A Practical Approach to Accelerating the Clinical Development Process

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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
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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 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

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Potential Dose Options to be Studied
High Dose
Low Dose
Control
Phase 3
Phase 2
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Add as you go Step 1
High Dose
Low Dose
Control
Phase 3 Large n
Phase 2 Small n
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Add as you go Step 2
High Dose
Low Dose
Low Dose
Control
Control
Phase 3 Large n
Phase 2 Small n
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Subtract as you go Step 1
High Dose
Low Dose
Control
Phase 3
Phase 2
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Subtract as you go Step 2
High Dose
Low Dose
Control
Control
Phase 3
Phase 2
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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

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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


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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
EDC Data
Lab Data
Random- ization
Safety Data
Drug Supply
Data Warehouse
Web access
IRS
eReview
Decision Rules
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Vision for Wyeth Integrated Clinical Information
System
Integrated Databases
1. Raw Data
2. Derived Data
3. Discrepancies/ Resolutions
4.Images
5.Documents
7. Administrative Data
8. Budgets
10. Non-Clinical Data
9. Post Marketing Safety Data
6. Tracking/ Study progress
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

• 10. Drug shipping
• and inventory
• tracking

• 11. Patient
• Enrollment

• 12. Monitoring
• Trip reporting

• 13. Investigator
• Enrollment

• 9.Dynamic
• Treatment
• Allocation

• 14. Electronic
• Review and
• Approval (sign-off)

• 15. Electronic
• Workspace
• Collaboration

• 16.Quality control
• review

• 17. Executive
• Information
• Summary reports

• 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
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
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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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