Biomarker Based PGx Strategies Rick Hockett, MD Chief Medical Officer Affymetrix.

Biomarker Based PGx Strategies

Rick Hockett, MD

Chief Medical Officer

Affymetrix

2Affymetrix Confidential

One size fits allOne size fits all

GoalGoal: Improve individual patient outcomes and health outcome predictability through tailoring drug, dose, timing of treatment, and relevant information

assess spectrum of patient response to therapy; stratify patient populations; optimize benefit/risk.

“Providing meaningful improved health outcomes for patients by delivering the right drug at the right dose at the right time.”

Tailoring (e.g. oncology productscomprising drug and companion diagnostic)

Targeted Targeted TherapyTherapy

Tailoring is Broader Than Pharmacogenomics

Measure something in a patient to learn how to prescribe medicine

Why Are Biomarkers So Important?


Increased Benefit:Risk Scenarios““Providing meaningful improved health outcomes for patients Providing meaningful improved health outcomes for patients by improving diagnosis, prognosis, or therapy choice.”by improving diagnosis, prognosis, or therapy choice.”

Can apply one or more scenarios to each Lilly compound. Can apply one or more scenarios to each Lilly compound. Scenarios can often be interdependent.Scenarios can often be interdependent.

Accommodate info for patient diversity, questions specific to payors or providers, or provide tools to meet needs of customers

Tailoring “Tailoring “Information/Information/ ToolsTools””

Identifying “Identifying “PatientPatient”” DiagnosisDiagnosis PrognosisPrognosis TherapyTherapy

Diagnosing patients with particular traits

Identifying responders for targeted therapies (essentially highly tailored therapies)

Identifying who have an alternate prognosis (perhaps needing additional therapy)

Optimize dosing regimen for patient subpopulation(s) to achieve optimal benefit/risk Tailoring “Tailoring “DoseDose””

Identify time to intervene during disease progression, time to complete therapy, or time to alter treatment regimen

Tailoring “Tailoring “TimeTime””


Why do we think genetics will play?


Pinpoint the ‘right’ biomarker

DNA ----ACGTGGGCAGTAGACTCAT---- ----TGCACCCGTCATCTGAGTA----

Protein----Thr Trp Ala Val Asp Ser ----

RNA ----ACGUGGGCAGUAGACUCAU----

Large Scale ‘Fishing’

Whole Genome Scan

Medium Scale ‘Confirmation’Many Different

Groups

Small Scale ‘Validated’Clinical Trial

Support

Large Scale Fishing

DNA – 100K to 2x106 SNPsChip BasedElectrophoresis

RNA – 30K+Chip Based -oligosSlide Based - cDNAs

Protein – 1K upwardMass Spec

Med. Scale Confirm.

DNA –2K to 30KChip BasedElectrophoresisPCR Based

RNA – 30 to 1KChip Based -oligosSlide Based – cDNAsRT-PCR Based

Protein – 50 to 500Mass SpecLuminex Type

Small Scale Valid.

DNA – 1 to 25PCR BasedElectrophoresis

RNA – 1 to 25RT-PCR Based

Protein – 1 to 30ImmunoAssay

Clinically Utilized PGx Tests

Shrinking Clinical PGx Funnel

Hercept Test

Philly Chromosome

VariationY

Resp

on

se

N

Y

N

VariationY

Resp

on

se

N

YNvs.

≥ 3

Predictive

Situation SpecificOnc vs. Neuroscience

c-kitTPMT HLA

Disease vs.Response

Interesting Genetic Associations

ProspectiveClinical Proof

Genetic PolymorphWith Rel. Risk

GeneticVariant

Freq

Genetic PolymorphWith Good

Sens. & Spec

Apo E, CETP

5 - LO

Variants in Growth Genes

DMET

Needs Examples

OncotypeDxUGT1A1

EGFrCYP2C9/VKORC1

Tissue of Origin


Hurdles to applying -omics to medicine Strategic

Gearing the infrastructure Obtaining the talent

Technologic Information overload Lack of biologic understanding Platform challenges

Regulatory Understand how to apply technology

Implementation Clinician education, understanding, and acceptance

X

??

