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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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A Practical Approach to Accelerating the Clinical Development Process

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A practical approach to accelerating the clinical development process l.jpg
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 Development Process

  • 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 Development Process

  • 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 Development Process

  • 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? Development Process

  • 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 Development Process

  • 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 Development Process

High Dose

Low Dose

Control

“Phase 3”

“Phase 2”


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Add as you go – Step 1 Development Process

High Dose

Low Dose

Control

“Phase 3”

Large n

“Phase 2”

Small n


Add as you go step 2 l.jpg
Add as you go – Step 2 Development Process

High Dose

Low Dose

Low Dose

Control

Control

“Phase 3”

Large n

“Phase 2”

Small n


Subtract as you go step 1 l.jpg
Subtract as you go – Step 1 Development Process

High Dose

Low Dose

Control

“Phase 3”

“Phase 2”


Subtract as you go step 2 l.jpg
Subtract as you go – Step 2 Development Process

High Dose

Low Dose

Control

Control

“Phase 3”

“Phase 2”


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Practical Consideration: Development Process 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 Development Process

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 Development Process (Usually 5 – 10 years)

------Phase 1----------------------Phase 2-----------------------------Phase 3---------------------


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Goals for Improving Efficiency of Clinical Development Development Process

  • 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 Development Process (3-7 years)

---Early development----------Registration Development--------


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Key Requirements – for Adaptive Trials Development Process (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 Development Process

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 Development Process

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


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Wyeth eReview System Development Process

  • 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 Development Process

  • 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 Development Process

  • 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 Development Process

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 Development Process

  • 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 Development Process

  • 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 Development Process 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 Process

  • 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 Process

  • 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 Development Process

  • 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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