 – 807.
36. Manning L, et al. 2011. Meningeal inflammation increases artemetherconcentrations in cerebrospinal fluid in Papua New Guinean childrentreated with intramuscular artemether. Antimicrob. Agents Chemother.55:5027–5033.
37. Matuszewski BK, Constanzer ML, Chavez-Eng CM. 2003. Strategies forthe assessment of matrix effect in quantitative bioanalytical methodsbased on HPLC-MS/MS. Anal. Chem. 75:3019 –3030.
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39. Obua C, et al. 2008. Population pharmacokinetics of chloroquine and
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sulfadoxine and treatment response in children with malaria: suggestionsfor an improved dose regimen. Br. J. Clin. Pharmacol. 65:493–501.
40. Price R, et al. 1999. Pharmacokinetics of mefloquine combined withartesunate in children with acute falciparum malaria. Antimicrob. AgentsChemother. 43:341–346.
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45. Sidhu JS, et al. 1998. Artemisinin population pharmacokinetics in chil-dren and adults with uncomplicated falciparum malaria. Br. J. Clin. Phar-macol. 45:347–354.
46. Sim IK, Davis TM, Ilett KF. 2005. Effects of a high-fat meal on the relativeoral bioavailability of piperaquine. Antimicrob. Agents Chemother. 49:2407–2411.
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48. Tarning J, et al. 2005. Pitfalls in estimating piperaquine elimination.Antimicrob. Agents Chemother. 49:5127–5128.
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289
xi.b 161B161BAppendixB:NONMEMcodeforpopulationpharmacokineticmodels
inthethesis
290
291
$PROB Azithromycin in Pregnancy Final Model
$DATA AZIDATA.CSV IGNORE=#
$INPUT # ID TIME CONC=DV DOSE=AMT CMT RATE MDV PREG DSGP FDHT GEST MALS HB GLUC
$SUB ADVAN12 TRANS4
$PK
TVCL=THETA(1)*(WT/70)**(.75)
TVV2=THETA(2)*(WT/70)+PREG*THETA(9)
TVQ3=THETA(3)*(WT/70)**(.75)
TVV3=THETA(4)*(WT/70)
TVQ4=THETA(5)*(WT/70)**(.75)
TVV4=THETA(6)*(WT/70)
TVKA=THETA(7)
TVD1=THETA(8)
CL=TVCL*EXP(ETA(1))
V2=TVV2*EXP(ETA(2))
Q3=TVQ3
V3=TVV3*EXP(ETA(3))
V4=TVV4
Q4=TVQ4
KA=TVKA
D1=TVD1*EXP(ETA(4))
S2=V2
S3=V3
S4=V4
$ERROR