DMET PlusDrugMetabolismEnzymes &Transporters

An Example of Applying New Technologies to the Clinical Marketplace


The Genesis of DMET:

March of 2004: Collaboration initiated between Lilly, ParAllele, and Affymetrix

The goal for Eli Lilly was to develop a clinical solution for better understanding the genetic components behind metabolism and transport:

Better ability to understand PK outliers in early phase trials

Build a database for selective recruitment of healthy volunteers with a defined genotype

Work with the FDA in an attempt to decrease the number of biopharm (DDI) trials needed for future NDAs

June 2006 was the inception of a working assay for clinical trials Dec 2007 first NDA was submitted the FDA


Pinpoint the ‘right’ biomarker

Large Scale ‘Fishing’

Whole Genome Scan

Medium Scale ‘Confirmation’Many Different

Groups

Small Scale ‘Validated’Clinical Trial

Support

Large Scale Fishing

DNA – 100K to 2x106 SNPsChip BasedElectrophoresis

RNA – 30K+Chip Based -oligosSlide Based - cDNAs

Protein – 1K upwardMass Spec

Med. Scale Confirm.

DNA –2K to 30KMIP BasedTrue Materials

RNA – 30 to 1KPanomics Expression

Protein – 50 to 500Mass SpecLuminex Type

Small Scale Valid.

DNA – 1 to 25MIP BasedTrue Materials

RNA – 1 to 25Panomics Expression

Protein – 1 to 30ImmunoAssay

Using existing Affymetrix technology


Setting the stage for adoption of genetic analysis tools for use in personalized medicine

Risks associated with taking

popular heart disease medication

Plavix (Clopidogrel)

Paper published in New England Journal of Medicine

Conclusion: Patients taking Clopidogrel and

who were carriers of a certain gene variation had higher rates of heart attack, death and other cardiac-related events

Two additional independent studies recently published in NEJM and Lancet show similar PGx associations.


No Relationship between Genetics and PK/PD for Prasugrel, Significant Effect for Clopidogrel

Pharmacokinetics

Integrated Genetic Analyses in Healthy Subjects

Pharmacodynamics


PGx associated clinical outcomes of 1459 acute coronary syndrome patients treated with clopidogrel were significant

1477 Patients were randomly assigned Plavix treatment with 98.8% being genotyped

CYP2C19 variant allele (1) frequency in treated population was 27.1%.

Primary efficacy outcome: composite of death from cardiovascular causes, MI and stroke.

395 variant carrier patients had a 1.5 fold higher risk of death vs non-carriers.

1389 Rx patients had stents implanted with a secondary endpoint of stent thrombosis.

375 2C19 variant patients had a 3 fold increase in risk of thrombosis.

Two additional independent studies recently published in NEJM and Lancet show similar PGx associations.


Identification ofpotential biomarkeror drug targetDNA markerRNA expression levelProtein

DNA orRNA Samples

Plasma orSerum Samples

Use of markerin prospectiveclinical trials

Patient Stratification

Literature

Cell Lines

Retrospectiveconfirmationon clinicalsamples

Tissues

A.B.

C. D.

Pharmacogenomics in drug development

Development of a biomarker

Patient Samples are the Key

Biomarker: A physiological response or laboratory test that occurs in association with a pathological process and that has putative diagnostic and/or prognostic utility


What we Must Do to Enable -omics Impact

Align focus on what can be done and where genetics is likely to work

Analyze, Integrate and Learn from data Enable the field of Molecular Epidemiology

Enhance our biologic understanding of genetic influence of complex traits and produce more examples

Develop and validate technologies for clinical use IT Infrastructure Standards & Controls

Educate the medical infrastructure Engage patients and third party payers


How Do We Enable -omics Uptake?

We cannot maintain silos

We must enable certain, common functions Sample banking

Clinical trials

We must look to the regulators for direction Standards

Controls

Critical Path Initiative

FDA

Industry

Academics

Labs

DiagnosticCompanies


The Biotech/GenomicsRevolution:

Increase the Benefit:Risk Ratio

Develop clinical aids for:DiagnosisPrognosis

DosingTherapy Decisions

Biomarker Based PGx Strategies Rick Hockett, MD Chief Medical Officer Affymetrix.

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