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A Practical Approach to Accelerating A Practical Approach to Accelerating the Clinical Development Process the Clinical Development Process
Jerald S. Schindler, Dr.P.H.Assistant Vice PresidentGlobal Biostatistics & Clinical Technology Wyeth Research
FDA-Industry WorkshopSeptember 23, 2004
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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
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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
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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
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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
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Two General Approaches to Adaptive Trials
Add as you goMore Bayesian
Re-estimate success probabilities while the trial progresses
Subtract as you goBased on futility boundaries
Start with many doses and eliminate low performing doses
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Potential Dose Options to be Studied
“Phase 2” “Phase 3”
High Dose
Low Dose
Control
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Add as you go – Step 1
“Phase 2”Small n
“Phase 3”Large n
High Dose
Low Dose
Control
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Add as you go – Step 2
“Phase 2”Small n
“Phase 3”Large n
High Dose
Low Dose
Control Control
Low Dose
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Subtract as you go – Step 1
“Phase 2” “Phase 3”
High Dose
Low Dose
Control
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Subtract as you go – Step 2
“Phase 2” “Phase 3”
High Dose
Low Dose
Control Control
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Practical Consideration: Drug Supply / Product Development
Many trials require pre-specified doses to be availableTablet 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 availableFavors “subtract as you go” designs rather than “add as you go”
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Clinical Development Timeline
FinalProtocolTo firstpatient
First Patient Visit to
First CRF in-house
Patient enrollment/treatment
All CRFsIn house
Locked Database
InitialResults
Time | 6 weeks | 6-18 months | 6 wks | 4 weeks | 1 day |
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The clinical trial process (Usually 5 – 10 years)
------Phase 1----------------------Phase 2-----------------------------Phase 3---------------------
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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
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The new clinical trial process (3-7 years)
---Early development----------Registration Development--------
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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
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Wyeth eClinical System
EDCData
LabData
SafetyData
Random-ization
Drug Supply
Web access
Data Warehouse
IRS eReview Decision Rules
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Vision for Wyeth Integrated Clinical Information System
1. Raw Data2. Derived
Data3. Discrepancies/
Resolutions 4.Images 5.Documents7. Administrative
Data 8. Budgets10. Non-Clinical
Data9. Post Marketing
Safety Data
Central Linkage and Synchronization System
1. In-house data entry
2. Remotedata entry
3. DataValidation
4. Coding-AEs/Meds
5. SAEreconciliation
8. RandomizationSetup
10. Drug shippingand inventory
tracking
11. Patient Enrollment
12. Monitoring& Trip reporting
13. InvestigatorEnrollment
6. Data Review 7. SAS Reports
14. Electronic Review and
Approval (sign-off)
15. ElectronicWorkspace
Collaboration
16.Quality controlreview
17. ExecutiveInformation
Summary reports
6. Tracking/Study progress
9.DynamicTreatment Allocation
Integrated Databases
18. Electronic Publishing
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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
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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
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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
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Wyeth Interactive Randomization System
Randomization features1. Run fresh for each new patient 2. Add or drop treatment arms 3. Dynamic randomization to balance
for covariables at baseline4. 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 droparms
Dynamicrandomization
Just in timedrug supply
Precise controlof sample size
Adjust probabilities
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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
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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
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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
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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?
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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
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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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