pharmacometrics: a shot in the arm for vaccine …

PHARMACOMETRICS: A SHOT IN THE ARM FOR VACCINE DISCOVERY AND DEVELOPMENT

~OR~VACCINES ARE NOT IMMUNE

TO THE CHARMS OF PHARMACOMETRICSPAGE 2019 Meeting, Stockholm, Sweden

Jeff SachsPharmacokinetics, Pharmacodynamics, and Drug Metabolism – Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Kenilworth, NJ, USA

June 12, 2019

Outline

• Motivation – Impact of vaccines• Potential impact for PMXMotivation

• Vaccines & ImmunologyBackground• Applications• Their impactPMX

2

Many Diseases Have Been Prevented

3

Halsey, NA, How Vaccines Cause Adverse Events, ADVAC Course Annecy France 2018

https://www.forbes.com/sites/matthewherper/2013/02/19/a-graphic-that-drives-home-how-vaccines-have-changed-our-world/ - 1a58e6dd3302

Age < 5Age ≥ 5

Polio

4

Historical Perspective: Smallpox

5

Bazin, H., Vaccination: a History

Don’t Count Your Children Until The Measles Have Come Through– African saying

6

20.3 Million*

*MMWR / November 11, 2016, 65 (44) 1228-1233

The Modern Toll of Measles

EU region2018

83,000 cases50,000 hospitalized

72 deaths1

Philippines1Q 2019

33,000 cases> 450 deaths3

7

1 European Region statistics. From “Measles in Europe: record number of both sick and immunized,” WHO Regional Office for Europe, Copenhagen, 7 February 20192 http://www.euro.who.int/en/media-centre/sections/press-releases/2019/over-100-000-people-sick-with-measles-in-14-months-with-measles-cases-at-an-alarming-level-in-the-european-region,-who-scales-up-response3 https://www.npr.org/2019/05/19/724747890/measles-outbreak-in-the-philippines

EU region1Jan18 – 8May19

100,000 cases>90 deaths2

Health and Economic Impact of Preventing Disease with Vaccination…

Just One Country: USAJust One Year’s Cohort: Children born in 2001

8

Adapted from: Zhou F et al. Arch Pediatr Adolesc Med. 2005;159:1136–1144.

All costs are given in US dollars (USD).Direct program costs included vaccines, administration, parent travel, and direct costs for the management of adverse events. Societal costs included direct program costs and parent time lost for vaccination and the management of adverse events.

>13 Million Cases Prevented > $53 Billion Saved

About 50 Vaccines Developed to Date

9

Adapted from: IOM (Institute of Medicine), Ranking vaccines: A prioritization framework: Phase I: Demonstration of concept and a software blueprint. Washington, DC, The National Academies Press, 2012, p. 19.

Number of Vaccines Developed:~30 as of 1990, ~50 as of 2010

1800 1900 2000

Cumu

lative

Num

ber

30

50

Smallpox10

…But Expensive and Takes Too Long

• Cost of a vaccine from discovery through Ph. 2a: $0.4 Billion (range $0.1-1B)*

• Time for a vaccine from discovery through Ph. 2a: 7 years (range 4-15 years)*

• Vaccines too often in development for ~20 years

10

Pharmacometrics

*Gouglas, D., TT Le, et al., Estimating the cost of vaccine development against epidemic infectious diseases: a cost minimisation study, Lancet Glob. Health, 2018;6:e1386–96

(Typically)• In Phase 3, 18,000 subjects

total, 3 years (range 1-5)

• Time: enrollment + low incidence rate

“Vaccine”

11

Vaccine (for today): Active Stimulator Of Immune Memory and Antibody Production for Prevention of an Infectious Disease.

12

Therapeutic

Chemo-Prophylaxis

PassiveImmunization

Seasonal Flu

Jeryl Lynn Hilleman with her sister, Kirsten, in 1966 getting the mumps vaccine developed by their father.

What is Special About Vaccines and Pharmacometrics?

Why were Vaccines not on our radar??• PK* Rare• Little DDI* (concomitant vaccination)• Traditional clinical pharmacology analyses not typical

• except safety & Tox not part of our traditional purview.

