The Challenge of Transforming Scientific Innovation into Commercial Success

1. The Challenge of Transforming Scientific Innovation into Commercial Success Dr. Norbert Riedel Kellogg Biotech Symposium April 16, 2004

2. Healthcare is Changing Rapidly • Increasing age of world population • Increased prevalence of life-threatening conditions • Cost-containment pressure from governments and other payors • Expanding patient knowledge and empowerment • More diverse sources of innovation • Personalized Medicine • Rapid technological change

3. Predicted 2003 and 2007 sales in the top nine markets in Europe and the top five in selected other regions at ex-manufacturers’ prices using forecast exchange rates. Source: IMS Market Prognosis Global

4. Top Ten Companies Worldwide in Prescription Sales for the First Six Months of 2003

5. Leading Major Companies by Profitability (profit as % of sales) in 2002

6. Leading Companies Ranked by Pharma R&D Spending in 2002

7. Major New US Product Approvals

8. BIO’s Biotech Drug Approval List Also approved in 2003 were 13 additional recombinant proteins, monoclonal antibodies, small-molecule products and selected tissue-engineered products. Biotechnology Industry Organization (BIO) considered relevant to the sector. With the exception of FluMist and Advate, all these products were transferred from the Center for Biologics Evaluation and Research (CBER) to CDER, but were not included on CDER’s list of NME approvals for 2003.

9. Major new EU product approvals

10. With only 31 new active substances last year, the much-vaunted revolution in drug development still seems a long way off.

11. Blood, immune and autoimmune disorders Cancer CNS Disorders Infectious diseases, including biodefense Tissue engineering Surgical situations Trauma Increase in Life-Threatening Conditions Presents Major Growth Opportunities Small molecules, biopharmaceuticals and vaccines for:

12. Bioinformatics Blood safety (prions) Drug delivery Homecare Medication error prevention Personalized Medicine Internet-based clinical trials Minimally invasive surgery Nanotechnology Proteomics Stem cells Cell therapy Tissue engineering Organ replacement Telemedicine/remote patient monitoring What’s Next in Technology?

13. Technological Changes areCreating New Possibilities Contemporary 1920s 1950s 1980s Major New Drug Classes Combinatorial small molecules, designer peptides Recombinant human proteins, monoclonal antibodies, nucleic acids Antibiotics Cardiovascular drugs, antihypertensives psychotropics Combinatorial chemistry, combinatorial peptides Genetic engineering, tissue culture New Drug Sources Proteins Genomics, proteomics, functional genomics, high-throughput screening, transgenic/knockout model systems, transcript profiling New Target Identifaction Tools Molecular biology, molecular genetics, PCR, rDNA Biotechnology, cell biology Targets Genes Genes, genetic regulatory elements, signal transduction pathways, protein-protein and protein-macromolecular interactions Human subjects Microorganisms, animal models Enzymes, receptors Sources: Pharmaceutical Research and Manufacturers of America, ING Baring Furman Setz

14. Future Mandate for R&D • Shorten discovery timelines • Strengthen development efforts • Increase research productivity

15. Timelines in drug development Targets selected Lead series selected Candidate leads selected IND approval NDA approval Lead selection Lead optimisation (in vitro ADMET) Target identification Target selection Preclinical evaluation (in vivo ADMET) Clinical evaluation Safety/Efficacy Marketing & sales Monitoring-ADR OLD PARADIGM Lead identification 1.5-4 yr 1.5-1 yr 1-2 yr 1-2 yr 8-10 yr 0.5 yr 0.5 yr 1-1.5 yr 1-1.5 yr 7-9 yr Increased efficiency in drug development Technological drivers: Genomics, proteomics, metabonomics, disease models, combinatorial chemistry, HTS, bioinformatics… Increased lifecycle of new drugs NEW PARADIGM 2-6 yr

16. 35% 30% 35% Even small improvements in compound selection will have huge effects on profits With up to 70% of clinical trial spending wasted on trials of failed compounds… Annual spending in clinical trials …a 1 percent improvement in selection can save $7 million from each $1 billion of an annual development budget. Total: $20 billion $700 million potential improvement opportunity by improving selection process Drugs that fall due to selection of wrong targets For each $1 billion of an annual development budget… Successful drugs …70% is spent on failed drugs $70 million for a 10 percent improvement Drugs that fall due to poor chemistry

