Lydia Pan, PhD Director, Worldwide Policy, Pfizer Inc.

1. Innovating to Better Care: How personalized medicine is changing the biopharmaceutical marketplace Lydia Pan, PhD Director, Worldwide Policy, Pfizer Inc. Presentation to the Cancer Action Coalition of Virginia September 12, 2013

2. Personalized Medicine: Towards a Definition “Personalized medicine” refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment. Preventive or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not. Report of the President’s Council of Advisors on Science and Technology, September 2008 The rightdrug …for the right person …in the right dose …at the right time 1

3. Older Drugs were Developed Empirically Source of data: Brian B. Spear, Margo Heath-Chiozzi, Jeffery Huff, “Clinical Trends in Molecular Medicine,” Volume 7, Issue 5, 1 May 2001, Pages 201-204. 2

4. Today’s Medicines are Developed with More Precision Precision Medicine Medicines targeting patient segments that will have an optimal response to therapy Linking disease understanding and clinical outcomes Building disease understanding to identify the right pathways and targets Segmented (not personalized) 3

5. Recognition of Leukemia and Lymphoma Sub-types has Improved Outcomes 5-Yr Survival ~0% 70% Source: Malorye, Allison. “Is Personalized Medicine Finally Arriving?” Nature Biotechnology, May 2008 4

6. The Human Genome: A Great Opportunity for Drug Discovery?

7. Biopharmaceutical R&D Investment and New Medicines Approved Sources: Paraxel's Pharmaceutical R&D Statistical Sourcebook 2005/2006; FDA; PhRMA 6

8. New treatment Comparator Genomic-based Research Enables Precision Medicine Goal to improve survival Right Patient Right Target 1.0 0.8 0.6 Overall Survival Probability 0.4 0.2 0 0 6 12 18 24 30 36 Months of survival Drug targeted to specific oncogene or aberrant pathway driving the specific tumor Patient identified through molecular profiling of their tumor Ultimate objective is to improve survival 7

9. Challenges for Coordination of Rx/Dx Co-development The FDA prefers to review both Rx & Dx applications concurrently. Sponsor must coordinate between different FDA Centers Diagnostic PMA (CDRH) Clinical Development Therapeutic Phase 1 Phase 2 Phase 3 CDER/CBER FDA has multiple programs to expedite drug/biologic development and review: Fast Track, Accelerated Approval, Breakthrough Therapy, Priority Review CDRH does not have similar mechanisms to accelerate diagnostic approval.

10. Drugs Labels with Genomic Biomarker Information Testing required • Trastazumab / breast cancer FISH/IHC HER2 • Panitumumab / colon cancer KRAS wildtype • Vemurafenib/ melanoma BRAF V600E • Crizotinib / NSCLC ALK gene rearrangementsIvacaftor / cystic fibrosis CFTR G551D Testing recommended • Abacavir / HIV AIDS HLA-B 5701 variant • Irinotecan / colon cancer UGT1A1 variant • Azathioprine / autoimmune Thiopurinemethyltransferase • Warfarin / thrombosis, CV prophylaxis CYP2C9, VKORC Informational tests • Fluoxetine / depression CYP2D6 • Codeine / analgesia CYP2D6 • Clopidogrel / CV prophylaxis CYP2C19 • Chloroquine / malaria G6PD deficiency Adapted from Nature Biotechnology, 25, 509-517, 2007; Table of Pharmacogenomic Biomarkers in Drug Labels http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm 9

11. FDA Framework for Personalized Medicine:A “Mosaic” of Guidance Documents Additional guidance documents forthcoming

12. Multiple Ways for Tests to Reach the Marketplace Distinct pathways for LDTs and Dx test kits CMS (CLIA) Regulator FDA All Lab-Developed Tests 510k PMA Risk class III I II III II Clinical utility required Certification of laboratory performance standards FDA wants all CDx to go through PMA

