The use of modelling and simulation in drug approval: A regulatory view

1. The use of modelling and simulation in drug approval: A regulatory view Norbert Benda Federal Institute for Drugs and Medical Devices Bonn Disclaimer: Views expressed in this presentation are the author's personal views and not necessarily the views of BfArM The BfArM is a Federal Institute within the portfolio of the Federal Ministry of Health (BMG)

2. Overview • Principles in drug approval • Challenges • Modelling ? • Simulation ? • Problems • Longitudinal analysis • Small population dilemma • Conclusions

3. General principles in drug approval • Demonstrate efficacy • Show favourable benefit risk • Additional requirements • Additional claims to be demonstrated after general efficacy (1) has been shown • Homogeneity • Subgroups to be excluded / justified • Relevant dose / regimen

4. Statistical principles in drug approval • Independent confirmatory conclusion • no use of other information • type-1 error control limiting false positive approvals • Internal validity • blinded randomized comparison • assumption based • External validity • relevant population to study • random sampling, etc

5. Areas that may challenge approval principles • Paediatrics • Orphan drugs • Integrated benefit risk assessments • Dose adjustments (body weight, renal impairement, etc.) • Individualized dosages / therapies

6. Example: Limitations in paediatric drug approvals • Sample size • small • Treatment control • placebo unethical / impossible • Endpoints • different from adults / between age groups • Dosages • age / weight dependent

7. General use of M&S • Prediction • dose response • dose adjustment • impact of important covariates • identification of subgroups of concern • Optimization of development program • identification of optimal / valid methods • informed decision making • accelerating drug development

8. Impact of M&S on the regulatory review • Low impact • internal decision making (hypothesis generation, learning) • more efficient determination of dose regimen for phase III • optimise clinical trial design • Medium impact • identify safe and efficacious exposure range • dose levels not tested in Phase II to be included in Phase III • inferences to inform SPC (e.g. posology with altered exposure) • High impact • extrapolation of efficacy / safety from limited data (e.g. paediatrics) • Model-based inference as evidence in lieu of pivotal clinical data

9. Model based inference Models = assumptions • Models with increasing complexity • random sampling from relevant population • variance homogeneity • proportional hazard • generalisability of treatment differences (scale) • longitudinal model for the treatment effect • PK models / population PK models • PK / PD models • models on PK – PD – clinical endpoints

10. Modelling Modelling = Model building + model based inference • Model building aspects • biological plausibility • extrapolation from • animal models • healthy volunteers • adults • interpretational ease • robustness • evidence based • derived from / supported by data

11. Problems with modelling • Model selection bias • if model selection and inference based on same data • Ignored pathway • Dose  PK  PD  clinical endpoint ? • Ignored between-study variability • validation usually within similar settings • no “long-term validation”

12. Simulations • Simulation = numerical tool • Complex models / methods require unfeasible high dimensional numerical integration • e.g. type-1 error / power calculation under complex assumptions (drop-outs, adaptive designs, etc) or model deviations • Simulation = visualization • Focus on statistical distributions • between subjects / within subjects • considering complex variance structures / non-linear mixed models • Visualize resulting distribution for specific settings (treatments, fixed covariates)

13. Simulations • Advantages: • visualization on distributions / populations • allow for an population based assessment • Disadvantages • often (unconsciously ?) misinterpreted as “new” data • inference from simulation impossible • depend on (unverifiable) model assumptions • incorrect variance modelling may be misleading

14. Longitudinal model-based inference • Repeated Scientific Advice question: • Pivotal confirmatory Phase III study • Longitudinal measurements at time t1, t2, ..., tn • relevant endpoint at tn (end of treatment) • primary analysis based on tn only or on a longitudinal model ? different possibilities • time dependency functional or categorical ? • covariance structured or unstructured ? • Robustness (tn) vs more informative analysis • “borrowing strength” or “relying on assumptions difficult to verify” ?

15. Longitudinal model-based inference • Case-by-case decision • Relevant missing data issue and non-inferiority: • consider assay sensitivity • longitudinal analysis / MMRM (Mixed-Effect Model Repeated Measure) preferred • justify model (by M&S ?) • Non-compliance and superiority vs placebo: • use of measurements under non-compliance / after discontinuation (retrieved data): “effectiveness” • longitudinal analysis under compliance: “efficacy”

16. Small population dilemma • Independent confirmation vs historical information • Population concerned vs extrapolation from other population • Modelling approaches to • bridge historical information • extrapolate from other population • Trade-off • Robustness and independent confirmation vs presumably more informative analysis • Less data available – more assumptions needed

17. Small population proposals • M&S approaches to extrapolate • Surrogate endpoints (PD) + adult evidence • Meta-analytic approaches using historical data • Bayesian: Evidence synthesis • (Paediatric) subgroup analyses • rely on transferability of (some) model components • Increase type-1 error Relying on more assumptions False positives  - false negatives  • missed drug worse than ineffective drug ?

18. Conclusions (1) • Differentiate • M&S to optimise study design • M&S to explore and optimise development program • M&S to predict efficacy and safety • Differentiate • M&S / Model building and exploration • Model-based inference

19. Conclusions (2) • Be honest with simulations • Numerical tool • Visualizing tool • Be honest with modelling • confirmatory inference independent of model building • inference is always model-based • amount and quality of assumptions to be assessed • simplicity preferred if robustness is of concern • trade-off between • precision vs robustness • false positives vs false negatives

20. Conclusions (3) • Virtues of M&S • increased understanding of underlying process • may facilitate focus on distributions • may optimise development program design • Independent confirmation • still required in Phase III in most applications • low amount of assumptions / simplicity to ensure robustness • possible exceptions where false positive decisions are worse than false negatives