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The Role of Simulation in Assessing Extrapolation Assumptions Marc - - PowerPoint PPT Presentation

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Quantitative Assessment of Assumptions to Support Extrapolation of Efficacy in Pediatrics: FDA-U Maryland CERSI Cosponsored Workshop. FDA White Oak Campus. June 1, 2016 The Role of Simulation in Assessing Extrapolation Assumptions Marc R.

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

Quantitative Assessment of Assumptions to Support Extrapolation of Efficacy in Pediatrics: FDA-U Maryland CERSI Cosponsored Workshop. FDA White Oak Campus. June 1, 2016

The Role of Simulation in Assessing Extrapolation Assumptions

Marc R. Gastonguay, Ph.D.

CEO, Metrum Research Group Scientific Director, Metrum Institute

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

  • What is the added value of quantitative approaches in reinforcing

the total body of evidence to support extrapolation?

  • How can we best design adult drug development programs to
  • btain the necessary information that will help us evaluate

assumptions for extrapolation and also inform the path of extrapolation?

Relevant Points for Discussion

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

http://www.fda.gov/ScienceResearch/SpecialTopics/PediatricTherapeuticsResearch/ucm106614.htm

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

http://www.fda.gov/ScienceResearch/SpecialTopics/PediatricTherapeuticsResearch/ucm106614.htm

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

The Challenge to Sanity

  • How can I judge if the adult or pediatric disease are

similar if I don’t understand the adult disease progression?

Ø How should this (disease progression) be defined and/or

quantified?

  • What are reasonable criteria for assessing “similarity” of

disease?

Ø Do criteria change with the disease? How? Why?

  • The same questions apply to similarity of drug response
  • How can simulation be used to assess these

assumptions, quantitatively?

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

Understand Key Questions and Constraints Define Prior Knowledge/Data Sources

Identify Decision Criteria and Potential Decision Paths/Options Quantitative Translation Model Building/Checking Construct Simulation Model Simulate Outcomes of Each Path/Option Summarize Simulation Results Check Sensitivity to Assumptions/ Uncertainties Choose Highest Value Decision Given the Current State of Knowledge

Simulation Based Decision-Making Process Flow

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

Define Metric(s) for Comparison and Decision Criteria

Target exposure range defined by adult data Distribution of Adult AUCinf following a single 60 mg PSE

  • dose. Dotted lines represent the

90% population prediction interval. 1194 3599 ng/mL

Gastonguay et al. Evaluation of the Performance of Pediatric OTC Monograph Dosing Guidance for Pseudoephedrine via Population Pharmacokinetic Modeling and Simulation. CP&T. Suppl. 2011

An Example Under Full Extrapolation Assumptions

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

  • Visual inspection
  • Quantify % individuals within target range
  • Across age/weight ranges
  • 2

4 6 8 10 12 20 40 60 80 100

Dosing Rule B

Age (years) AUC

  • ●
  • ●
  • 2

4 6 8 10 12 20 40 60 80 100

Dosing Rule A

Age (years) AUC

*AUC in arbitrary units

Simulation to Assess Performance Across Age/Weight Range

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Decision: Select dosing rule that achieves decision criteria, given practical constraints.

Percent of Pediatric Subjects with AUCinf Below and Above Target Exposure Bounds Following Monograph Dosing by Age. 95% CI based on 1000 simulated trials with 1821 subjects/trial (amplified from CDC age-weight database).

Below Target Above Target

Gastonguay et al. Evaluation of the Performance of Pediatric OTC Monograph Dosing Guidance for Pseudoephedrine via Population Pharmacokinetic Modeling and Simulation. CP&T. Suppl. 2011

More Quantitative Decision Criteria

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  • How do we arrive at a decision of similarity or

non-similarity of disease progression, intervention response, exposure-response? “Whoever best describes the problem is the one most likely to solve it” – Dan Roam

What Are the Metrics and Criteria for Assumption Checking?

