A Practical Approach to Accelerating the Clinical Development Process

1. A Practical Approach to Accelerating the Clinical Development Process Jerald S. Schindler, Dr.P.H. Assistant Vice President Global Biostatistics & Clinical Technology Wyeth Research FDA-Industry Workshop September 23, 2004

2. Business Case for Adaptive Trials • More efficient, faster trials • Process efficiency for Clinical Trials • Midcourse correction for trials that are off target • Fewer patients enrolled into ineffective treatment arms • Shorter trials – smaller overall sample size required • Increased quality of results – more patients enrolled into successful treatments • Reduce timeline by combining phases • Reduce white space between phases • Reduce overall time of Clinical Development • Reduce costs by stopping unsuccessful trials early

3. Adaptive Trials at Wyeth • How can a large pharmaceutical company add adaptive trials to the clinical development process? • What major infrastructure changes are required? • Capabilities for any new processes required are: • (In addition to regulatory acceptance of adaptive trials) • Must be applicable to large numbers of trials • Hundreds of clinical trials in progress each year • Can be used for both small molecules and protein therapies • This presentation will outline some of activities underway at Wyeth to incorporate adaptive trials into our clinical development programs

4. Adaptive Trial Concept • General Concept: • Maximize patient exposure to doses that will eventually be marketed. • Reduce patient exposure to doses that will not be marketed (i.e. ineffective doses) • Where possible combine development phases

5. Are all Adaptive Designs – Bayesian Trials? • Much discussion about the acceptability of Bayesian trials • No real conclusion to the discussion yet • There are still many available options from the frequentist world which provide the same benefits of Bayesian adaptive trials • Similar advantages with less controversy and risk • Based on optimizing the use of many of the currently accepted options • Key is an integrated IT/Statistical approach to trial design and analysis • Many of these IT tools are needed for either frequentist or Bayesian adaptive trials • At Wyeth, we are building the tools to enable both sets of options for adaptive trials

6. Two General Approaches to Adaptive Trials • Add as you go • More Bayesian • Re-estimate success probabilities while the trial progresses • Subtract as you go • Based on futility boundaries • Start with many doses and eliminate low performing doses

7. Potential Dose Options to be Studied High Dose Low Dose Control “Phase 3” “Phase 2”

8. Add as you go – Step 1 High Dose Low Dose Control “Phase 3” Large n “Phase 2” Small n

9. Add as you go – Step 2 High Dose Low Dose Low Dose Control Control “Phase 3” Large n “Phase 2” Small n

10. Subtract as you go – Step 1 High Dose Low Dose Control “Phase 3” “Phase 2”

11. Subtract as you go – Step 2 High Dose Low Dose Control Control “Phase 3” “Phase 2”

12. Practical Consideration: Drug Supply / Product Development • Many trials require pre-specified doses to be available • Tablet form rather than mix when given • Need to manufacture and package all dose options before trial begins • Limits the total number different dose options available • Since they are all available • Favors “subtract as you go” designs rather than “add as you go”

13. Clinical Development Timeline Final Protocol To first patient First Patient Visit to First CRF in-house Patient enrollment/ treatment All CRFs In house Locked Database Initial Results Time | 6 weeks | 6-18 months | 6 wks | 4 weeks | 1 day |

14. The clinical trial process (Usually 5 – 10 years) ------Phase 1----------------------Phase 2-----------------------------Phase 3---------------------

15. Goals for Improving Efficiency of Clinical Development • Fewer total number of trials • Less ‘white space’ or ‘down time’ between trials or phases • Fewer patients enrolled into doses that will not be marketed • More patients enrolled into doses that will be marketed • Early indication of program success • View of all trials for a product as a group (rather than as a set of independent trials) • Focus on Integrated Efficacy and Integrated Safety as you go rather than at the end

16. The new clinical trial process (3-7 years) ---Early development----------Registration Development--------

17. Key Requirements – for Adaptive Trials (Help from Information Technology) • Real time databases • EDC • Rapid data validation • 100% clean data for completed patients • Tool for rapid data review • On-line (web based, eClinical) • Maintain blind (if appropriate) • Produce planned listings and analyses within hours • Tool to guide decision making • Automate decision rules before patients enroll • Tool to implement decisions • Rapidly stop a trial or drop treatment arms • Across potentially hundreds of sites and in dozens of countries • Production Environment • Able to handle hundreds of clinical trials

18. Wyeth eClinical System EDC Data Lab Data Random- ization Safety Data Drug Supply Data Warehouse Web access IRS eReview Decision Rules

19. Vision for Wyeth Integrated Clinical Information System IntegratedDatabases 1. Raw Data 2. Derived Data 3. Discrepancies/ Resolutions 4.Images 5.Documents 6. Tracking/ Study progress 7. Administrative Data 8. Budgets 9. Post Marketing Safety Data 10. Non-Clinical Data Central Linkage and Synchronization System • 1. In-house • data entry • 2. Remote • data entry • 3. Data • Validation • 4. Coding- • AEs/Meds • 5. SAE • reconciliation • 6. Data Review • 7. SAS Reports • 8. Randomization • Setup • 9.Dynamic • Treatment • Allocation • 10. Drug shipping • and inventory • tracking • 11. Patient • Enrollment • 12. Monitoring • & Trip reporting • 13. Investigator • Enrollment • 14. Electronic • Review and • Approval (sign-off) • 15. Electronic • Workspace • Collaboration • 16.Quality control • review • 17. Executive • Information • Summary reports • 18. Electronic • Publishing