13*PK: pharmacokinetics, DDI: drug-drug interaction

The BASICS:VACCINES and IMMUNOLOGY

14

Active Immunization – How it works

• Measuring Immune response: “Titer” ~ Target engagemento Quantity and quality of antibodies

• More is better

15

Immunogenicity ≠ efficacy

Immune Memory

Recognize pathogen

Ramp up defenses quickly

Reduce symptomsor prevent disease

Overview of PMX and Vax

16

Vaccine Pharmacometrics

Modeling and Simulation in Vaccines

17

Computational vaccinology:

EpidemiologyHealth Econ

chem eng. & SYS BIO for bioprocess

PKPDof mAb

(“passive immu-

nization”)

QSP/PKPD in Chemo-

prophylaxis

immuno-genicity

= F(antigensequence)


(Today’s focus)

Rich History of Published Work (not a complete list!)

18

Hobson D, et al. (1972) The role of serum haemagglutination-inhibiting antibody in protection against challenge infection with influenza A2 and B viruses. J Hyg Camb. 70:767.

Fraser C, et al. (2007) Modeling the long-term antibody response of a human papillomavirus (HPV) virus-like particle (VLP) type 16 prophylactic vaccine. Vaccine. 25:4324–4333.

Coudeville L, et al. (2010) A new approach to estimate vaccine efficacy based on immunogenicity data applied to influenza vaccines administered by the intradermal or intramuscular routes. Hum Vaccin. 6(10):841–848.

Desai K, et al. (2012) Modelling the long-term persistence of neutralizing antibody in adults after one dose of live attenuated Japanese encephalitis chimeric virus vaccine. Vaccine. 30:2510–2515.

Andraud M, et al. (2012) Living on Three Time Scales: The Dynamics of Plasma Cell and Antibody Populations Illustrated for Hepatitis A Virus. PLOS Computational Biology. 8(3).

Le D, et al. (2015) Mathematical modeling provides kinetic details of the human immune response to vaccination. Front Cell Infect Microbiol. 4:177

Gómez‐Mantilla JD, et. al. ADME processes in vaccines and PK/PD approaches for vaccination optimization. In: ADME and tPK/PD. Wiley; 2016.

Rhodes SJ, et al. (2016) The TB vaccine H56 + IC31 dose-response curve is peaked not saturating: Data generation for new mathematical modelling methods to inform vaccine dose decisions. Vaccine. 34(50):6285-6291.

Rhodes, SJ, et al. (2018) Using vaccine Immunostimulation/ Immunodynamic modelling methods to inform vaccine dose decision-making. npj Vaccines. 3:36.

Rhodes SJ, et al. (2019) Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling. J Theor Bio. 465:51-55.

18

Barbarossa, et al. (2015). Mathematical models for vaccination, waning immunity and immune system boosting: a general framework. arXiv:1501.03451.

Atcheson E, et al. (2019) A probabilistic model of pre-erythrocytic malaria vaccine combination in mice. PLoS One. 14(1):e0209028.

Modelling dose responses following vaccination: lessons from the PK/PD field Rockville, Maryland, May 29, 2014Organizers:

BMGF: Steve Kern LSHTM: Richard White, Sophie Rhodes Imperial College: Gwen Knight AERAS: Tom Evans, Lewis Schrager

Modelling dose responses following vaccination: lessons from the PK/PD field Rockville, Maryland, May 29, 2014Organizers:

BMGF: Steve Kern LSHTM: Richard White, Sophie Rhodes Imperial College: Gwen Knight AERAS: Tom Evans, Lewis Schrager

www.lshtm.ac.uk/research/centres-projects-groups/isid#welcome

EXAMPLES

19

Key question, Method, Decision / ImpactPhase Key Question Method(s) Decision(s) / Impact

20

Enabling Translation

Understanding TE* Biomarkers Using Past Data

Leveraging TE* Biomarkers for Trial Design

Making Earlier Decisions with Quantified Risk

Supporting Regulatory Interaction

*TE: Target Engagement (stimulating protective immune response)

Seven Examples

Capability DemonstrationProgram Impact


1/2What N (# subjects)

will let us tell if vaccines A and B are different?

Phenomenological model Clinical trial simulation

Trial design, program strategy for sequence of trials

21

Wrong Question!

What can we learn about dose-level & formulation impact on immunogenicity?How can we use past data to inform the trial design?How can we integrate data across trials in the future?

22

How many arms (and which ones) were needed to address information desired in the first question?

