1 case studies in modeling and simulation discussion stella g. machado, ph.d. office of...

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1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

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Page 1: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

1

Case Studies in Modeling and Simulation

Discussion

Stella G. Machado, Ph.D.

Office of Biostatistics/OTS/CDER/FDA

FDA/Industry Workshop, September 2006

Page 2: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

2

Regulatory issue

• Approval was sought for monotherapy for pediatric population, without another clinical trial

• Clinical trial data for Drug X: – Adults: adjunct and monotherapy– Pediatric population: adjunct

• PK/PD modeling used for bridging the adjunct therapy data (data masked)

Page 3: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

3

Bridging PK/PD Studies• General method comparing PK/PD response curves in:

Pediatric versus Adult populationsDifferent Regions

• Exposure: dose, AUC, Cmin, etc

• Response: biomarkers, clinical endpoints

• Goal is to evaluate similarity in PK/PD relationships between 2 populations

Conclude: similarity, similarity with some dose regimen modification; lack of similarity

Page 4: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

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0 20 40 60 80 100 120

Conc.

0

10

20

30

40

50

Res

pons

e

NewOriginal

DRUG X: PK/PD scatter plot with loess fits

Page 5: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

5

STEPS IN THE STATISTICAL APPROACH

• assess similarity between responses at all concentrations likely to be encountered

• account for variability of the response

• need “Equivalence” type approach, not hypothesis tests showing that the responses are not significantly different

• analysis is more “exploratory” than “confirmatory”

Page 6: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

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Steps• Usual equivalence-type analysis:

– “similarity” defined as requirement that average responses in the 2 populations, at the same C, are closely similar:

– choose reference “goalposts” L and U, eg 80% to 125%

– calculate 95% confidence interval for ratio of average

responses (1 / 0) for “all” C

Page 7: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

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EXAMPLE: Drug X

• Response transformed by square root to stabilize the variance

• Linear models fitted separately for the two populations:

• sqrt(response) = a + b * Conc +

• For each C, 5000 pairs of studies generated 5000 estimates of 1/0, and percentiles

Page 8: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

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DRUG X: 95% CI’s for ratios 1/0 for concentrations: 0, 20,50,70,90 via

model-based method

0 40 80 120

Conc

0.6

1.0

1.4

1.8

95

% c

on

fid

en

ce

bo

un

ds f

or

sim

ila

rity

Page 9: 1 Case Studies in Modeling and Simulation Discussion Stella G. Machado, Ph.D. Office of Biostatistics/OTS/CDER/FDA FDA/Industry Workshop, September 2006

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Remarks for example

• Response higher for pediatric population for concentrations above 50mg

=>Shows lack of similarity, but dose adjustment would be possible if high concentrations are called for

• Limits of (80, 125) might not be medically most sensible for interpretation in each situation