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Federal Institute for Drugs and Medical Devices 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) Overview Principles in drug approval Challenges Modelling ? Simulation ? Problems Longitudinal analysis Small population dilemma Conclusions 2/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices General principles in drug approval Federal Institute for Drugs and Medical Devices 1. Demonstrate efficacy 2. Show favourable benefit risk 3. Additional requirements Additional claims to be demonstrated after general efficacy (1) has been shown Homogeneity Subgroups to be excluded / justified Relevant dose / regimen 3/20 N Benda: M&S in Drug Approval Statistical principles in drug approval Federal Institute for Drugs and Medical Devices 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 4/20 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 5/20 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 6/20 N Benda: M&S in Drug Approval 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 7/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices Impact of M&S on the regulatory review Federal Institute for Drugs and Medical Devices 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 8/20 N Benda: M&S in Drug Approval 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 9/20 N Benda: M&S in Drug Approval Federal Institute for Drugs and Medical Devices Modelling Federal Institute for Drugs and Medical Devices 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 10/20 N Benda: M&S in Drug Approval Problems with modelling Federal Institute for Drugs and Medical Devices 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” 11/20 N Benda: M&S in Drug Approval Simulations Federal Institute for Drugs and Medical Devices Simulation = numerical tool Complex models / methods require unfeasible high dimensional numerical integration • e.g. type-1 error / power calculation under complex assumptions (dropouts, 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) 12/20 N Benda: M&S in Drug Approval Simulations Federal Institute for Drugs and Medical Devices 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 13/20 N Benda: M&S in Drug Approval Longitudinal model-based inference Federal Institute for Drugs and Medical Devices 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” ? 14/20 N Benda: M&S in Drug Approval Longitudinal model-based inference Federal Institute for Drugs and Medical Devices 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” 15/20 N Benda: M&S in Drug Approval Small population dilemma Federal Institute for Drugs and Medical Devices 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 16/20 N Benda: M&S in Drug Approval Small population proposals Federal Institute for Drugs and Medical Devices 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 ? 17/20 N Benda: M&S in Drug Approval Conclusions (1) Federal Institute for Drugs and Medical Devices 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 18/20 N Benda: M&S in Drug Approval Conclusions (2) Federal Institute for Drugs and Medical Devices 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 19/20 N Benda: M&S in Drug Approval Conclusions (3) Federal Institute for Drugs and Medical Devices 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 20/20 N Benda: M&S in Drug Approval