13. Personalized Medicine: Key Components • Science & Technology • Driving the understanding of disease and the discovery anddevelopment of medicines • Regulatory science advances • Medical Practice • What’s best for the patient? • Changes in medical practice • Health Care Environment • How do we get personalized • medicines to patients? 12

14. Understanding of Oncologic Drivers is Rapidly Increasing Adenocarcinoma 2011 Targeting Oncogenic Drivers1 Adenocarcinoma 1999 Histology-driven Selection K-RAS EGFR B-RAF HER-2 PIK3CA ALK MET Unknown 13 References: 1. Massachusetts General Hospital, data on file2.Horn L, Pao W. J Clin Oncol 2009;26:4232–5

15. Creating a New Paradigm for NSCLC Treatment Traditional Paradigm Evolving Personalized Paradigm Metastatic disease (stage IIIB/ IV) Metastatic disease (stage IIIB/ IV) Multiple test options Biomarkers can direct treatment towards targeted therapy or clinical trials (where available) Squamous cell carcinoma Non-squamous cell carcinoma EGFR ERCC1 HER-2 K-RAS ALK B-RAF TS • More complex decisions involving more stakeholders beyond oncologist (surgeon, pathologist) • Education required to integrate molecular diagnostics into treatment decisions • Need for multiple molecular Dx creates competition for available tissue, budget, manpower • Not a “simple” issue of a single drug-diagnostic combination • Oncologist sole treatment decision maker • Treatment decisions depend on histology

16. Biomarkers Support Expansion of Use Adapted from GLEEVEC® prescribing information (www.novartis.com) 15

17. Payers Must Determine How to Pay for Personalized Medicine Test Coding: • AMA creatednew Molecular Diagnostic CPT codes for 2013 • Retirement of code-stacking • Unique tests still not identified (McKesson Z-codes) Coverage and Reimbursement: • CMS rolling out new policies for molecular diagnostics • MolDx test payments being set at local level by gap-filling; process has not been transparent • Proposed payment determinations for products paid under the CLFS included the decision NOT to pay for algorithm portion of multi-analyte tests • Increasingly, payers are demanding high levels of clinical evidence to justify the reimbursement of personalized medicine products • Challenges to generating timely evidence without denying or delaying access to treatment • Targeted therapeutics increasingly subject to utilization management tools 16

18. Challenges to Personalized Medicine in the Marketplace • Precision medicine may drive efficiencies in drug development but application of technologies isn’t cheap • Drug development may or may not be less costly • If targeting smaller,moredefined populations, medicines should have greater efficacy / safety risk ratios but also likely be more expensive • Diagnostics landscape is rapidly evolving – needs investment to sustain innovation • Integrating each new intervention into healthcare management takes time • Growing pressure to show PM improves health outcomes • Value loss if access is restricted 17

19. Rx to Deliver the Pipeline for Personalized Medicine • Aggressive application of science to R&D • Informatics tools to analyzelarge, multi-dimensional data sets • Closer industry-academia collaboration to drive customized therapy solutions • Novel clinical trial designs that incorporate new drug development tools • Opportunities to add value to existing and potential medicines • Secure systems that allow safe sharing of data between health care providers, industry and regulators to streamline development and approval processes • Collaborativerelationships with regulators that strengthen patient safety but also speed the approval of novel biomarker applications and Dx technologies • Evidence standards to demonstrate the effectiveness of diagnostics in improving patient outcomes 18

20. Toward a Health Care System that Delivers the Value of Personalized Medicine • Data systems that assure security and access to the growing body of patient data • Quality standards to insure data compatibility and comparability • Integrated health information: a complete systems-based readout of the health status of an individual in a given environment • Physicians need easy-to-interpret results • user-friendly technological interface • data from multiple sources • continuously refined algorithms and database updates • Enabling functions: standards, infrastructure, systems approach, sharing mechanisms • Education along the entire health care ecosystem Policy will determine success or failure of personalized medicine implementation 19

21. Thank You! Questions??? 20