Y=F*(1+ERR(1))
$THETA
(0,137.6943) ; CL
(0,431.3440) ; Vcentral
(0,681.0347) ; Q3
(0,2928.8) ; Vperi
(0,42.8039) ; Q4
(0,7019.6) ; Vperi 2
(0,0.4800,2) KA
(0,1.5549) ; DUR
(0,28.5617) ; COV
$OMEGA
0.0545 ; IIV‐CL
0.9630 ; IIV‐V2
0.1174 ; IIV‐V3
0.0313 ; IIV‐DUR
292
$SIGMA
0.2037 ; Proportional error
$EST MAX=9990 SIG=3 METHOD=COND INTER POSTHOC PRINT=5
$COV PRINT=E
293
$PROB Pyrimethamine in Infants Final Model
$DATA PYRDATA.CSV IGNORE #
$INPUT # ID TIME CONC=DV DOSE=AMT MDV GFR DSGR DSKG EPMA MALS AQTR HB
$SUB ADVAN4 TRANS4
$PK
TVHILL=THETA(5)
HILL=TVHILL
TVMA50=THETA(6)
MA50=TVMA50
FCL=((EPMA)**HILL)/(((EPMA)**HILL)+MA50**HILL)
TVCLH=THETA(7)*FCL
CLH=TVCLH
RGFR=EGFR/120
TVCLR=THETA(8)*RGFR
CLR=TVCLR
TVCL=(CLH+CLR)*((WT/70)**0.75)
TVKA=THETA(1)
TVV2=THETA(2)*(WT/70)
TVQ=THETA(4)*((WT/70)**0.75)
TVV3=THETA(3)*(WT/70)
CL=TVCL*EXP(ETA(1))
KA=TVKA
V2=TVV2*EXP(ETA(2))
Q=TVQ*EXP(ETA(2)*THETA(9))
V3=TVV3
S2=V2
S3=V3
$ERROR
IPRED =LOG(F+0.001)
IRES=DV‐IPRED
IWRES=IRES/1
Y=LOG(F)+ERR(1)
$THETA
(0, 0.779) FIX ; KA
(0, 222) ; V2
(0, 64.1) ; V3
(0, 0.0735,20) ; Q
(0, 7.39,40) ; hill
(0, 318,400) ; ma50
(0, 0.854,10) ; CLh
(0, 0.416,10) ; CLr
294
(0.1, 6.95,50) ; ETA‐R
$OMEGA BLOCK(2)
0.0772 ; IIV‐CL
0.0192 ; R (CL, V2)
0.0168 ; IIV‐V2
$SIGMA
0.113 ; Proportional Error
$EST MAX=0 SIG=3 METHOD=COND INTER POSTHOC PRINT=5 MSFO=MSFILE
$COV PRINT=E
295
$PROB Sulfadoxine in Infants Final Model
$DATA SDXDATA.CSV IGNORE #
$INPUT # ID TIME CONC=DV DOSE=AMT MDV GFR DSGR DSKG EPMA MALS AQTR HB
$SUB ADVAN2 TRANS2
$PK
TVHILL=THETA(4)
HILL=TVHILL
TVMA50=THETA(5)
MA50=TVMA50
FCL=((EPMA)**HILL)/(((EPMA)**HILL)+MA50**HILL)
TVCLH=THETA(6)*FCL
CLH=TVCLH
RGFR=GFR/120
TVCLR=THETA(7)*RGFR
CLR=TVCLR
TVKA=THETA(1)
TVCL=(CLH+CLR)*((WT/70)**0.75)
TVV=THETA(2)*(WT/70)
KA=TVKA
CL=TVCL*EXP(ETA(1))
V=TVV*EXP(ETA(2))
F1=1*((DSKG/60)**THETA(3))
S2=V
$ERROR
IPRED =LOG(F+0.001)
IRES=DV‐IPRED
IWRES=IRES/1
Y=LOG(F)+ERR(1)
$THETA
(0,1.23) FIX ; KA
(0,20,30) ;V
(‐.8,‐.2,0) ;DSKG‐F
(0,6,100) ;HILL
(0,280,400) ;MA50
(0,.03,.5) ;CLH
(0,0.003,.5) ;CLR
$OMEGA BLOCK(2)
0.1 ; IIV‐CL
0.05 ; R (CL,V)
0.1 ; IIV‐V
296
$SIGMA
