20150526 icu onderwijs - intensivistenopleidingintensivistenopleiding.nl/downloads-25/files/doseren...

How to adapt dosing in cri/cally ill pa/ents

Dr. Roger Brüggemann clinical pharmacologist, clinical pharmacist Nijmegen, The Netherlands

http://www.fungalpharmacology.org

•  Adap6ng the dose prac6cally? •  Based on toxicity? •  Based on efficacy?

•  Therapeu6c Drug Monitoring = personalized healthcare

3

PERSONALIZED …..

Define PK

Define covariates

Define dose -‐ PD Define PK-‐PD

TDM – adjust dose

Define PK

Between-‐subject variability (BSV, IIV)

Concen

tra6

on

Time

Ind. predic6on

Residual error (ε): Cobs - Cpred

BSV (η): θind (CL,V) – θpop (CL,V)

Pop. predic6on

6

Methods – stochas/c model iden/fica/on

Slide from T. Dorlo

Goodness-of-fit plots for

Mar6al et al; manuscript under prepara6on

Goodness of fit Caspofungin in ICU pa/ents

Results simula/on study

Concentratio

Licensed regimens

70/50 or 70/70 70/35

100/50 100/70 100/100

Experimental regimens

Pharmacokine/cs of Caspofungin in Intensive Care Unit pa/ents

(CASCADE)

Pharmacokine/cs of Micafungin in Intensive Care Unit pa/ents

(MIMIC)

Muilwijk et al. JAC 2014 Lempers et al. AAC 2015

Pharmacokine?cs -‐ parameters

Median pharmacokine6c parameters (IQR) for PK curve 1 (day 3 +/-‐ 1) and PK curve 2 (day 7 +/-‐ 1)

10

AUC0-‐24u [mg*h*L-‐1]

Cmin

[mg/L] Cmax

[mg/L] Vd

[L] CL

[L*h-‐1] Caspofungin

PK curve day 3 (n=21)

88.7 (72.24-‐97.54)

2.15 (1.40-‐2.48)

7.51 (6.05-‐8.17)

7.72 (6.12-‐9.01)

0.57 (0.54-‐0.77)

PK curve day 7 (n=13)

107.2 (90.39-‐125.28)

2.55 (1.82-‐3.08)

8.65 (7.16-‐9.34)

7.03 (5.51-‐7.73)

0.54 (0.44-‐0.60)

Micafungin

PK curve day 3 (n = 20)

78.60 (65.29 -‐ 94.07)

1.55 (1.39 -‐ 3.14)

7.22 (5.40 -‐ 9.22)

25.61 (21.32 -‐ 29.07)

1.27 (1.07 -‐ 1.53)

PK curve day 7 (n = 12)

65.71 (55.85 -‐ 88.73)

1.55 (1.30 -‐ 2.44)

6.16 (5.14 -‐ 9.22)

28.66 (16.76 -‐ 32.12)

1.52 (1.17 -‐ 1.79)

Significantly different

Not significantly different

Results Comparing day 3 of therapy with day 7

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0 4 8 12 16 20 240

2

4

6

8

Day 3

Day 7

Time after dose (h)

Mic

afun

gin

conc

entra

tion

(mg/

L)

Caspofungin Micafungin

Results Daily trough concentra?ons increase gradually

12

1 2 3 4 5 6 7 8 9 10 11 12 130

2

4

6

8

10

Study day 1 2 3 4 5 6 7 8 9 10 11 12 13 Cmin (n) 17 19 18 16 16 12 6 5 5 5 3 3 3 GM conc. 0.25 1.57 1.93 1.85 1.89 1.74 1.83 1.88 1.82 1.71 1.82 2.44 1.99

Ratio* -‐ 6.39 1.23 0.96 1.02 0.92 1.06 1.03 0.97 0.94 1.06 1.34 0.82

* Previous day / current day

Days of micafungin therapyM

icaf

ungi

n co

ncen

tratio

n (m

g/L)

Define covariates

17 Wim van het Hof -‐ AKA the Iceman at Radboudumc ICU -‐ Kox et al, PNAS April 2014

Results No significant covariates detected

R² = 0,01378

0

50

100

150

200

0,0 10,0 20,0 30,0 40,0

AUC0

-‐24h

*mg/L

Albumin (g/L)

Albumin vs. AUC0-‐24

R² = 0,01653

0

50

100

150

200

0,0 10,0 20,0 30,0 40,0 50,0

AUC0

-‐24h

*mg/L

APACHE II score

APACHE II vs. AUC0-‐24

R² = 0,02905

0

50

100

150

200

0,0 5,0 10,0 15,0 20,0

AUC0

-‐24h

*mg/L

SOFA score

SOFA vs. AUC0-‐24

R² = 0,05522

0

50

100

150

200

0,0 50,0 100,0 150,0

AUC0

-‐24h

*mg/L

Bodyweight (kg)

Bodyweight vs. AUC0-‐24

Structural model

Covariate model

Stochas6c (sta6s6cal) model

E.g. Between-‐subject, -‐occasion & residual

variability

E.g. 2-‐ or 3-‐ compartment model, zero-‐ or first-‐order elimina6on

Rela6onship between parameters and

demographics (e.g. age or weight)

