Christopher C. Gallen, M.D., Ph.D.Vice President, Wyeth Research

Strategic Challenges in Strategic Challenges in Neuroprotective Drug Neuroprotective Drug

DevelopmentDevelopment

March 15, 2003Washington, D.C.

The Current World of Pharma The Big Picture Challenge for R&D-Driven

Pharmaceutical Companies The Challenges of CNS R&D Meeting the Challenge Changing the Model A Strategy Going Forward

Source: OECD-OECD Health Data, 1998.*1997 data

U.K. Japan* Italy Netherlands Canada France Germany U.S.

As a Percentage of Gross Domestic Product in Major Industrialized Countries, 1997

Health Care Costs

Source: OECD-OECD Health Data, 1998.*1997 data

U.K. Japan*ItalyNetherlands Canada FranceGermanyU.S.

Pharmaceutical Costs

What Happens When a Patent Expires?Prozac Total Prescriptions Per Month

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

-12

-10 -8 -6 -4 -2 1 3 5 7 9

11

13

15

To

tal R

x

Prozac

Generic

Source: Cap Gemini Ernst and Young, 2002. Global Market Research & Analysis

0.0% 10.0% 20.0% 30.0% 40.0% 50.0%

Nov Nov 8%8%

Pfizer 14%Pfizer 14%GSK 18%GSK 18%Aventis 22%Aventis 22%Roche 24%Roche 24%Bristol-Myers 26%Bristol-Myers 26%Merck 27%Merck 27%Eli-Lilly 36%Eli-Lilly 36%Schering-Plough 41%Schering-Plough 41%AstraZeneca 50%AstraZeneca 50%

% of Total Sales to 2005 Vulnerable to Patent Expiration

The Patent Expiration Challenge

Over $100B of Products Face Generic Competition by 2005

Pharmaceutical R&D Investment is High

Source: PhRMA, 2001, Based on Data from PhRMA Annual Survey and Standard & Poor’s Compustat, a Division of McGraw-Hill

17.0%

15.6%

12.8%

10.5%

8.4%

7.8%

5.3%

4.7%

3.9%

1.2%

3.9%

0.73%

3.8%

Research-based Pharmaceutical Companies1

Domestic R&D

Industrial Sector Comparison:

Drugs & Medicine

Computer Software & Services

Office Equipment & Services

Automotive

Telecommunications

Leisure Time Products

Aerospace & Defense

Metals & Mining

Paper & Forest Products

All Industries

Global R&D

Electrical & Electronics

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

Sensory organs

Hormones

Dermatological

Blood

Respiratory

GU & Sex hormones

Musculo-skeletal

Antiinfectives

Alimentary & Metabolism

Cardiovascular

Cancer

Nervous system

Percentage of companies

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

Sensory organs

Hormones

Dermatological

Blood

Respiratory

GU & Sex hormones

Musculo-skeletal

Antiinfectives

Alimentary & Metabolism

Cardiovascular

Cancer

Nervous system

Percentage of companies

Major companies(n=14)

Other companies(n=24)

Therapeutic area ordered by decreasing number ofNASs in development on December 31st, 2001

IO0-1009924/05/02Source: Institute for Regulatory Science

Percentage of companies active in each therapeutic area

0 5 10 15 20 25

Respiratory

Musculoskeletal

Cardiovascular

Antiinfectives

Cancer

Alimentary &Metabolism

Nervous System

Number of NASs first tested in man in 2001

0 20 40 60 80 100 120 140

Respiratory

Musculoskeletal

Antiinfectives

Cardiovascular

Alimentary &Metabolism

Cancer

Nervous System

Number of NASs in development

Source: Institute for Regulatory Science

Nervous system NASs dominate the development pipeline

Source: PhRMA Annual Survey, 2001. U.S. FDA. Global Market Research & Analysis

But R&D Productivity is Decreasing

0

5

10

15

20

25

30

35

4019

80

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1999

2000

2001

2002

E

R&

D E

xp

en

dit

ure

s (

US

$ B

illi

on

s)

0

10

20

30

40

50

60

New

Molecular E

ntities Approved

Discovery and Development Costs are Increasing

84

214

336

54104

466

138

318

802

0

100

200

300

400

500

600

700

800

900

Mill

ion

s o

f 20

00 $

Preclinical Clinical Total

1970s approvals 1980s approvals 1990s approvals

Source: DiMasi et al., Tufts CSDD R&D Cost Study, 2002

Source: Boston Consulting Group, 1993; Peck, C., “Drug Development: Improving the Process,”Food & Drug Law Journal, Vol. 52, 1997.

Number of Trials

Clinical Trial Number Per NDA is Increasing

Source: Boston Consulting Group, 1993; Peck, C., “Drug Development: Improving the Process,”Food Drug Law Journal, Vol. 52, 1997.

Number of Patients

Number of Patients Per NDA is Increasing

Number of patients per phase III study to support first submission

0

200

400

600

800

1000

1200

Anti-in

fecti

ves

Alimen

tary

/met

aboli

sm

Respir

ator

y

Anti-c

ance

r

Mus

culos

kelet

al

Nervo

us sy

stem

CVS

Mean number of patients

Therapeutic area

Where enrolment completed 1999-01

R&D Cycle Times are Increasing

Source: Joseph A. DiMasi, “New Drug Development; Cost, Risk and Complexity”, Drug Information Journal, May 1995. (From R&D Directions, 1996)

0

2

4

6

8

10

12

14

16

1960s 1970s 1980s 1990s

Pre-IND Phase IND Phase NDA Phase

Yea

rs

3.23.2

2.52.5

2.42.4

5.15.1

4.14.1

2.12.1

5.95.9

5.55.5

2.82.8

6.16.1

6.16.1

2.62.6

8.1

11.6

14.2 14.8

Drug Approval Times are Increasing Again

Source: U.S. Food and Drug Administration

Mea

n A

pp

rova

l T

ime

(Mo

nth

s)

