Leveraging Data Sharing Klaus Romero MD MS FCP Director of Clinical Pharmacology Critical Path Institute

2 C-Path & FDA MOU October 14, 2005 “purpose… to establish an overarching framework for collaboration… to foster development of new evaluation tools to inform medical product development”

C-Path Consortium Model 3 FDA EMA Patients NIH Academia A A B C D E Precompetitive Neutral ground Multiple Companies Formal Legal Agreement Critical Path Institute (C-Path) has developed a consortium structure that provides a unique neutral, precompetitive environment to increase collaborative efforts for drug development

C-Path Collaborators

Creating Consensus Six global consortia collaborating with 1,000+ scientists and 41 companies

Informal discussion with FDA/EMA. Sponsor submits a letter of intent requesting formal qualification. FDA/EMA Review Team formed. Sponsor submits briefing document. F2F meeting between sponsor and FDA/EMA Review Team. Review Team may request additional information. Sponsor submits full data package. Review process within FDA/EMA begins. Consultation and Advise Process 6 Regulatory decision qualifying or endorsing the submitted tools Success!!! Regulatory Review Process: What’s success?

What Was Learned? ADAS-Cog Variability Sponsor 1Sponsor 2Sponsor 3Sponsor 4Sponsor 5Sponsor 6Sponsor 7 Item 1 Word Recall Item 2 CommandsName Obj/fing. CommandsName Obj/fing. Item 3 Constr. PraxisDelayed recallCommandsConstr. PraxisCommands Item 4 Delayed recallCommandsConstr. PraxisDelayed recall Constr. Praxis Item 5 Naming Obj/fing.Constr. PraxisIdea PraxisName Obj/fing.Constr. PraxisIdea. Praxis Item 6 Idea. PraxisIdea PraxisOrientationIdea. Praxis Orientation Item 7 Orientation Word RecogOrientation Word Recog Item 8 Word Recog. Remem. Instr.Word Recog Remem. Instr. Spoken Lang Abil. Item 9 Remem Instr. Spoken Lang. Abil.Remem. Instr. Spoken Lang. Abil.Comprehension Item 10 Comprehension Spoken Lang. Abil. Word Finding Dif. Spoken Lang Abil. Word Finding Dif. Item 11 Word Finding Dif. Comprehension Diff. Spont. Speech Word Finding Dif.ComprehensionRemem. Instr. Item 12 Spoken Lang. Abil.ComprehensionConcentrationComprehension Concentration Item 13 Number cancel.Concentration

Benefits of Data Standards SDTM clinical data standard used / preferred within FDA – standards required in PDUFA V Enable data sharing between organizations Enable aggregation and querying of data When implemented from the start, can lower costs of acquiring and analyzing data CDISC Standards 8

CDISC and C-Path C-Path Mission: To improve human health and well-being by developing new technologies and methods to accelerate the development and review of medical products CDISC Mission: To develop and support global, platform ‐ independent data standards that enable information system interoperability to improve medical research and related areas of healthcare 9

C-Path and CDISC Collaborations C-Path – FDA Qualification Collaborations CAMD – Alzheimer’s CAMD – Parkinson’s PKD – Polycystic Kidney Disease PSTC – Safety Testing CPTR – Tuberculosis CDISC – Data Standards

C-Path Data Repository

C-Path and CDISC announce formal release of data standard for Alzheimer’s Disease

~6000 Patients Seven companies remapped and pooled data from 21 trials for ~6000 patients: value = $400 Million Database open to >200 qualified research teams in 35 countries C-Path’s Data Repository for Alzheimer’s Disease

CODR – integrated CDISC data model C-Path Online Data Repository (CODR) CODR is a relational database with a data model designed around the CDISC SDTM clinical data standard CDISC domains and variables are integrated into the database architecture Common framework for easy generation of new data repositories based on applicable CDISC domains 14

CAMD Process Overview WG1 Data WG 2 Modeling and Simulation WG 3 Biomarkers & Imaging Models Biomarkers Regulatory Review, Qualification, Acceptance WG 4 Health Authorities Submission Consensus 15

16 Quantitative Disease-Drug-Trial Models Disease Model Drug Model Trial Model  Biology  Natural Progression  Placebo  Biomarker-Outcome  Pharmacology  Effectiveness  Safety  Early-Late  Preclinical-Healthy-Patient  Patient Population  Drop-out  Compliance FDA Data Diverse Expertise Physiology Disease-drug-trial models are mathematical representations of the time course of biomarker-clinical outcomes, placebo effects, drug’s pharmacologic effects and trial execution characteristics for both the desired and undesired responses, and across experiments. Janet Woodcock

Clinical Trial Simulations Based on the Model help inform the Development process for New Drugs 17 If symptomatic only:If symptomatic + Disease modifying

78-week Parallel Study Design versus 91 Week Delayed Start Design by Varying Disease Modifying Effects 18

When Given a Regulatory Decision, Sponsors will be able to more Confidently use the Tool and the Review Process for New Drugs will be Streamlined 19 CAMD Regulatory Path for AD Disease-Drug-Trial Model MAY 2012 Team responding to Agency questions

C-Path Project Pipeline FDA EMA PMDA CAMD Disease or TargetDrug Development Tool Feasibility 1 Scoping 2 Research 3 Submitted 4 Qualified 5 Alzheimer's disease (AD) Imaging Biomarkers CSF Biomarkers Disease model of mild and moderate AD Disease model of Mild Cognitive Impairment Parkinson's disease (PD) PD imaging biomarkers

C-Path and CDISC announce formal release of data standard for Alzheimer’s Disease

C-Path Project Pipeline FDA EMA PMDA CPTR Disease or TargetDrug Development Tool Feasibility 1 Scoping 2 Research 3 Submitted 4 Qualified 5 Tuberculosis Liquid cultures TB quantitative disease progression model Hollow Fiber System

Clinical Trial Inventory 23

TB M&S inventory 24

Approaches from other areas Predator-Prey models in HCV may provide useful insights for TB modeling and simulation. Guedj J. et al. Understanding HCV dynamics with direct-acting antiviral agents due to interplay between intracellular replication and cellular infection dynamics. J Theor Bio 2010;267:330-40

What about HCV? C-Path firsts Are industry and regulators interested? Which DDTs are a priority? What is the current status of data standardization? Which are the relevant potential data sources? How can we collaborate?