Number of arms the team had planned:

Trial Design by Phenomenological Simulation

Formulation 2

Low Med HighLow N12Med N15High N18

Formulation 1

Low Med HighLow N1 N2 N3Med N4 N5 N6High N7 N8 N9

Dose

of

Antig

en se

t B

Dose of Antigen set ASimulate

each of the 18 possible arms

Estimate power to detect dose /

formulation effects

Powe

r (%

)

High dose increases response by factor on x-axis

0 2 4 6 8 10 12 14 16 18 20-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Formulation 1 Formulation 2

Log

(titer

)

Simulation Trial Arm #

Comp

arato

r

Choose trialarms & sizes

Formulation 2

Low Med HighLowMedHigh

Formulation 1

Low Med HighLow NMed N NHigh N

Dose

of

Antig

en se

t B Dose of Antigen set A

Covariance of Clin. Immune response &

form of R = R(Dose, Formulation)

Thanks: Kapil Mayawala, Jon Hartzel

23

Trial Design by Phenomenological Simulation

Formulation 2

Low Med HighLow N12Med N15High N18

Formulation 1

Low Med HighLow N1 N2 N3Med N4 N5 N6High N7 N8 N9

Dose

of

Antig

en se

t B

Dose of Antigen set ASimulate

each of the 18 possible arms

Estimate power to detect dose /

formulation effects

Powe

r (%

)

High dose increases response by factor on x-axis

0 2 4 6 8 10 12 14 16 18 20-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Formulation 1 Formulation 2

Log

(titer

)

Simulation Trial Arm #

Comp

arato

r

Choose trialarms & sizes

Formulation 2

Low Med HighLowMedHigh

Formulation 1

Low Med HighLow NMed N NHigh N

Dose

of

Antig

en se

t B Dose of Antigen set A

Covariance of Clin. Immune response &

form of R = R(Dose, Formulation)

Thanks: Kapil Mayawala, Jon Hartzel

24

Impact: • changed from 2-arm “yes-no” study to 5-arms (same

total # subjects), optimized to learn key properties • Helped plan:

(1) next studies, and(2) how to integrate data across studies


2 What disease assay(s) should we use and how often?

QSP and Bayesian probabilistic

Saved $, increased POSChoice of assay, frequency

25

• Measuring vaccine efficacy requires counting number of disease cases.

• What happens if the counting (assay) process is not perfect?

• What assays should we use and how often?

Placebo Vaccinated

Efficacy = Proportional Risk Reduction10% of placebo subjects get sick,3% vaccinated subjects get sick

Efficacy = (10% - 3%) / 10% = 0.7 = “70% efficacy”

26

Placebo Vaccinated

Efficacy = Proportional Risk Reduction10% of placebo subjects get sick,3% vaccinated subjects get sick

Efficacy = (10% - 3%) / 10% = 0.7 = “70% efficacy”

27

Typically need tens/hundred cases (Ph. 2/3, resp.) No efficacy information until Ph. 2b/3

10% of placebo subjects get sick,3% vaccinated subjects get sick

Efficacy = (10% - 3%)/10% = 0.7 = “70% efficacy”Efficacy = (13% - 6%)/13% = 0.54 = “54% efficacy”

Efficacy = Proportional Risk Reduction

28

Placebo Vaccinated

Daniel Rosenbloom, Nitin Mehrotra

Impact of False Positives

+3%+3%

Repeat testing Smart testing

Ph. 2/3 DesignMore assays:

Trade Time for powerDuration of

detectability matters

+ Pathogen Dynamics

13%6%

Radh

a Ra

ilkar,

Dan

iel R

osen

bloom

, Cas

ey D

avis

Shorter-duration infection

Longer-duration infection

10% of placebo subjects get sick,3% vaccinated subjects get sick

Efficacy = (10% - 3%)/10% = 0.7 = “70% efficacy”Efficacy = (13% - 6%)/13% = 0.54 = “54% efficacy”

Efficacy = Proportional Risk Reduction

29

Placebo Vaccinated

Daniel Rosenbloom, Nitin Mehrotra

Impact of False Positives

+3%+3%

Repeat testing Smart testing

Ph. 2/3 DesignMore assays:

Trade Time for powerDuration of

detectability matters

+ Pathogen Dynamics

13%6%

Radh

a Ra

ilkar,

Dan

iel R

osen

bloom

, Cas

ey D

avis

Shorter-duration infection

Longer-duration infection

Impact:• Saved $, • increased POS (per subject)• Choice of assay(s) and their frequency


(na) Can we model enough of the immune system to be predictive? QSP Capability development

30

How can we use preclinical data to help design vaccines and to predict the right dose-level or regimen?

