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Presentation Outline

� A problem statement� Base-lining within Pfizer� Enhanced Clinical Trial Design (ECTD)

� Recommendations� Roll out activities� Impact metrics

� Enhanced Quantitative Drug Development (EQDD)� Recommendations

� EQDD = model based drug development

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What is the Problem?

�FDA cleared just 17 NMEs in 2002, the lowest number since 1984. Total NMEs approved in 2005 was 18, the worst tallies in a generation.�

�FDA approved 39 NMEs in 1997; no fewer than 7 of these drugs were subsequently withdrawn from the market�

From In Vivo – The Business and Medicine Report, Feb 2006, pp 63-70

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What the Problem is

� (Level of confidence in) Prediction� How to efficiently identify �products� from the

abundance of compounds emanating from discovery?� Increasing failure rate in Phase 2 for compounds

completing Phase I� Why do compounds fail for lack of effectiveness in

Phase 3?� Failure rate is 50% in Phase 3 for compounds completing

Phase II� Failure to sufficiently differentiate from placebo

� The cost of medication is seen as “too expensive” in the developed countries and unaffordable in the rest of the world

Data from Dr. Peter Kim, President of Merck, PhRMA, and McKInsey & Co.

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Was Pfizer Any Different (in 2004)?

0% 20% 40% 60% 80% 100%

Total

Phase II

Phase III

Phase IIIb

Phase IV

Positive Negative Equivocal

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Form 10-Q for PFIZER INC 3-Nov-2006

Quarterly Report

Enhanced Clinical Trial Design is a key Pfizer initiative aimed at reducing the frequency and cost of clinical trial failures, which is a common issue across the industry. The standardization and broad application of advanced improvements in quantitative techniques, such as pharmacokinetic/pharmacodynamic modeling and computer-based clinical trial simulation, along with use of leading edge statistical techniques including adaptive learning and confirming approaches, have transformed the way we design clinical trials today. Benefits achieved include improvements in positive predictive capacity, efficiency, risk management and knowledge management. Once fullyimplemented, the Enhanced Clinical Trial Design initiative is expected to yield significant savings and enhance research productivity.

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Key Recommendations for Enhanced Clinical Trial Design

� Adopt quantitative approaches to enhance design, analysis and interpretation or programs and trials

� Quantify exposure response relationships prior to PIII� Characterize and articulate inherent risk of studies and

programs� Pursue resource-efficient designs & methodologies� Design studies based on most complete knowledge of

a compound� Ensure teams possess appropriate TA and drug

development expertise and exhibit desirable behavioural characteristics and communication skills

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“Ensure teams possess appropriate TA and drug development expertise and exhibit desirable behavioral

characteristics and communication skills”

Protocol LevelPatient Level

Clinicians

Clinical Pharmacologists

Statisticians

Program Level

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“Ensure teams possess appropriate TA and drug development expertise and exhibit desirable behavioral

characteristics and communication skills”

Clinical Pharmacologists

Statisticians

Program Level

Clinical TeamProtocol LevelPatient Level

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“Ensure teams possess appropriate TA and drug development expertise and exhibit desirable behavioral

characteristics and communication skills”

� Visible and active senior level sponsorship� Initially, line specific technical training

� Orientation around what to expect from others� Secondly, case based workshops bringing three

lines together� Six development sites, across 3 continents, n≈1000

colleagues, over 2.5 days� Appreciation of the problem using historic cases� Act as �governance� reviewing program strategy from PIIa

through PIV� Act as clinical team developing compound emphasizing

EQDD and Knowledge Management

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Richard Mead (1673-1754)

�Mathematical learning will be the distinguishing feature between a medic

and a quack.�

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ECTD (Learning and Confirming) Emphasis on de-risking late phase studies

Alternative Statistical Designs

EQDD Knowledge Management

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Adoption of Alternative Statistical Designs

� Design options considered within a "fit for purpose" context� group sequential designs

� stopping early for success and/or futility� flexible & adaptive designs

� terminating some dose groups based on interim results� altering randomization ratios to increase precision in the

determination of an effective dose� Studies in the �learning phase� look and behave

differently from those in the �confirming phase�� Appropriate design choice can effectively manage risks

� probability of a given strategy and/or design successfully achieving a favorable outcome

� Further efficiencies gained through designs which leverages knowledge obtained from EQDD

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David Hume (1711�1776)

�All knowledge degenerates into probability.�

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Objective to Decrease Costs per Successful PIII Trial

Clinical Grant $ in phase II & III trials No. of successful

Phase III trials$

Success

25%*22%*13%14%baseReduction in SpendPer Positive Ph III Trial

20072006200520042003

* 3 year running mean

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Efficient Statistical Designs in Ph II*