0.5 ; Proportional error
$ESTIM MAX=9990 SIG=3 METHOD=COND INTER POSTHOC PRINT=5
$COV PRINT=E
297
$PROB Sulfadoxine + N‐acetylsulfadoxine in Infants Final Model
$DATA SDXNSXDATA.CSV IGNORE #
$INPUT # ID TIME CONC=DV DOSE=AMT CMT MDV GFR DSGR DSKG EPMA MALS AQTR HB
$SUB ADVAN5 TRANS1
$MODEL
NCOMPARTMENTS=3
COMP=(GUT,DEFDOSE) ;Gut compartment
COMP=(PARENT) ;Observation compartment for sulfadoxine
COMP=(METAB) ;Observation compartment for n‐acetylsulfadoxine
$PK
TVHILL=THETA(4)
HILL=TVHILL
TVMA50=THETA(5)
MA50=TVMA50
FCL=((EPMA)**HILL)/(((EPMA)**HILL)+MA50**HILL)
TVCLH=THETA(6)*FCL
CLH=TVCLH
RGFR=GFR/120
TVCLR=THETA(7)*RGFR
CLR=TVCLR
TVKA=THETA(1)
TVCL2=(CLH+CLR)*((WT/70)**0.75)
TVV2=THETA(2)*(WT/70)
TVCL3=THETA(8)*RGFR*((WT/70)**0.75)
TVV3=THETA(9)*(WT/70)
KA=TVKA
CL2=TVCL*EXP(ETA(1))
V2=TVV2*EXP(ETA(2))
CL3=TVCL3*EXP(ETA(3))
V3=TVV3*EXP(ETA(4))
F1=1*((DSKG/60)**THETA(3))
K12=KA
K20=(CL2*.4)/V2
K23=(CL2*.6)/V2
K30=CL3/V3
S2=V
$ERROR
IF (CMT.EQ.2) THEN
IPRED=LOG((A(2)/V2)+0.0001)
IRES=DV‐IPRED
IWRES=IRES/1
298
Y=LOG(F)+ERR(1)
ENDIF
IF(CMT.EQ.3) THEN
IPRE=LOG((A(3)/V3)+.0001)
IRES=DV‐IPRE
IWRE=IRES/1
Y=LOG(F)+ERR(2)\
ENDIF
$THETA
(0,1.23) FIX ; KA
(0,24.2,30) FIX ; V2
(‐1,‐.56,1.5) FIX ; DSKG‐F
(0,4.07,100) FIX ; HILL
(0,271,400) FIX ; MA50
(0,.0458,.5) FIX ; CLh
(0,0.00439,.5) FIX ; CLr
(0,0.3,30) ; CL3
(0,10,30) ; V3
$OMEGA BLOCK(2)
0.1 ; IIV‐CL2
0.05 ; R (CL2,V2)
0.1 ; IIV‐V2
$OMEGA BLOCK(2)
0.1 ; IIV‐CL3
0.05 ; R (CL3,V3)
0.1 ; IIV‐V3
$SIGMA
0.0272 ; Proportional error for sulfadoxine
0.1 ; Proportional error for n‐acetylsulfadoxine
$ESTIM MAX=9990 SIG=3 METHOD=COND INTER POSTHOC PRINT=5
$COV PRINT=E
299
$PROB Lumefantrine + Desbutyl‐lumefantrine in Children Final Model
$DATA LUMDBLDATA.CSV IGNORE=#
$INPUT # ID TIME CONC=DV DOSE=AMT CMT MDV AGE SEX PARA HB
$SUB ADVAN5 TRANS1
$MODEL
NCOMPARTMENTS=6
COMP=(GUT,DEFDOSE) ;Gut compartment
COMP=(PAR1) ;Observation compartment for lumefantrine
COMP=(MET1) ;Observation compartment for desbutyl‐lumefantrine
COMP=(PAR2) ;Peripheral compartment 1 for lumefantrine
COMP=(PAR3) ; Peripheral compartment 2 for lumefantrine
