Translation of Genetic

Technologies into the Clinical

Space: DMET

Rick Hockett, MD

Chief Medical Officer

2Affymetrix Confidential

One size fits all

Goal: Improve individual patient outcomes and health outcome predictability through tailoring diagnosis, prognosis, 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 diagnosis, right prognosis, or the right

drug at the right dose at the right time.”

Tailoring (e.g. oncology products

comprising drug and companion diagnostic)

Targeted Therapy

Tailoring is Broader Than Pharmacogenomics

Measure something in a patient to learn how to treat them better

Why Are Biomarkers So Important?

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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 TrialSupport

Large Scale Fishing

DNA – 100K to 2x106 SNPs

Chip Based

Electrophoresis

RNA – 30K+

Chip Based -oligos

Slide Based - cDNAs

Protein – 1K upward

Mass Spec

Med. Scale Confirm.

DNA –2K to 30K

Chip Based

Electrophoresis

PCR Based

RNA – 30 to 1K

Chip Based -oligos

Slide Based – cDNAs

RT-PCR Based

Protein – 50 to 500

Mass Spec

Luminex Type

Small Scale Valid.

DNA – 1 to 25

PCR Based

Electrophoresis

RNA – 1 to 25

RT-PCR Based

Protein – 1 to 30

ImmunoAssay

Clinically Utilized

PGx Tests

Shrinking Clinical PGx Funnel

Hercept Test

Ph Chromosome

VariationY

Resp

on

se

N

Y

N

VariationY

Resp

on

se

N

YNvs.

≥ 3

Predictive

Situation SpecificOnc vs. Neuroscience

c-kit

TPMTHLA

Disease vs.

ResponseInteresting Genetic Associations

Prospective

Clinical Proof

Genetic Polymorph

With Rel. Risk

Genetic

Variant

Freq

Genetic Polymorph

With Good

Sens. & Spec

Apo E, CETP

5 - LO

Variants in

Growth Genes

DMET

Needs Examples

OncotypeDxUGT1A1

EGFr

CYP2C9/VKORC1Tissue of Origin CYP2C19

k-RAS

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Hurdles to applying -omics to medicine

Strategic Gearing the infrastructure

Technologic Information overload

Lack of biologic understanding

Platform challenges

Regulatory Understand how to apply technology

Implementation Clinician education, understanding, and acceptance

X

??

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We Need to Develop Good Clinical Tools

Not all tools can be readily moved to the clinical

arena

Many technologies generate more data than

is clinically relevant

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Discovery Experiments

• Hypothesis Generation

• Variant Identification

• Scientific Validation

Clinical Research

• Hypothesis Testing

• Study Design

• Clinical Validation

• Patient Selection

Clinical Diagnostic

• Patient Selection

• Drug Selection

• Dose Modification

• Prognosis

Changing Arenas of Importance

Biologic Relevance

Determined

Clinical Relevance

Determined

Regulatory Scrutiny

DMET PlusDrug

Metabolism

Enzymes &

Transporters

A Comprehensive Tool for Assessing

Drug Bioavailability

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Rationale for pursuing DMET

Result of Early Phase strategy

Drug Activity Biomarkers Genetic Polymorphisms Predicting Drug Metabolism

DNA Variations Predicting Drug Response

DNA Variations Predicting Adverse Events

Main objectives of this strategy were to set out a plan to collect samples to enable genetic studies and to generate more information on metabolism

Benefits of a better understanding of the genetics behind metabolism :

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

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Comprehensive Assessment of DMET

Phase I Metabolism

Drug Phase I Metabolite Phase II Metabolite

Oxidation

Reduction

Hydrolysis

Conjugation with:Glucuronic acid Amino acids

Glutathione Sulfuric acid

Acetic acid

Cytochrome P450 Genes

CYP2D6 CYP2A6

CYP2C8/9 CYP2C19

CYP1A2 CYP3A4/5

CYP2E1

N-acetyl transferases

Glutathione-S transferases

Methyl transferases

Phase II Metabolism

Two Sets of Genes:

Validated set – known clinical correlation

~40 Genes – ~300 Variants

Additional genes – suspected clinical utility

~200 Genes – ~1,600 Variants

Transporter Genes

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DME/T PLUS - Benefits

Fully validated system for core set

FDA accepts use in support of clinical trials

Analyze 99.9% of known functional variants

CYP enzymes

Non-CYP enzymes

Transporters

Common platform for all genes

Ability to be flexible and react to additional data

Add new variants and/or genes to list

Move variants on and off ‘must have’ list

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DMET: the ‘right’ biomarker

Large Scale ‘Fishing’

Whole Genome Scan

Medium Scale ‘Confirmation’Many Different

Groups

Small Scale ‘Validated’

Clinical TrialSupport

Large Scale Fishing

DNA – 100K to 2x106 SNPs

RNA – 30K+

Med. Scale Confirm.

