1. Christopher C. Gallen, M.D., Ph.D. Vice President, Wyeth Research Strategic Challenges in Neuroprotective Drug Development March 15, 2003 Washington, D.C.

2. 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

3. Source: OECD-OECD Health Data, 1998. *1997 data U.K.Japan*ItalyNetherlandsCanadaFranceGermanyU.S. As a Percentage of Gross Domestic Product in Major Industrialized Countries, 1997 Health Care Costs

4. Source: OECD-OECD Health Data, 1998. *1997 data U.K.Japan*ItalyNetherlandsCanadaFranceGermanyU.S. Pharmaceutical Costs

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

6. Source: Cap Gemini Ernst and Young, 2002. Global Market Research & Analysis Nov 8% Nov 8% Pfizer 14% GSK 18% Aventis 22% Roche 24% Bristol-Myers 26% Merck 27% Eli-Lilly 36% Schering-Plough 41% AstraZeneca 50% % of Total Sales to 2005 Vulnerable to Patent Expiration The Patent Expiration Challenge Over $100B of Products Face Generic Competition by 2005

7. 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 Companies 1 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

8. 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 of NASs in development on December 31st, 2001 IO0-10099 24/05/02 Source: Institute for Regulatory Science Percentage of companies active in each therapeutic area

9. 0510152025 Respiratory Musculoskeletal Cardiovascular Antiinfectives Cancer Alimentary & Metabolism Nervous System Number of NASs first tested in man in 2001 020406080100120140 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

10. Source: PhRMA Annual Survey, 2001. U.S. FDA. Global Market Research & Analysis But R&D Productivity is Decreasing

11. Discovery and Development Costs are Increasing Source: DiMasi et al., Tufts CSDD R&D Cost Study, 2002

12. 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

13. 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

14. Number of patients per phase III study to support first submission 0 200 400 600 800 1000 1200 Anti-infectives Alimentary/metabolism Respiratory Anti-cancer Musculoskeletal Nervous system CVS Mean number of patients Therapeutic area Where enrolment completed 1999-01

15. 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) Years 3.2 2.5 2.4 5.1 4.1 2.1 5.9 5.5 2.8 6.1 6.1 2.6 8.1 11.6 14.2 14.8

16. Drug Approval Times are Increasing Again Source: U.S. Food and Drug Administration Mean Approval Time (Months) Calendar Year 3026252228533935272430 Total Number of New Drugs Approved in Each Year

17. Time to termination by therapeutic area (for NASs terminated 1999-2001) Source: CMR International

18. Breakdown of reasons for termination (for NASs terminated 1999-2001) Source: CMR International

19. 30 40 50 60 70 80 90 100 1.41.92.42.93.4 Average duration of phase III (decreasing left to right) Success rate: phase III to submission 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

20. Why are Success Rates Declining? Discovery issues Conceptual issues re disease models Clinical Trial issues

21. 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

22. Technological Challenges Structure-based Drug Design  Match molecules to targets different from in-situ conformation  Fit for in vitro viral proteins likely > CNS proteins Combinatorial Chemistry  Existing libraries limited by origins - monoamine GPCRs, steroid receptors and serine-aspartyl proteases

23. Why is CNS Particularly Challenging? Normal Functioning  Intimate connections, fine timing and pattern code  Parallel paths, multiple systems/step  Instantaneous mutual regulation  Self regulation of the system over time Antagonists versus agonists Single target bullets best for probes Therapies generally multi-target

24. CNS Disease Animal Models can be Misleading Model congruity with disease  Understand the animal model  Understand the human disease  Show them to be congruent in all important respects Cell Culture  Cell-cell interactions, relation to nutritional systems, exogenous environment, phospholipid composition all differ Mouse Models  Major failures of single genes  Strain differences suggest a cause for concern

25. 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

26. 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

27. 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?

28. Disease Models Reality is a complex set of interactions Each step can be modeled as differential equations  Myriad publications describe individual pieces  Supplemented 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

29. 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

30. 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

31. Changing the Business Model Historical  Platform oriented  First line treatments, one size fits all, mass population, easy (oral) treatment, ameliorating chronic disease  One treatment per disease Next Generation  Disease focus  Defined populations  Administered by specialists  Targeted treatments  Expand treatments to capture therapeutic subpopulations  Polypharmacy in cases (similar to oncology development)

32. Pharma and Academicians Partnership  Intellectual challenge of deciphering targets  Building disease models Closer ongoing collaborative contact  Remote presence technologies  Secure e-data sharing

33. 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

34. 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