COMP=(MET2) ; Peripheral compartment for desbutyl‐lumefantrine
$PK
CL2=THETA(1)*((WT/70)**(.75))*EXP(ETA(2))
V2=THETA(2)*(WT/70)
Q4=THETA(3)*((WT/70)**(.75))
V4=THETA(4)*(WT/70)
Q5=THETA(5)*((WT/70)**(.75))
V5=THETA(6)*(WT/70)
KA=THETA(7)*EXP(ETA(1))
IF(DOSN.EQ.6) KA=THETA(13)*EXP(ETA(1))
ALAG1=2.0
F1=(1)*EXP(ETA(3)+ETA(6))
IF (DOSN.EQ.2) F1=1*EXP(ETA(3)+ETA(7))
IF (DOSN.EQ.3) F1=1*EXP(ETA(3)+ETA(8))
IF (DOSN.EQ.4) F1=1*EXP(ETA(3)+ETA(9))
IF (DOSN.EQ.5) F1=1*EXP(ETA(3)+ETA(10))
IF (DOSN.EQ.6) F1=1*EXP(ETA(3)+ETA(11))
MWRATO=472.83/528.939
CL3=THETA(8)*MWRATO*((WT/70)**(.75))*EXP(ETA(4))
V3=THETA(9)*MWRATO*(WT/70)*EXP(ETA(5))
Q6=THETA(10)*MWRATO*((WT/70)**(.75))
V6=THETA(11)*MWRATO*(WT/70)
FP=THETA(12)
S2=V2
S3=V3
S4=V4
S5=V5
S6=V6
K12=KA
K13=KA*(FP)/(1‐FP)
300
K24=Q4/V2
K42=Q4/V4
K25=Q5/V2
K52=Q5/V5
K20=0
K23=CL2/V2
K30=CL3/V3
K36=Q6/V3
K63=Q6/V6
$ERROR
IF (CMT.EQ.2) THEN
IPRED =LOG(F+0.001)
IRES=DV‐IPRED
IWRES=IRES/1
Y=LOG(F)+ERR(1)
ENDIF
IF(CMT.EQ.3) THEN
IPRE =LOG(F+0.001)
IRES=DV‐IPRE
IWRE=IRES/1
Y=LOG(F)+ERR(2)
ENDIF
$THETA
(0,7.87,) ;CL2
(0,257,) ; V2
(0,1.45,) ; Q4
(0,109,) ; V4
(0,.874,) ; Q5
(0,184,) ; V5
(0,.409,) ; KA
(0, 873) ; CL3
(0, 35400) ; V3
(0, 757) ; Q6
(0, 68200) ; V6
(0, .1,1) ; FP
(0,.409,) ; KA‐DOSE6
$OMEGA
0.1 ; IIV‐KA
$OMEGA BLOCK(2)
0.1 ; IIV‐CL2
0.05 ; R (CL2, F)
0.1 ; IIV‐F
$OMEGA BLOCK(2)
0.1 ; IIV‐CL3
0.05 ; R (CL3,V3)
301
0.1 ; IIV‐V3
$OMEGA BLOCK(1)
.1 ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$SIGMA
0.0565 ; Proportional error for lumefantrine
0.565 ; Proportional error for desbutyl‐lumefantrine
$EST MAX=9990 SIG=3 METHOD=COND INTER POSTHOC PRINT=1
$COV PRINT=E
302
$PROB Artemether + Dihydroartemisinin in Children Final Model
$DATA LUMDBLDATA.CSV IGNORE=#
$INPUT # ID TIME CONC=DV DOSE=AMT CMT MDV AGE SEX PARA HB
$SUB ADVAN5 TRANS1
$MODEL
NCOMPARTMENTS=6
COMP=(GUT,DEFDOSE) ;Gut compartment
COMP=(PAR1) ;Observation compartment for artemether
COMP=(MET1) ;Observation compartment for dihydroartemisinin
COMP=(PAR2) ;Peripheral compartment for artemether
$PK
CL2=THETA(1)*((WT/70)**(0.75))*(1+THETA(7)*EXP(ETA(8))*(DSCL‐1))
V2=THETA(2)*(WT/70)