20. Wyeth eReview System • Online review of live data • Monitor variance and trial ‘information’ to determine sample size • Option for blinded or unblinded • Overall or by treatment group • Monitor primary safety/efficacy variables • Option for blinded or unblinded • Overall or by treatment group • Early stopping for efficacy or futility • Formal data monitoring committee • Decisions at key predefined time points • Future options include automated review • Computerized review of data pre-programmed • Notification when observed data crosses pre-defined boundaries • Otherwise trial progresses as planned

21. Wyeth Interactive Randomization System • Crucial to rapid implementation of adaptive trials • Investigator connects to Wyeth eClinical via internet or phone • Web based IVRS • After patient eligibility is assessed • Treatment assignment is calculated based on current rules • No pre study “randomization lists” are used • System requires • Stratification variables (if any) • Number of treatments • Treatment Ratio or Treatment probability • Similar to “rolling the dice” or “spinning the pointer” every time a patient enrolls • Tested pre study to validate accuracy • Appropriate security built in to maintain the blind

22. Eliminate Over-enrolled Studies • Large multi-center trials often enroll more than the desired numer of patients • Sites keep enrolling after the pre-determined sample size has been reached • Due to slow (or no) communication between sponsor and sites • Live, centralized randomization eliminates over-enrollment completely • Cut-off enrollment as soon as target number is reached • Large multi-center trials can over-enroll by 10% • Adds to CDM and monitoring workload • Plus additional analyses required • Added time while we wait fro the last patients to complete study treatment

23. Wyeth Interactive Randomization System Live for each patient • Randomization features • Run fresh for each new patient • Add or drop treatment arms • Dynamic randomization to balance • for covariables at baseline • Integrated with drug supply for • “Just in time” shipping • 5. Stop enrollment when appropriate • sample size is reached • (no need for pre-set sample size, • no over-enrollment) • 6. Adjust randomization probabilities • over time Add or drop arms Just in time drug supply Dynamic randomization Precise control of sample size Adjust probabilities

24. Advantages to this eClinical Randomization System • Flexibility • All adaptive changes to the trial implemented via the randomization system • No need to stop the trial to implement new randomization • Example 1: • Five treatment trial – A, B, C, D, Control • Equal Probability: (.2, .2, .2, .2, .2) • At interim look drop ‘B’ • Change probability to (.25, 0, .25, .25, .25) • Example 2: • Large multi-continent trial • 2000 patients, 200 sites, worldwide • All sites access eClinical for treatment assignment • Four treatments – A, B, C, Control • Unequal Probability: (.4, .1, .1, .4) • One patient #2000 enrolls, no new patients enroll • Change probability to (0, 0, 0, 0) • Ends unplanned over enrollment of trials

25. Features to Consider for Adaptive Designs • Adjust Sample Size – • Monitor overall variance • Monitor overall dropout rate • Randomization – • Dynamic - Balance for many covariables at baseline • Adaptive - Adjust probability of treatment assignments during the trial • Pre-planned Interim Analysis • Stop trial or individual arm early due to: • unexpected efficacy • futility • Combine Drug Development Phases

26. Requirements for Adaptive Trials • eClinical System • Bring information from many different systems into one place • Easy access and reporting • Live, “real time” data • The more current the data are the more powerful the result will be • Ability to review and analyze the data often • Acquire software to support sophisticated analyses • Train and develop staff to acquire additional statistical skills • Ability to implement the desired changes quickly • Adjust randomization probabilities • Link between randomization system/ drug supplies tracking

27. Critical Path Opportunities • Development of standard IT tools • Plug and play modules • Standardized specifications • Rapid implementation • Rapid review/decision making • Statistical Methodology • Trial approaches • Add as you go or subtract as you go • Bayesian or Frequentist style • Rules for spending beta error • Simulation pre-study • Regulatory issues • One protocol – that can change over time • IRB review – one review or new reviews after each “change” • Informed consent form – How to outline all the potential options?

28. Critical Path Opportunities • Development of standard tools (or plug and play modules): • EDC using standard data structures (CDISC, HL7) • Integrated database guidelines from these standard structures • Live on-line data review tool (or standardized specifications) • Real time randomization tool • Not-list based • Randomization specs can change over the course of the trial • Drop treatments, dynamic randomization, precise sample size • Analysis tools • Options for on-line futility analysis • Rules for controlling beta spending function • Simulation tools • Pre-study simulations to help guide the design of new trials • Decision implementation tools • Once a decision is made – implement the results quickly

29. Critical Path Opportunities for Efficient Clinical Trials • Software tools required for Adaptive Trials • Are expensive to develop • Only large pharma companies can develop all of them • Vendor developed tools • Are usually based on proprietary designs • Provide limited functionality • Limited (or no) interoperability among vendor tools • Also high cost, especially if you are conducting hundreds of trials • Opportunity to develop common interoperable software • All parties can work together to collaborate on one approach to technology • At least develop common specifications for software • Goal is inter-operability • Potential opportunity to design trials to save time and money and also to build systems/processes efficiently and inexpensively