DNA –2K to 30K

DMET Plus

RNA – 30 to 1K

Small Scale Clinical Res.

DNA – 1 to 25

DMET Plus

RNA – 1 to 25

DMET Plus has only causative variants

Once causative variants are

known, ethnicity becomes

irrelevant.

DMET PlusDrug

Metabolism

Enzymes &

Transporters

Clinical relevance of a purpose-built

comprehensive targeted panel

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S

N

Cl

O OCH3

S

N

Cl

O OCH3

O

HS

N

Cl

O OCH3

HOOC

Clopidogrel

CYP2C19

CYP1A2

CYP2B6

Esterases

~85%

Inactive

Metabolites

S

N

F

O

O

O

S

N

F

O

O

HS

N

F

O

HOOC

Esterases

Prasugrel

CYP3A4/5

CYP2C19

CYP2C9

CYP2B6

CYP3A4/5

CYP2B6

CYP2C19

CYP2C9

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

Pharmacokinetics

Integrated Genetic Analyses in Healthy Subjects

Pharmacodynamics

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CYP2C19 Null Variant Frequency

Those patients who have at least one copy of a

reduced-function variant of CYP2C19

Caucasians - 25 – 30%

Africans - 40%

Asians - 60%

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Research Applications

Pre-clinical Research

Enzyme activity analysis for in vitro toxicology studies

Early Clinical Research

Enzyme activity analysis for ADME work

Early Understanding of PK outliers

Broad Based Clinical Research

Comprehensive metabolic profiles for long term clinical programs

(PGENI or PharmGKB)

Study Benefit/Risk Ratio

Building a Database of Known Genotypes

Support industry sponsored trials

Apply DMET for primary discovery of new metabolic associations and to validate involvement of metabolic pathways in current medicines.

Regulatory ChallengesHow to get a complex device

designation on a molecular device

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Research Use

Only

Typical Technology Development Path - US

Laboratory

Derived Test

In vitro

Diagnostic

Regulations for LDTs Enforced by CLIA Guidelines for Labs Expert Groups

Involved Manufacturers

Cannot Promote

Regulations for IVDs Enforced by FDA Guidelines for

Manufacturers Safety is Paramount Label drives Marketing

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FDA: The process and challenges

Complexity must be decided

Drives regulatory path

Class II – 510(k)

Class III – PMA

Number of Analytes Available is Increasing

IVDMIA

Intended Use Must be Stated

Can drive class designation

Sets clinical strategy

Will drive labeling of device

Clinical Validation

Sets the FDA process apart

Must define both safety and efficacy

To date, a diagnostic intended use has been linked to a specific clinical diagnosis

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Working with the FDA

FDA at Their Computer

Contrary to some popular beliefs, the FDA is not

waiting to pounce

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Genetic Biomarkers in Drug Labels

Drug Biomarker Drug Biomarker

Required Maraviroc CCR5 receptor Informational Imatinib c-Kit expression

Cetuximab - colon EGFr expression Voriconazole CYP2C19 variants

6 Trastuzumab Her2/neu overexpression Celecoxib CYP2C9 variants

Lenalidomide del(5)q Atomoxetine CYP2D6 variants

Panitumumab KRAS Variants Fluoxetine CYP2D6 Status

Dasatinib Ph1 chromosome Lenalidomide Chromosome 5q del

Recommended Warfarin CYP2C9 variants Capecitabine DPD variants

Warfarin Protein C deficiency Erlotinib EGFr expression

Warfarin VKORC1 variants 14 Cetuximab - Head EGFr expression

Atorvastatin LDL variants Primaquine G6PD variants

11 Rasburicase G6PD variants Rifampin/isoniazid NAT variants

Carbemazepine HLA-B1502 allele Busulfan Ph1 chromosome

Abacavir HLA-B5701 allele Nilotinib UGT1A1 vriants

Azathioprine TPMT variants Prasugrel CYP2C19 variants

Irinotecan UGT1A1 variants

Clopidogrel CYP2C19 variants

Valproic Acid Urea cycle disorder deficiency

Tretinoin PML/RAR a gene expression

FDA Current Drug Labels Containing Genetic Biomarkers

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Summary

Utility of genetic tests in a clinical setting is to

define subgroups with a better risk:benefit profile

The translation of genetic technologies to clinical

problem solving is in its infancy

Device manufacturers, health care professionals,

and third party payers must partner with the

regulatory agencies to enable the best use of

genetic technologies