Basic Model of Some Immune System Components

31

Chen et al., CPT PSP 2014

Dendritic cells

T cells

Antigen presentation

B cellsPlasma cells

PK

Mechanistic QSP

2 6 10Time (months)

0

2

4

100

200

Antib

ody l

evel

Ab tit

ers

Thanks: Jeff Perley, Josiah Ryman

300

Basic Model of Some Immune System Components

32

Chen et al., CPT PSP 2014

Dendritic cells

T cells

Antigen presentation

B cellsPlasma cells

PK

Mechanistic QSP

2 6 10Time (months)

0

2

4

100

200

Antib

ody l

evel

Ab tit

ers

Thanks: Jeff Perley, Josiah Ryman

300Impact: Capability Development


4How many doses of vaccine are

needed to confer lasting protection?

QSPSuggests single dose could provide

protective immune memoryMechanistic insight

33

Can we leverage mechanistic information to help inform

regimen?

Hepatitis B: Models for Antigen, Anti-viral Titer and Immune Memory

34

Antigen

Immune memory

Anti-viral titer

• 10,815 anti-viral titres in 1,923 patients• 2-4 vaccinations in 6-48 month period• No Immune memory (Mi) or antigen (Vi) measurements

Wilson JN, Nokes DJ, Medley GF, Shouval D. Mathematical model of the antibody response to hepatitis B vaccines: implications for reduced schedules. Vaccine. 25(18):3705-12. 2007

35

• The model demonstrates significant differences between different vaccines in both the time taken to generate immune memory and the amount of memory generated.

• The model provides theoretical support for the hypothesis that a single vaccine dose can generate protective immune memory.

Authors’ Conclusions

Simulation Results

30Time (days)

0

24

6

8

Memo

ry (M

, mlU

/ml) 10

12

90 150 210 270 33030Time (days)

0

6

10

15

Vacc

ine an

tigen

(V, ų

g)20

25

90 150 210 270 330


36

• The model demonstrates significant differences between different vaccines in both the time taken to generate immune memory and the amount of memory generated.

• The model provides theoretical support for the hypothesis that a single vaccine dose can generate protective immune memory.

Authors’ Conclusions

Simulation Results

30Time (days)

0

24

6

8

Memo

ry (M

, mlU

/ml) 10

12

90 150 210 270 33030Time (days)

0

6

10

15

Vacc

ine an

tigen

(V, ų

g)20

25

90 150 210 270 330


Impact:• Suggests single dose might be

enough (for immune memory for this pathogen and vaccine mechanism)

• Mechanistic insight


2Which regimens should be tested

in Ph. 2 Trial?(Regimen: dose‐level, # doses, timing)

QSP Ph. 2 Trial Design: add new dose level and different regimen

37

Can we leverage mechanistic information to help inform

regimen in Ph. 2 trial?

NHP Data

Trial Design by QSP

38

• Predicted response to different dose-levels, regimens, formulations

• Predictions qualified with Ph1 data

• Changed Ph. 2 design to incorporate a lower dose-level, additional regimen

Thanks: Jeff Perley, Guido Jajamovich, Jos Lommerse (Certara), April Barbour

Literature Data(mostly non-clinical)

Clinical Data Overlaid on Translated Prediction

2Time (months)

101

102

103

104

104

Neutr

alizin

g Anti

body

Titer

s

105

4 6 8 10 12

106

0

200Time (Days)

2

3

4

Antib

ody T

iter (

log)

4000

Dose 300

200Time (Days)

4000

Dose1000

Antib

ody T

iter

Immune Cells1

Immune Cells2

Immune Cells3

Ab titer

NHP Data

Trial Design by QSP

39

• Predicted response to different dose-levels, regimens, formulations

• Predictions qualified with Ph1 data

• Changed Ph. 2 design to incorporate a lower dose-level, additional regimen

Thanks: Jeff Perley, Guido Jajamovich, Jos Lommerse (Certara), April Barbour

Literature Data(mostly non-clinical)

Clinical Data Overlaid on Translated Prediction

2Time (months)

101

102

103

104

104

Neutr

alizin

g Anti

body

Titer

s

105

4 6 8 10 12

106

0

200Time (Days)

2

3

4

Antib

ody T

iter (

log)

4000

Dose 300

200Time (Days)

4000

Dose1000

Antib

ody T

iter

Impact:• Increased confidence in ability to

• model regimen-response• Translate from non-clinical species

• Changed planned Phase 2 • dose-levels • number of doses


2,3Do we have adequate evidence of efficacy if some pathogens have too

few cases?