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Less than or Equal to 5 mmHg Less than or Equal to 5 mmHg

Less Than 12% Incidence Rate

e f f e c t s i z e

  • f

+ 3 p

  • i

n t s

no more than 10 msec

Quantitative Specification of Decision Criteria

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

Understand Key Questions and Constraints Define Prior Knowledge/Data Sources

Identify Decision Criteria and Potential Decision Paths/Options Quantitative Translation Model Building/Checking Construct Simulation Model Simulate Outcomes of Each Path/Option Summarize Simulation Results Check Sensitivity to Assumptions/ Uncertainties Choose Highest Value Decision Given the Current State of Knowledge

Simulation Based Decision-Making Process Flow

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Simulation-Based Assumption Checking

  • Scenario 1: Sufficient data are available to quantitatively

check assumptions using simulation

  • Scenario 2: Assumptions rely on extrapolation to new

conditions where data are insufficient for quantitative checking

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Simulation-Based Assumption Checking

  • Scenario 1: Sufficient data are available to quantitatively

check assumptions using simulation

  • Scenario 2: Assumptions rely on extrapolation to new

conditions where data are insufficient for quantitative checking

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Distribution of simulated dropout times within each individual are compared to the actual

  • bserved dropout times

from the model building

  • dataset. Simulations

were performed using the final time to event dropout model. Kaplan- Meir survival curves (thick black line) for each study demonstrate the observed distribution

  • f dropout times.

5 10 20 30 0.0 0.4 0.8

Study A

5 10 20 30 0.0 0.4 0.8

Study B

10 20 30 40 50 60 0.0 0.4 0.8

Study C

10 20 30 40 50 0.0 0.4 0.8

Study D

10 20 30 40 0.0 0.4 0.8

Study E

10 20 30 40 50 60 0.0 0.4 0.8

Study F

10 20 30 40 50 60 0.0 0.4 0.8

Study G

10 20 30 40 50 60 0.0 0.4 0.8

Study H

5 10 15 20 25 30 0.0 0.4 0.8

Study I

Model and Assumption Checking: Dropout

Modeling and simulation of the exposure-response and dropout pattern of guanfacine extended-release in pediatric patients with

  • ADHD. Knebel W, Rogers J, Polhamus D, Ermer J, Gastonguay MR. J Pharmacokinet Pharmacodyn. 2015 Feb;42(1):45-65.
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Model and Assumption Checking – Endpoint

Distributions of simulated ADHD RS-IV score at endpoint within each individual are compared to the actual observed distribution of baseline values for adolescents from the model building datasets. Simulations were performed using the final placebo model and exposure-response models with correction for dropouts.

Placebo Exposure-response

10 20 30 40 50 10 20 30 40 50

adolescents

Observed at Endpoint Simulated at Endpoint

10 20 30 40 50 10 20 30 40 50

adolescents

Observed at Endpoint Simulated at Endpoint

Modeling and simulation of the exposure-response and dropout pattern of guanfacine extended-release in pediatric patients with

  • ADHD. Knebel W, Rogers J, Polhamus D, Ermer J, Gastonguay MR. J Pharmacokinet Pharmacodyn. 2015 Feb;42(1):45-65.
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Distributions of variance in change from baseline to endpoint in ADHD RS-IV score in simulated individuals are compared to the actual observed variance in change from baseline to endpoint for adolescents from the model building datasets. Simulations were performed using the final placebo model and exposure-response models with correction for dropouts.

Placebo Exposure-response

adolescents

Simulated var(Change from Baseline) Frequency 110 120 130 140 150 160 170 180 10 20 30 40 50 60 70

adolescents

Simulated var(Change from Baseline) Frequency 100 120 140 160 180 10 20 30 40 50

Model Checking – Variance in Change from Baseline

Modeling and simulation of the exposure-response and dropout pattern of guanfacine extended-release in pediatric patients with

  • ADHD. Knebel W, Rogers J, Polhamus D, Ermer J, Gastonguay MR. J Pharmacokinet Pharmacodyn. 2015 Feb;42(1):45-65.
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Simulation-Based Checking of Similar Disease Progression Assessing similarity of disease progression… Is simulation in panel a quantitatively different from observed data?