Increase prob successMeta-analysisUrinary incontinence

Increase prob successMeta-analysisLUTS260 pts, 1 yearOmit Ph IIbGlobal anxiety disorder437 ptsModel based D/RRheumatoid arthritis1025 ptsModel based D/RGastro-esoph. reflux

120 pts, 1 yearPrior data supplementation, Model based D/R

Type II diabetes

760 pts, 1 yearPrior data supplementation, Model based D/R, sequential design

Fibromyalgia1000 ptsModel based D/RHot flashes

2750 pts, 1 yearOmit PIIa, Model based D/R, adaptive design

Thrombo-embolism

Efficiencies**ApproachIndication

*efficiency gains from futility analyses only realised during study conduct**smaller studies also reduce clinical development time

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ECTD (Learning and Confirming) Emphasis on de-risking studies

Alternative Statistical Designs

EQDD Knowledge Management

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Knowledge Management

Alternative Statistical Designs

EQDD

EQDD (integrated analyses informing strategies, designs and decisions)Emphasis on de-risking programs

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Enhanced Quantitative Drug Development

� Increase understanding of the pharmacological properties of a compound across a range of experimental conditions, development phases, patient populations

� Quantification of potential risks (sources of uncertainty) along with contingency plans that enable these risks to be managed appropriately� both protocol and program levels

� Uncertainty is a measure of how well teams understand or can predict the clinical outcomes for a compound

� Uncertainty cannot be eliminated entirely, it can be addressed more effectively and stated more formally

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Sir William Osler (1849-1919)

�Medicine is a science of uncertainty and an art of probability.�

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EQDD Founded on Knowledge Management

� Comprehensive quantification uses data from multiple sources� prior data on the compound, mechanistically related compounds,

other compounds in the same area/indication� patient and/or summary level data

� Provides greater insights (certainty) around a compound's properties with less �new� data by leveraging the accumulated data more effectively

� Necessitates a description of emerging data and a logical prediction from accumulated data to occur routinely in a structured and coordinated manner

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EQDD in Design Phase� Beyond typical sample size methods

� Want to know something beyond properties of given design� In-depth consideration of operating characteristics

� Focus on false positive and negative rates (when you should GO or NO GO), probability of making a correct decision, probability of technical success

� Want to know something about the properties of the compound� Quantifies the ability of protocol/program to meet stated objectives

� Decisions based on a statistic or confidence interval limit(s) with respect to a target value

� Uncertainty in parameter estimates are incorporated into predictions� Design simulations performed averaging over the uncertainty in the

parameters� Provides a degree of robustness averaging over the set of possible

fixed �truths�� Experiments generate finite information; truth is unknown for subsequent

protocol designs and program decisions

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William Stanley Jevons (1835�1882)

�Perfect knowledge alone can give certainty, and in nature perfect knowledge would be infinite knowledge, which is

clearly beyond our capacities. We have, therefore, to content ourselves with partial knowledge � knowledge

mingled with ignorance, producing doubt.�

Structure of EQDD Framework DocumentDiscovery/Development Interval, Issue/Emphasis, Required Knowledge,

Activities at Compound, Mechanism, Physiological & Indication Level

Pre LDPOP

LDPOP to CAN

CAN to FIH

FIH to POM

POM to POC

POC to P2b

P2b to P3

P3 to Registration

P4

Efficacy

Safety

Risk: Benefit

Clinical Viability

Commercial Viability

Activities at:

Compound Level

Mechanism Level

Physiological and Indication Level

Required Knowledge

For unprecedented mechanisms

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Donald Henry Rumsfeld (1932 - )

��. as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we

know there are some things we do not know. But there are also unknown unknowns -- the ones we don't know we don't know."

"I would not say that the future is necessarily less predictablethan the past. I think the past was not predictable when it

started."

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Closing Comments

� Recognize the challenges posed by ever changing (internal, external, scientific) environments

� Structural vs functional optimization� Provide continual reinforcement� Provide demonstrable (meaningful) impact

PKUK 2008, 26PKUK 2008, 26thth--2828thth NovemberNovemberStanstedStansted LondonLondon

PKUK 2008 will take place at the Hilton Hotel, London Stansted Airport, from midday Wednesday 26th to midday Friday 28th November.

Registration will open on Thursday 1st May (9am UK time). To register, please visit www.pkuk.org.uk and select ‘register’ from the next meeting tab.

Early registration fees (on or before Friday 12th September), which are inclusive of accommodation and meals, are as follows:

•Industrial/Consultancy Organisations: £700•Academic/Governmental Organisations: £450

Students who are willing to give oral presentations may receive a bursary to cover up to half the registration fees.

As always, we would welcome any applications to give oral or poster presentations at the meeting.