PoDBA ( = Probability of Disease

Bayesian Analysis)

Novel Ph. 3 endpointGNG test criteria

40

Can we increase POS by mitigating risk of a season with

low incidence rate?

PoDBA Method

• Estimate relationship between probability of disease and antibody titer values based on titer values of subjects with and without disease

41

Log Titer0

Prob

abilit

y of

Dis

ease

And

Tite

r Den

sity

Prob

abilit

y of

Dis

ease

Antibody Titer Value

vaccineplacebo

• Use this relationship and titer values of control and vaccine groups to estimate vaccine efficacyand its confidence interval

Prob

abilit

y of

Dis

ease

Antibody Titer Value

Wait for enough cases

Needmanycases

Method: Estimating the “Probability of Disease” Curve

• Use titer values measured in infected and non-infected subjects• Assume that the relationship between titer values and probability of disease follows a sigmoidal curve• Estimate the parameters of the curve and their confidence intervals using standard statistical method (Maximum likelihood)

log2 (Titer value)

Cou

nt

alldiseased

sample ofnon-diseased

Prob

abili

ty o

f dis

ease

log2 (Titer value)ET50

Pmax

slope

Pmax/2

0

Maximum likelihood estimate

Observed Estimated

42

Qualified PoDBA method & Efficacy CI (demonstrated predictive power) with published data and simulation

PoDBA Novel Endpoint (example from a program plan, not agency guidance on different approaches to basis of licensure)

43

case-count alone

+PoDBA

YesYesTrial P001

No

No

No correlate

(With correlate)

TBD

TBD

Yes

Impact:• Increased capability to develop “Correlate

of Protection”• Developed potential Correlate of Protection• Increased Power of planned trials

increased POS for same # subjects• Method for early decision and

understanding of covariates


2Is our immunogenicity likely to

provide the necessary protection, and at what dose‐level?

NLME+ MBMA + PoDBA(Comparator modeling +

PoDBA)No‐go, GO, Dose‐selection

44

Can we leverage literature data and early immunogenicity data to drive early, objective, risk-

based decisions?

Incid

ence

Rat

e

Dose-level

Incid

ence

Rat

e

Titer

Modeling Overview for Supporting Both Go and No-Go Decisions

1.Titer Incidence Rate (“IR”)• Published clinical data• Incidence rate for different disease levels• Data cover various populations

Dose-level Titer Incidence RatePredicted change in incidence rate efficacy

Tite

r

Dose-level

Vaxpbo

Efficacy =% Drop

3.Combine 1 & 2 “integrated” modeling:

2.Dose-level Titer• Relate dose-level to serum neutralization titer response• FIH Data

45

Visualization Has to Tie Together Data for Different Disease Levels and Populations

Titer

Incide

nce

rate

in Hu

mans

(%/ye

ar)

Population

Di

seas

e se

verity

leve

l

46

Titer

Incide

nce

rate

in Hu

mans

(%/ye

ar)

Pbo VaxA B C

12

3

Predicted biomarker-incidence rate relationship for population B at disease severity level 2 with 95% prediction interval

Data point weight 246

PopulationABC

Disease severity level123


Titer

Incide

nce

rate

in Hu

mans

(%/ye

ar)

Population

Di

seas

e se

verity

leve

l

47

Titer

Incide

nce

rate

in Hu

mans

(%/ye

ar)

Pbo VaxA B C

12

3



PopulationABC


Pr( efficacy > E ) > T

E = sufficient for intended public health impactT = tolerance of organization for risk


Titer

Incide

nce

rate

in Hu

mans

(%/ye

ar)

Population

Di

seas

e se

verity

leve

l

48

Titer

Incide

nce

rate

in Hu

mans

(%/ye

ar)

Pbo VaxA B C

12

3



PopulationABC


Pr( efficacy > E ) > T

E = sufficient for intended public health impactT = tolerance of organization for risk

Impact:Objective, Quantitative…

No-Go: $40M trialGo: $10M trial

Dose-level, Biological insight, …


2What N (# subjects)

will let us tell if vaccines A and B are different?