Friberg LE, de Greef R, Kerbusch T, Karlsson MO. Modeling and simulation of the time course of asenapine exposure response and dropout patterns in acute schizophrenia. Clin Pharmacol Ther. 2009 Jul;86(1):84-91.

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Simulation-Based Checking of Similar Disease Progression

Friberg LE, de Greef R, Kerbusch T, Karlsson MO. Modeling and simulation of the time course of asenapine exposure response and dropout patterns in acute schizophrenia. Clin Pharmacol Ther. 2009 Jul;86(1):84-91.

Assessing similarity of disease progression… Is simulation in panel a quantitatively different from observed data? Would results in panel b provide evidence of similarity? Why or Why not?

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Simulation-Based Assumption Checking

  • Scenario 1: Sufficient data are available to quantitatively

check assumptions using simulation

  • Scenario 2: Assumptions rely on extrapolation to new

conditions where data are insufficient for quantitative checking

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  • No test data set available. What can be

done?

  • Assess sensitivity of decision/conclusion

to uncertainties about extrapolation assumptions.

Hypothetical Q: Will 80% of patients land within target trough effect range at this dose?

  • Conclusions depend on the value of EMAX.
  • Uncertainty in extrapolation assumptions

about EC50 is less important than assumptions about in EMAX

Similarity of Exposure Response – No Reference Data

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  • Are conclusions independent of uncertainties in

extrapolation assumptions?

What Is the Probability of a Successful Pediatric Efficacy Trial?

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Other Suggestions for Checking Extrapolation Assumptions

  • Qualitative understanding of biology/pharmacology

Ø Similarity of disease (subtypes based on aetiology,

pathophysiology, clinical manifestation, progression (indicators)).

Ø Similarity of medicine disposition & effect (mode of action, PK,

PD).

Ø Similarity and applicability of clinical efficacy and safety endpoints.

  • Quantitative evidence

Ø Disease progression: Simulation with disease models to

characterize differneces between groups.

Ø PK and PD: using existing data, modeling and simulation to

investigate the relationship between PK/PD, age and other important covariates.

Ø Clinical response: quantitative synthesis of all existing data to

predict the degree of similarity in clinical response

Adapted from: Concept paper on extrapolation of efficacy and safety in medicine development. EMA. 19 March 2013. Final.

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Dunne J et al. Pediatrics 2011;128;e1242

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Dunne J et al. Pediatrics 2011;128;e1242

Even within extrapolation categories, sources of evidence and requirements for new pediatric studies vary. What are the metrics/criteria driving this variability?

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Dunne J et al. Pediatrics 2011;128;e1242

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Dunne J et al. Pediatrics 2011;128;e1242

Extrapolation approaches differ across disease areas, and have evolved within disease areas. What metrics or decision criteria drive these differences?

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Gaps in the Strategy for Assessing Extrapolation Assumptions

  • Disease Progression

Ø Which endpoints are relevant and comparable? Ø Sufficient duration of disease progression in adult and pediatric

populations

  • Exposure-Response

Ø Sufficient characterization of randomized dose or exposure-

response in adult development program

Ø Challenges of dose-ranging studies with relevant PD or efficacy

endpoints in pediatric populations

  • Decision Criteria

Ø How similar is similar enough? Ø How different can disease progression or exposure-response be

before it is a clinically meaningful difference?

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Summary

  • Simulation-based quantitative assessment of extrapolation

assumptions:

Ø May be useful to identify cases or conditions when adult and

pediatric populations are not similar

Ø Is likely insufficient to confirm similarity between adult and pediatric

populations, without other sources of evidence

Ø Should be supplemented with qualitative evidence based on

biological understanding

  • NEEDED: Disease-area specific guidance on metrics and

decision criteria (or points to consider) when assessing similarity of disease progression, intervention response, and exposure-response

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FDA-UMD Workshop: Efficacy Extrapolation in Pediatrics

Thank You FDA-UMD CERSI Workshop Panel and Organizers Metrum Research Group Scientists Industry Collaborators Pediatric trial participants