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

Presentation to the Cancer Action Coalition of VirginiaSeptember 12, 2013

Innovating to Better Care: How personalized medicine is changing the

biopharmaceutical marketplace

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 right drug …for the right person

…in the right dose …at the right time

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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.

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Today’s Medicines are Developed with More Precision

Medicines targeting patient segments that will

have an optimal response to therapy

Building disease understanding to identify the right

pathways and targets

Linking disease understanding

and clinical outcomes

Precision Medicine

Segmented (not personalized)

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Recognition of Leukemia and Lymphoma Sub-types has Improved Outcomes

100 years ago Disease of the blood

80 years ago Leukemia or lymphoma

60 years ago

Chronic leukemia Acute leukemia Preleukemia

Indolent lymphomaAggressive lymphoma

Today

~38 leukemia types identified:• Acute myeloid leukemia (~12 types)• Acute lymphoblastic leukemia (2 types)• Acute promyelocytic leukemia (2 types)• Acute monocytic leukemia (2 types)• Acute erythroid leukemia (2 types)• Acute megakaryoblastic leukemia• Acute myelomoncytic leukemia (2 types)• Chronic myeloid leukemia• Chronic myeloproliferative disorders (5 types)• Myelodysplastic syndromes (6 types)• Mixed myeloproliferative/myelodysplastic

syndromes (3 types)

51 lymphomas identified:• Mature B-cell lymphomas (~14 types)• Mature T-cell lymphomas (15 types)• Plasma cell neoplasm (3 types)• Immature (precursor) lymphomas

(2 types)• Hodgkin’s lymphoma (5 types)• Immunodeficiency-associated

lymphomas ~ 5 types)• Other hematolymphoid neoplasm's

(~7 types)

5-Yr Survival

~0%

70%

Source: Malorye, Allison. “Is Personalized Medicine Finally Arriving?” Nature Biotechnology, May 2008

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The Human Genome: A Great Opportunity for Drug Discovery?

Biopharmaceutical R&D Investment and New Medicines Approved

Sources: Paraxel's Pharmaceutical R&D Statistical Sourcebook 2005/2006; FDA; PhRMA 7

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3.2 3.6 4.1 4.8 5.7 6.5 7.3 8.4 9.611.612.713.4

15.216.9

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29.831.0

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50.748.648.5NME

R&D Spend

Year

Num

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ts

$ Bi

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Genomic-based Research Enables Precision Medicine

Right Target Right Patient Goal to improve survival

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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

New treatmentComparator

0 6 12 18 24 30 360

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Ove

rall

Surv

ival

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babi

lity

Months of survival

Phase 1 Phase 2 Phase 3

Clinical Development

Challenges for Coordination of Rx/Dx Co-development

PMA (CDRH)

CDER/CBER

The FDA prefers to review both Rx & Dx applications concurrently. Sponsor must coordinate between different FDA Centers

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.

Diagnostic

Therapeutic

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 rearrangements

Ivacaftor / cystic fibrosis CFTR G551D

Testing recommended– Abacavir / HIV AIDS HLA-B 5701 variant– Irinotecan / colon cancer UGT1A1 variant– Azathioprine / autoimmune Thiopurine methyltransferase– 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

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FDA Framework for Personalized Medicine:A “Mosaic” of Guidance Documents

Document Type Title DateConcept Paper Drug-Diagnostic Co-Development April 2005

Guidance Pharmacogenetic Tests and Genetic Tests for Heritable Markers

Feb 2006 (draft)June 2007 (final)

Draft Guidance Pharmacogenomic Data Submissions Aug 2007

Draft Guidance In Vitro Diagnostic Multivariate Index Assays Sept 2006 (draft)Feb 2007 (public meeting)July 2007 (revised)2010 (withdrawn)

Draft Guidance (FAQ)

Commercially Distributed In Vitro Diagnostic Products Labeled for Research Use Only or

Investigational Use Only

June 2011

Draft Guidance In Vitro Companion Diagnostic Devices July 2011

Guidance Clinical Pharmacogenomics: Premarketing Evaluation in Early Phase Clinical Studies

January 2013(draft Feb 2011)

Additional guidance documents forthcoming

I IIIII IIIII

CMS(CLIA) FDA

510k PMAAll Lab-Developed Tests

Distinct pathways for LDTs and Dx test kits

Certification of laboratory performance standards

Multiple Ways for Tests to Reach the Marketplace

Risk class

Clinical utility required

Regulator

FDA wants all CDx to go through PMA

Personalized Medicine: Key Components

• Science & Technology• Driving the understanding of disease and the discovery and

development of medicines• Regulatory science advances

• Medical Practice- What’s best for the patient? - Changes in medical practice

• Health Care EnvironmentoHow do we get personalized omedicines to patients?

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Understanding of Oncologic Drivers is Rapidly Increasing

References: 1. Massachusetts General Hospital, data on file 2. Horn L, Pao W. J Clin Oncol 2009;26:4232–5 14

Adenocarcinoma 1999Histology-driven Selection

K-RAS

EGFR

B-RAF

HER-2

PIK3CAALK

MET

Unknown

Adenocarcinoma 2011Targeting Oncogenic Drivers1

Evolving Personalized Paradigm

Metastatic disease (stage IIIB/ IV)

Biomarkers can direct treatment towards targeted therapy or

clinical trials (where available)

EGFR K-RAS ERCC1 ALK TS B-RAFHER-2

Traditional Paradigm

Non-squamous cell carcinoma

Metastatic disease (stage IIIB/ IV)

Squamous cell carcinoma

Creating a New Paradigm for NSCLC Treatment

Oncologist sole treatment decision maker Treatment decisions depend on histology

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

Multiple test options

Biomarkers Support Expansion of Use

Therapeutic Biomarker Indication(s)

GLEEVEC® Imatinib

C-Kit Gastrointestinal stromal tumors, aggressive systemic mastocytosis

Philadelphia Chromosome

chronic myeloid leukemia, acute lymphocytic leukemia

PDGFR myelodysplastic/ myeloproliferative diseases

FIP1L1-PDGFRα hypereosinophilic syndrome and/or chronic eosinophilic leukemia

Adapted from GLEEVEC® prescribing information (www.novartis.com)

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Payers Must Determine How to Pay for Personalized Medicine

Test Coding: • AMA created new 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

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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, more defined 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

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Rx to Deliver the Pipeline for Personalized Medicine

• Aggressive application of science to R&D

– Informatics tools to analyze large, 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

• Collaborative relationships 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

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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

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Thank You!

Questions???

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