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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