Advancing Regulatory Science for Medical Countermeasures Development: An Institute of Medicine Workshop

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1 Institute of Medicine Workshop Venable Conference Center 29 Mar 2011 Advancing Regulatory Science for Medical Countermeasures Development: An Institute of Medicine Workshop Stephen J. Ruberg, PhD Distinguished Research Fellow Scientific Leader, Advanced Analytics Eli Lilly & Company

2 Disclaimer I have not been involved directly with the development of medical countermeasures for high-threat pathogens or other national public health emergencies. The methodologies for clinical study design, analysis and interpretation used for more traditional drug development are likely to have applicability to this arena.

3 Study Conclusion On Drug Effect Negative Positive Clinical Trial Simulation Goal: Get to the right answer faster, cheaper and with greater certainty Kill the ineffective/unsafe drugs sooner Get better information on the useful drugs Drug Effect (Unknown Truth) Positive Negative True Positive False Positive False Negative True Negative

4 Clinical Trial Simulation Examine a broad range of clinical trial designs, decision rules and analysis plans Systematically evaluate parameters that are known and can be controlled (design/analysis parameters) # of patients, doses, visits, interim analyses, randomization schemes, analysis methods, etc. Assess a range of possibilities for unknown parameters or factors that cannot be controlled (outcome assumptions) True response rate, variability, drop-out rate, adverse event rate, etc. Select the design/analysis parameters that provide the best trade-off for minimizing false positive and false negative findings across the unknown parameter space

5 Clinical Trial Simulation Regulatory Science impact CT Simulation requires very sophisticated software and substantial training Software tools are emerging Not all adaptive designs have closed form solutions for controlling false positive findings Simulations are needed When is enough enough? FDA Draft Guidance on Adaptive Designs Simulating a sequence of clinical trials is next

6 Clinical Trial Design Goal: Use as much data as possible current or historical to make decisions on drug safety and efficacy There are clinical data and results in most disease areas from drug development programs or government sponsored research Current practice is to have each clinical trial in a drug development program stand on its own

7 Clinical Trial Design Bayesian Augmented Control Designs Use control group data from other/historical trials to supplement data from a current, prospective trial Smaller trials (faster and cheaper) More patient allocated to treatment/dose groups (more information)

8 Clinical Trial Design Regulatory Science impact Require careful modeling and simulation to understand operating characteristics Choice of historical data to supplement prospective control Selection of priors for Bayesian methods Enable robust, efficient Phase II testing of new molecules/regimens Use in Phase III confirmatory trials needs study/discussion with FDA

9 Data Element Standards ampoule French German glühlampe light bulb bombilla English Spanish I do not want to translate anymore! At least minimize as mush as possible.

10 Data Element Standards Regulatory Science Issues There is a need for a comprehensive data element dictionary It must be maintained by a central authoritative group Eventually span clinical research and clinical care The needs overlap, but have some notable differences

11 Consortia Adaptive/Pooled Studies I-SPY-like studies for other very expensive drug development areas Alzheimer s, CV safety in diabetes Bayesian methods for borrowing information DIA Working Group on Bayesian methods in Clinical Drug Development National Academy of Sciences investigation Subgroup on safety signal detection/evaluation CDISC, HL7, etc. for data standards