1. Federal Institute for Drugs and Medical Devices The BfArM is a Federal Institute within the portfolio of the Federal Ministry of Health (BMG) 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

2. 2/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices Overview  Principles in drug approval  Challenges  Modelling ?  Simulation ?  Problems  Longitudinal analysis  Small population dilemma  Conclusions

3. 3/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 4/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 5/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices Areas that may challenge approval principles  Paediatrics  Orphan drugs  Integrated benefit risk assessments  Dose adjustments (body weight, renal impairement, etc.)  Individualized dosages / therapies

6. 6/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 7/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 8/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 9/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 10/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 11/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 12/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 13/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 14/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices Longitudinal model-based inference  Repeated Scientific Advice question:  Pivotal confirmatory Phase III study  Longitudinal measurements at time t 1, t 2,..., t n  relevant endpoint at t n (end of treatment)  primary analysis based on t n only or on a longitudinal model ? different possibilities time dependency functional or categorical ? covariance structured or unstructured ?  Robustness (t n ) vs more informative analysis  “borrowing strength” or “relying on assumptions difficult to verify” ?

15. 15/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 16/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 17/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 18/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 19/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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. 20/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices 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