Q4=THETA(3)*((WT/70)**(0.75))
V4=THETA(4)*(WT/70)
KA=THETA(5)
SIG=THETA(6)
F1=1*EXP(ETA(1)+ETA(2));*EXP(ETA(1))
IF (DOSN.EQ.2) F1=1*EXP(ETA(1)+ETA(3))
IF (DOSN.EQ.3) F1=1*EXP(ETA(1)+ETA(4))
IF (DOSN.EQ.4) F1=1*EXP(ETA(1)+ETA(5))
IF (DOSN.EQ.5) F1=1*EXP(ETA(1)+ETA(6))
IF (DOSN.EQ.6) F1=1*EXP(ETA(1)+ETA(7))
MWRAT= 284.35/298.37
V3=THETA(8)*(WT/70)*(MWRAT)
CL3=THETA(9)*((WT/70)**0.75)*(MWRAT)
SIG2=THETA(10)
K12=KA
K20=0
K30=CL3/V3
K23=CL2/V2
K24=Q4/V2
K42=Q4/V4
S2=V2
S3=V3
S4=V4
$ERROR
LOQ=LOG(5)
IPRED=LOG(F+0.001)
IRES=DV‐IPRED
303
IWRES=IRES/1
DUM=(LOQ‐IPRED)/SIG
CUMD=PHI(DUM)
IF(TYPE.EQ.1.AND.CMT.EQ.2) THEN
F_FLAG=0
Y=LOG(F)+SIG*ERR(1)
ENDIF
IF(TYPE.EQ.2.AND.CMT.EQ.2) THEN
F_FLAG=1
Y=CUMD
ENDIF
LOQ2=LOG(2)
IPRED=LOG(F+0.001)
IRES=DV‐IPRED
IWRES=IRES/1
DUM=(LOQ2‐IPRED)/SIG2
CUMD=PHI(DUM)
IF(TYPE.EQ.1.AND.CMT.EQ.3) THEN
F_FLAG=0
Y=LOG(F)+SIG2*ERR(1)
ENDIF
IF(TYPE.EQ.2.AND.CMT.EQ.3) THEN
F_FLAG=1
Y=CUMD
ENDIF
$THETA
(0, 64.2) ; CL
(0, 53.1) ; V2
(0, 15) ; Q
(0, 349) ; V3
(0, 1) FIX ; KA
(0, 0.587) ; Proportional error for artemether
(0, 0.628) ; CLDOSN
(0,360) ; V
(0,260) ; CL
(0,.6) ; Proportional error for dihydroartemisinin
$OMEGA
0.385 ; IIV‐F
$OMEGA BLOCK(1)
.1 ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA
304
0.537 ; IIV‐CL2
$SIGMA
1 FIX
$EST MAX=9990 SIG=3 METHOD=COND INTER LAPLACIAN
$COV PRINT=E
305
$PROB Piperaquine in Children Final Model
$DATA PQDATA.CSV IGNORE=#
$INPUT # ID TIME CONC=DV DOSE=AMT AGE SEX PARA FEV
$SUB ADVAN6 TOL=3
$MODEL
NCOMPARTMENTS=4
COMP=(GUT,DEFDOSE) ;Gut compartment
COMP=(PAR1) ;Observation compartment for piperaquine
COMP=(PAR2) ; Peripheral compartment 1 for piperaquine
COMP=(PAR3) ;Peripheral compartment 2 for piperaquine
$PK
CL=THETA(1)*(WT/70)**(.75)*EXP(ETA(4))
V2=THETA(2)*(WT/70)*EXP(ETA(5))
Q3=THETA(3)*(WT/70)**(.75)
V3=THETA(4)*(WT/70)*EXP(ETA(6))
Q4=THETA(5)*(WT/70)**(.75)
V4=THETA(6)*(WT/70)
MTT=THETA(7)*EXP(ETA(7));Mean transit time
NN=THETA(8);*EXP(ETA(4)) ;Number of transit compartments
FPQ=1
IF(DOSN.EQ.1) FPQ=1*EXP(ETA(1))
IF(DOSN.EQ.2) FPQ=1*EXP(ETA(2))
IF(DOSN.EQ.3) FPQ=1*EXP(ETA(3))
S2=V2
S3=V3
S4=V4