Slide with permission from Thomas Dorlo

Stepwise Covariate Modelling (SCM) Covariate Parameter Condi6on tested Criterion

Con6nuous=AGE,WT,HT, LBM,BMI,TMP,CREA, UREA, ALB,ALAT,ASAT,GGT, AF,BILI,CRP,PH,CP,GFR,APAC,SOFA

CL, V1

None, linear, exponen6al & power

p forward = 0.05 p backward = 0.01

Categorical=SEX,GFRN None & linear

GFR as a measure of renal func6on both con6nuous as categorical (normal/abnormal i.e., >60 mL/min or not)

Define dose -‐ PD

Define PK-‐PD

24

Absorp6on Clearance PB

Toxicity

Resistance

Target: cidal / sta6c pharmacodynamics

Virulence

Underlying disease Phase of Therapy Host defense

The drug -‐ the bug -‐ the host

25

Conclusions. Infected critically ill patients may have adverse outcomes as a result of inadeqaute antibiotic exposure;a paradigm change to more personalized antibiotic dosing may be necessary to improve outcomes for these most seriously ill patients.

Discussion Micafungin exposure response rela?ons

1Hebert MF et al. J Clin Pharmacol 2005;45(8):954-‐960. 2Hebert MF et al. J Clin Pharmacol. 2005;45(9):1018-‐1024. 3Undre N et al. Europ J Drug Metab. Pharm. 2014. 4Hebert MF et al. J Clin Pharmacol 2005;45(10):1145-‐1152. 5Undre NA et al. Open J Med Microbiol 2012b;2(3):84-‐90. 6Hiemenz J et al. AAC, 2005;49(4):1331-‐1336. 7Maseda E et al. JAC 2014;69(6):1624-‐1632.

70 mg / 50 mg (BW≤80 kg) or 70/ 70 mg (BW>80 kg) 70 mg / 35 mg

100 mg / 50 mg 100 mg / 70 mg 100 mg / 100 mg


AUC on day 14 (mg*h/L) AUC on day 14 (mg*h/L) AUC on day 14 (mg*h/L)

AUC on day 14 (mg*h/L) AUC on day 14 (mg*h/L)

Caspofungin AUC obtained aver different dosing regimen

Target awainment (pre-‐clinical target AUC/MIC of 865)

0.016 0.0

30.0

60.1

25 0.25 0.5 1

0

20

40

60

80

10070/70 or 70/50*70/35100/50100/70100/100

MIC

% T

arge

t att

ainm

ent

Target awainment versus MIC for all five simulated regimens based on a preclinical target AUC/MIC ra6o of >865. *Regimen is based on BW. Maintenance dose was 50 mg for pa6ents with BW≤80 kg and 70 mg for BW>80kg.

Andes D, Diekema DJ, Pfaller MA, Bohrmuller J, Marchillo K, Lepak A. In vivo comparison of the pharmacodynamic targets for echinocandin drugs against Candida species. An6microbial agents and chemotherapy. 2010;54(6):2497-‐506.


TDM – adjust dose

31

Among critically ill patients with normal kidney function, a strategy of dose adaptation based on daily TDM led to an increase in PK/ PD target attainment compared to conventional dosing.

How to tailor the dose

Courtesy of Michael Neely ; with permission

MwPharm

Slides with permission from Ron Keizer, InsightRx, info@insight-‐rx.com

Concluding remarks •  Timing and dosing of drugs need to be adapted to the individual •  Structured efforts should be put in collec6ng data on PK and PD

•  The current collabora6ons greatly assists in this •  Modeling and simula6on may provide us with alternate (op6mal) new

dosing regimens (ie extended infusion, con6nuous infusions, strategies in seyng of extra-‐corporeal elimina6on).

•  An6bio6c dosing programs will help in predic6ng op6mal dosing of an6bio6cs in cri6cally ill pa6ents


•  My PhD students •  Vincent Lempers •  Lisa Mar6al •  Eline Mulwijk

•  Pharmacy •  Prof. Dr. David Burger •  Angela Colbers •  Dr. Rob Aarnoutse •  Dr. Jan-‐Willem Alffenaar (UMCG)

•  Intensive Care Unit •  Prof. Dr. Peter Pickkers (Radboudumc) •  Prof. Dr. Hans van der Hoeven (Radboudumc) •  Prof. Dr. Jan Bakker (EMC) •  Dr. Dylan de Lange (UMCU) •  Dr. Henk van Leeuwen (Rijnstate) •  Dr. Jeroen Schouten (CWZ) •  Dr. Noortje Swart (VUMc) •  Dr. Arthur van Zanten (Gelderse Vallei)

•  Leiden Academic Center for Drug Research •  Prof. Dr. Catherijne Knibbe

•  Medical Microbiology •  Prof. Dr. Paul Verweij •  Prof. Dr. Johan Mouton

•  Hematology •  Dr. J Peter Donnelly •  Prof. Dr. Nicole Blijlevens •  Dr. Walter van der Velden

•  Pediatrics •  Dr. Adilia Warris (Aberdeen)

•  Pharmacology and Toxicology •  Dr. Jan Koenderink •  Prof. Dr. Frans Russel

•  Interna6onal •  Prof. Dr. Johan Maertens (Leuven) •  Prof. Dr. Isabel Spriet (Leuven) •  Prof. Dr. Joost Wauters (Leuven) •  Prof. Dr. Andrew McLachlan (Sydney)


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