30.3 29.926.5

19.7 19.217.8

16.2

11.7 12.6

17.6 16.4

0

6

12

18

24

30

36

42

91 92 93 94 95 96 97 98 99 00 01

Calendar Year

3030 2626 2525 2222 2828 5353 3939 3535 2727 24243030 Total Number of New Drugs Approved in Each Year

Time to termination by therapeutic area(for NASs terminated 1999-2001)

0

25

50

75

100

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Time from first human dose (years)

Per

cent

age

of N

AS

s te

rmin

ated

Nervous system Anti-infectives Cardiovascular

Source: CMR International

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Clinical efficacy ClinicalPk/bioavailability

Clinical safety Portfolioconsiderations

Toxicology Various

Per

cen

tag

e o

f te

rmin

atio

ns

Nervous system (n=127 )

All therapy areas excluding nervous system (n=360)

Breakdown of reasons for termination(for NASs terminated 1999-2001)

Source: CMR International

30

40

50

60

70

80

90

100

1.41.92.42.93.4

Average duration of phase III (decreasing left to right)

Su

cces

s ra

te:

ph

ase

III t

o s

ub

mis

sio

n

Anti-infectives

Respiratory

Nervous system

CVS

Musculo-skeletal

Alimentary/metabolism

Oncology

Bubble size = current market size (IMS); number in bubble = number of NASs in phase II/III development

Attractiveness profile of industry’s late stage pipeline

42

39

High success rate, slow cycle time

Fast cycle time,low success rate

High success rate,fast cycle time

Low success rate, slow cycle time

25

18

51

19

43

Why are Success Rates Declining?

Discovery issues Conceptual issues re disease models Clinical Trial issues

Genomic Targets: Promise and Concerns

The Promise - improved diagnostics, fundamentally targeted treatments

Reality: Proliferation of “targets” - but targets with a limit

Within target heterogeneity Challenging targets - known models of molecular

dysfunction Most targets likely “loss of function” Large market diseases polygenic Twin concordance rates disturbing

Technological Challenges

Structure-based Drug DesignMatch molecules to targets different from in-situ

conformation

Fit for in vitro viral proteins likely > CNS proteins

Combinatorial ChemistryExisting libraries limited by origins - monoamine

GPCRs, steroid receptors and serine-aspartyl proteases

Why is CNS Particularly Challenging?

Normal FunctioningIntimate connections, fine timing and pattern codeParallel paths, multiple systems/stepInstantaneous mutual regulationSelf regulation of the system over time

Antagonists versus agonists Single target bullets best for probes Therapies generally multi-target

CNS Disease Animal Models can be Misleading Model congruity with disease

Understand the animal modelUnderstand the human diseaseShow them to be congruent in all important respects

Cell CultureCell-cell interactions, relation to nutritional systems,

exogenous environment, phospholipid composition all differ

Mouse ModelsMajor failures of single genesStrain differences suggest a cause for concern

Meeting the Challenge: Clinical Rigor

Success rates are too low to tolerate avoidable flaws

Animal testing under one set of conditions, human trials under another

Ignoring the “does it make scientific sense?” test

Animal models measuring very different dependent variables

Inadequate determination of dose and duration

Using Technology to do Better Trials Key: Near-time trial conduct and analysis Scrutinize blinded data to detect poor sites Exploratory development - double-blind but not

triple blind Exploratory data analysis oriented database

and approach for better programs and submissions

Modeling and simulation for better trials Adaptive trial designs to optimize dose-ranging

Experimental Medicine - Part of the Solution

Is the compound absorbed? Does the compound penetrate to the desired

site of action? For appropriate period of time? Mechanism consistent with hypothesis? Biological effect? Free of class-associated limiting toxicities?

Disease Models Reality is a complex set of interactions Each step can be modeled as differential equations

Myriad publications describe individual piecesSupplemented with research to test the model

Technology allows generation of increasingly sophisticated disease models

Stronger model will produce the insights on target selection and effective therapies

Core Intellectual Property

Electronic Technologies can Improve Chemistry

NIH Protein Structure Initiative Increased supercomputer modeling of protein

folding and interactions Virtual screening Virtual combinatorial chemistry Moving past target to cross-assessing

potential toxic interactions and metabolism

Biological Technologies Have Great Promise

35% of the 37 NAS launched in 2001 Biologics have important attractions Typically less toxic, more predictable

Increasingly human derived

Easier to predict distribution, metabolism and elimination

Faster development Higher success rates Huge ability to match potential targets

Changing the Business Model Historical

Platform orientedFirst line treatments, one size fits all, mass population, easy

(oral) treatment, ameliorating chronic diseaseOne treatment per disease

Next GenerationDisease focusDefined populationsAdministered by specialistsTargeted treatmentsExpand treatments to capture therapeutic subpopulationsPolypharmacy in cases (similar to oncology development)

Pharma and Academicians

Partnership

Intellectual challenge of deciphering targets

Building disease models Closer ongoing collaborative contact

Remote presence technologies

Secure e-data sharing

Pharma and Regulators

Shifting to a model of early POC studies in man for both target and molecule validation calls for earlier consultations

Partnership Closer ongoing collaborative contact Rolling dossiers Marketing rights will change from being one-off

to continuous evaluation

A Strategy Going Forward

Focus on intellect and collaboration Pharma focus on disease model Experimental medicine model Tap the power of the information revolution Tap the power of biologic-based technologies Adapt the Business Model