IF(TIME.EQ.0) THEN
PD1=0
TDOS1=0
PD2=0
TDOS2=200
PD3=0
TDOS3=200
ENDIF
IF(AMT.GT.0.AND.DOSN.EQ.1) THEN
PD1=AMT*FPQ
TDOS1=TIME
ENDIF
IF(AMT.GT.0.AND.DOSN.EQ.2) THEN
PD2=AMT*FPQ
TDOS2=TIME
306
ENDIF
IF(AMT.GT.0.AND.DOSN.EQ.3) THEN
PD3=AMT*FPQ
TDOS3=TIME
ENDIF
F1=0
KTR=(NN+1)/MTT
KA=KTR
L=LOG(2.5066)+(NN+.5)*LOG(NN)‐NN ;Sterling
$DES
X=0.00001
RATE1=EXP(LOG(PD1+X)+LOG(KTR+X)+NN*LOG(KTR*T+X)‐KTR*T‐L)
DADT(1)=RATE1‐KA*A(1)
IF(T.GE.TDOS2) THEN
RATE1=EXP(LOG(PD1+X)+LOG(KTR+X)+NN*LOG(KTR*T+X)‐KTR*T‐L)
RATE2=EXP(LOG(PD2+X)+LOG(KTR+X)+NN*LOG(KTR*(T‐TDOS2)+X)‐KTR*(T‐TDOS2)‐L)
DADT(1)=RATE1+ RATE2‐KA*A(1)
ENDIF
IF(T.GE.TDOS3) THEN
RATE1=EXP(LOG(PD1+X)+LOG(KTR+X)+NN*LOG(KTR*T+X)‐KTR*T‐L)
RATE2=EXP(LOG(PD2+X)+LOG(KTR+X)+NN*LOG(KTR*(T‐TDOS2)+X)‐KTR*(T‐TDOS2)‐L)
RATE3=EXP(LOG(PD3+X)+LOG(KTR+X)+NN*LOG(KTR*(T‐TDOS3)+X)‐KTR*(T‐TDOS3)‐L)
DADT(1)=RATE1+ RATE2 + RATE3 ‐KA*A(1)
ENDIF
DADT(2)=KA*A(1)‐Q3/V2*A(2)+Q3/V3*A(3)‐Q4/V2*A(2)+Q4/V4*A(4)‐CL/V2*A(2)
DADT(3)=Q3/V2*A(2)‐Q3/V3*A(3)
DADT(4)=Q4/V2*A(2)‐Q4/V4*A(4)
$ERROR
IPRED =LOG(F+0.001)
IRES=DV‐IPRED
IWRES=IRES/1
Y=LOG(F)+ERR(1)
$THETA
(1,70,) ; CL
(1,4000,) ; V2
(1,400,) ; Q3
(1,4000,) ; V3
(1,120,) ; Q4
(1,40000,) ; V4
(.1,1.1,20) ; MTT
(.1,1.02,) ; NN
$OMEGA BLOCK(1)
307
0.6 ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$OMEGA BLOCK(3)
0.1 ; IIV‐CL
0.05 ; R (CL,V2)
0.1 ; IIV‐V2
0
0.1 ; R (V2,V3)
0.5 ; IIV‐V3
$OMEGA
0.6 ; IIV‐MTT
$SIGMA
0.15 ; Proportional error for piperaquine
$ESTIM MAX=9990 SIG=2 METHOD=COND INTER POSTHOC
$COV PRINT=E
308
$PROB Artemisinin (from ART‐PQ) in Children Final Model
$DATA ART_PQ_DATA.CSV IGNORE #
$INPUT # ID TIME CONC=DV DOSE=AMT AGE SEX PARA FEV
$SUB ADVAN4 TRANS4
$PK
CL=THETA(1)*((WT/70)**(0.75))*EXP(ETA(1))
V2=THETA(2)*(WT/70)
Q=THETA(3)*((WT/70)**(0.75))
V3=THETA(4)*(WT/70)
KA=THETA(5)
F1=1*EXP(ETA(2))
IF(DOSN.EQ.2) F1=THETA(6)*EXP(ETA(3))
S2=V2
S3=V3
$ERROR
IPRED =LOG(F+0.001)
IRES=DV‐IPRED
IWRES=IRES/1
Y=LOG(F)+ERR(1)
$THETA
(0,100,) ;CL
(200,500,) ; V2
(0,50,) ; Q
(0,500,) ; V3
(1,3,) ; KA
(0,.2,2) ; F2
$OMEGA
0.5 ; IIV‐V2
$OMEGA BLOCK(1)
0.1 ; IOV‐F
$OMEGA BLOCK SAME ; IOV‐F
$SIGMA