17. Drug Discovery in the 21st Century High Throughput Screening Genomics/Proteomics Novel Targets Informatics Therapeutic Areas Informatics Informatics Combinatorial Chemistry

18. Medicinal chemist Pharmacologist/ toxicologist Clinical researcher Molecular biologist/ bioinformatician Characterize mechanism of action Prioritize lead compounds Characterize ‘off target’ effects Develop surrogate markers Understand clinical responses Discovery Preclinical Clinical Target selection Lead optimization IND NDA

19. HIGH-EFFICIENCY PROTEOMICS (applied to small molecule drug discovery) Informatics Proteomics Sorting of cells from tissue, in vitro cell culture Affinity chromatography (MDLC) Quantitation & Identification (LC-MS, MS/MS) Genome sequence (identification & in silico triage) INPUT: Normal & Disease Samples (+ in vitro) Affinity capture of protein class (in vitro/invivo) Proteomic profiling of differences Validate with class-specific inhibitors (in vitro/in vivo) Structure- based drug design OUTPUT: Optimised lead X-ray crystallography Focused library (+ biology) Class-specific agonists/antagonists Activity-based affinity reagents Chemistry

20. QUANTITY Library Generation Medicinal Chemistry QUALITY TARGET VALIDATION cell-based SAR Functional Genomics Proteomics LEAD PROFILING ASSAY DEVELOPMENT MET/TOX Primary Screening HIT VALIDATION cell-based

21. Key Srategies for Success Industrialization of lead identification Data warehousing and portals for knowledge access Customer groups Innovation Commercialization Internet-based clinical trials Computer-aided trial design and simulation

22. IBM’s New Business Model for Pharma Accurate Assessment of the Threshold of Innovation Investment New Products Discovery Marketing Development Manufacturing Sales Traditional Products High Density Products Targeted Treatment Solutions New development approach Adaptive trials and in-life testing Rolling dossier Outcomes – oriented marketing EMRs Smaller and smarter sales force Integrated media New disease-led approach Multiple supply chain models

23. Integrating Into Networks of Innovationis a Key Growth Strategy Areas of Strength Areas of Weakness Academia Preferred Flows Biotech Start-up Companies Large Healthcare Companies Regulatory Marketing & Sales Exploratory Research Development Manufacturing

24. Key Business Challenges for Biotech • Create a strategic vision for “grown up” companies • Deliver earnings to support valuations • Structure value maximizing alliances with traditional pharmaceutical companies • Evaluate mergers and acquisitions • Build production capacity

25. Challenge: Define Strategic Vision • What is the appropriate vision? • Fully integrated pharmaceutical company or pure discovery engine? • What therapeutic / technology focus should a company have? • Diagnostics, delivery or drugs? Technology Play? • What supporting functions, processes are needed? • Discovery to development to portfolio management to marketing

26. Challenge: Translate InnovationInto Earnings • How do companies maximize the value of their: • Technology platforms • Identify the appropriate targets and tools • Development candidates • Balance technical and commercial risk • Marketed products • Expand capacity and develop line extension strategies

27. Challenge: StructureValue-Maximizing Alliances • Alliances between biotechs and pharma firms will become even more important • How do biotechs balance short-term funding needs with long-term value maximization? • What are the elements of win-win alliances? • What strategic frameworks and processes can shape future alliances?

28. Some of the more significant deals signed between pharma and biotech companies last year. Valuations cannot be directly compared because they have not been uniformly calculated.

29. Biotech Disappointments • Merck & Co drops Celltech compound • Novo Nordisk tries to stop Pfizer ending its hormone replacement therapy deal • Lilly drops Ono’s sivelestat • Lilly ends a deal to develop oral insulin with Generex • Biogen ends Icos deal • Bayer shelves key PPL project • Pfizer ends deal with Phytopharm’s P57 obesity project • AstraZeneca pulls out of a deal with NicOx • Cambridge Antibody Technology/Abbott deal goes sour • GlaxoSmithKline scraps its oral insulin deal with Nobex • Abbott scraps its diabetes deal with Karo Bio • Pfizer hands back Celltech’s antiheumatic • Pfizer returns product rights to Debiopharm

30. Challenge: Evaluate M&A • The biotech industry is still highly fragmented, and more consolidation is inevitable • Successful companies will proactively evaluate: • When is the right time for M&A? • What attributes make a candidate attractive? • What synergies exist, and how quickly can they be realized?

31. Biotech companies are consolidating among themselves or being bought up by pharma