Phenomenological model Clinical trial simulation

Trial design, program strategy for sequence of trials

2 What immunogenicity assay(s) should we use and how often?

QSP and Bayesian probabilistic

Saved $, increased POSChoice of assay, frequency

(na) Can we model enough of the immune system to be predictive? QSP Capability development

4 How many doses of vaccine are needed to confer lasting protection? QSP Suggests 1 less dose

Mechanistic insight

2Which regimens should be tested

in Ph. 2 Trial?(Regimen: dose‐level, # doses, timing)

QSP Ph. 2 Trial Design: add new dose level and different regimen

2,3 Do we have adequate evidence of efficacy if some pathogens have too few

cases?

PoDBA ( = Probability of Disease

Bayesian Analysis)

Novel Ph. 3 endpointGNG test criteria

2Is our immunogenicity likely to provide the necessary protection, and at what

dose‐level?

NLME+ MBMA + PoDBA(Comparator modeling +

PoDBA)No‐go, GO, Dose‐selection

49


Today• Simulation-based trial design to add/save

trial arms or subjects• QSP modeling of the immune system as a platform• Using dose, regimen, formulation to predict

immunogenicity/efficacy• Translation between preclinical and

clinical immunogenicity• Leveraging literature data• Establishing new trial endpoints• Understanding covariate (age, genetics, geography,…)

effects on immunogenicity & efficacy

Don’t forget also…• Predicting most effective vaccine platform by

mechanistic modeling• Understanding or predicting safety/toxicity• Predicting the best route of administration• Leveraging results of real-world trials• Prioritizing vaccine candidates• Prioritizing pathogen candidates

50

CONCLUSION

51

52Source: https://www.ag.ndsu.edu/news/columns/beeftalk/beeftalk-an-ounce-of-prevention-is-worth-a-pound-of-cure/.

53

NSAID Anti-viral

Anti-biotic

54

• Vaccines are a key component of public health• Vaccines are a key component of public health• Pharmacometrics useful for vaccine discovery & development

‐ QSP, PK/PD, Bayesian, comparator, translational,…• These (and other) methods have impacted decisions• Pharmacometrics (we) can impact human health by helping

inform vaccine discovery & development‐ Assumptions, study design & strategy, data interpretation

Acknowledgements• Subjects (& their Parents)

• MSD– Discovery & Development Teams, PPDM-Bioanalytics– Vaxmodsim team+:

Luzelena Caro, Carolyn Cho, Julie Dudasova, Pavel Fiser, Jon Hartzel, Sean Hayes, Justina Ivanauskaite, Regina Laube, Brian Maas, Nitin Mehrotra, Jeff Perley, Seth Robey, Radha Railkar, Daniel Rosenbloom, Josiah Ryman

– Paula Annunziato and Daria Hazuda– Dinesh de Alwis & Vikram Sinha, Nancy Agrawal– PPDM-QP2– Mike Pish & co. @ MCS– Skip Irvine, John Grabenstein

• Also…– Certara, Inc: Jos Lommerse, Nele Mueller-Plock, Michelle Green, Amy Cheung many others– Former MSD: Matt Wiener, Sandy Allerheiligen

55

Backup

PROPRIETARY ICONS HERE 56

Attributes of Vaccine Development

1. Stringent safety2. Trial size and duration (event frequency)3. Efficacy = proportional risk reduction4. Surrogate marker (“Correlate of Protection,” a.k.a. “CoP”) challenges5. Lack of translational models6. Need arm with placebo or active comparator7. Complex biologics, need to be transportable stable, usable

57

CoP Challenges: Knowledge, Time, Resources, Variability…

58

Knowledge• Which measurements?• Which species?• Predictive power?

Resources• Many samples• Often multiple species• Resource-intensive

assays

Time• Knowledge early enough• Timely availability of

clinical data

Variability• Assays often +/- 2-fold• Large BSV in response• Variability in CoP predictive

power? BSV: between-subject variability

pharmacometrics: a shot in the arm for vaccine …

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