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

1 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

2 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

3 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

4 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

5 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

6 Potential Dose Options to be Studied Phase 2 Phase 3 High Dose Low Dose Control

7 Add as you go – Step 1 Phase 2 Small n Phase 3 Large n High Dose Low Dose Control

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

9 Subtract as you go – Step 1 Phase 2 Phase 3 High Dose Low Dose Control

10 Subtract as you go – Step 2 Phase 2 Phase 3 High Dose Low Dose Control

11 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

12 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 |

13 The clinical trial process (Usually 5 – 10 years) Phase Phase Phase

14 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

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

16 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

17 Wyeth eClinical System EDC Data Lab Data Safety Data Random- ization Drug Supply Web access Data Warehouse IRS eReviewDecision Rules

18 Vision for Wyeth Integrated Clinical Information System 1. Raw Data 2. Derived Data 3. Discrepancies/ Resolutions 4.Images5.Documents 7. Administrative Data 8. Budgets 10. Non-Clinical Data 9. Post Marketing Safety 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 8. Randomization Setup 10. Drug shipping and inventory tracking 11. Patient Enrollment 12. Monitoring & Trip reporting 13. Investigator Enrollment 6. Data Review7. SAS Reports 14. Electronic Review and Approval (sign-off) 15. Electronic Workspace Collaboration 16.Quality control review 17. Executive Information Summary reports 6. Tracking/ Study progress 9.Dynamic Treatment Allocation Integrated Databases 18. Electronic Publishing

19 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

20 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

21 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

22 Wyeth Interactive Randomization System Randomization features 1.Run fresh for each new patient 2.Add or drop treatment arms 3.Dynamic randomization to balance for covariables at baseline 4.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 Live for each patient Add or drop arms Dynamic randomization Just in time drug supply Precise control of sample size Adjust probabilities

23 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

24 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

25 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

26 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?

27 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

28 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

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