0.1 ; Proportional error for artemisinin
$ESTIM MAX=9990 SIG=3 METHOD=COND INTER POSTHOC
$COV PRINT=E
309
$PROB Napthoquine in Children Final Model
$DATA NQDATA.CSV IGNORE #
$INPUT # ID TIME CONC=DV DOSE=AMT GRP DOSN FVR DSKG SEX SPLN MAL PARA AGE FEV HB
$SUB ADVAN12 TRANS4
$PK
KA=THETA(1)*EXP(ETA(1))
CL=THETA(2)*(WT/70)**0.75*EXP(ETA(3))
V2=THETA(3)*(WT/70)*EXP(ETA(2))*(1+THETA(11)*(HB‐10.3))
Q3=THETA(4)*(WT/70)**0.75
V3=THETA(5)*(WT/70)
Q4=THETA(6)*(WT/70)**0.75*EXP(ETA(5))
V4=THETA(7)*(WT/70)*EXP(ETA(4))
ALAG1=THETA(8)
F1=1*(1‐THETA(9)*(FEV))
IF (GRP.EQ.3.AND.DOSN.EQ.1) F1=1*(1‐THETA(9)*(FEV))*(1‐THETA(10))
S2=V2
S3=V3
$ERROR
IPRED =LOG(F+0.001)
IRES=DV‐IPRED
IWRES=IRES/1
Y=LOG(F)+ERR(1)
$THETA
(0,0.4107,5) ; KA
(0,70.8289,600) ; CL
(0,10502.4,100000) ; V2
(0,44.8867,500) ; Q3
(0,28499.2,100000) ; V3
(0,273.5786,4000) ; Q4
(0,13137.0,120000) ; V4
(0,0.0307,1) ; LAG
(0,0.0280,.8) ; F‐FEV
(0,0.0584,.5) ; F31
(0,0.0156,.2) ; V2‐HB
$OMEGA BLOCK(5)
0.21 ; IIV‐KA
0.017 ; R (KA,V2)
0.29 ; IIV‐V2
0
0.14 ; R (V2,CL)
0.1789 ; IIV‐CL
0
310
0
0.01 ; R (CL,V4)
0.078 ; IIV‐V4
0
0
0
0.06 ; R (V4,Q4)
0.10 ; IIV‐Q4
$SIGMA
0.0376 ; Proportional error for naphthoquine
$EST MAX=9990 SIG=3 METHOD=COND INTER POSTHOC
$COV PRINT=E
311
$PROB Artemisinin (from ART‐NQ) in Children Final Model
$DATA ART_NQ_DATA.CSV IGNORE #
$INPUT # ID TIME CONC=DV DOSE=AMT GRP DOSN FVR DSKG SEX SPLN MAL PARA AGE FEV HB
$SUBROUTINES ADVAN4 TRANS4
$PK
CL=THETA(1)*((WT/70)**(0.75))*EXP(ETA(1))
V2=THETA(2)*(WT/70)*EXP(ETA(1)*THETA(8))
Q=THETA(3)*((WT/70)**(0.75))
V3=THETA(4)*(WT/70)
KA=THETA(5)*EXP(ETA(3))
F1=1
IF(DOSN.EQ.2) F1=1*(1‐THETA(6))
ALAG1=THETA(7)*EXP(ETA(2))
S2=V2
S3=V3
$ERROR
IPRED =LOG(F+0.001)
IRES=DV‐IPRED
IWRES=IRES/1
Y=LOG(F)+ERR(1)
$THETA
(0,544.3046) ; CL
(0,202.6390) ; V2
(0,5.2472) ; Q
(0,61.5488) ; V3
(0,1.5661) ; KA
(0.01,0.7709,.9) ; F2
(0,0.3301,1) ; LAG
(0,1.1865,10) ; ETA‐R
$OMEGA BLOCK(3)
0.2 ; IIV‐CL
0.1 ; R (CL,LAG)
0.6 ; IIV‐LAG
0.1 ; R (CL,KA)
.07 ; R(LAG,KA)
.23 ; IIV‐KA
$SIGMA
0.22 ;Proportional Error for artemisinin
$EST MA
312
X=9990 SIG=3 METHOD=COND INTER POSTHOC
$COV PRINT=E