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Clinical Pharmacogenomics David A Flockhart MD, PhD Chief, Division of Clinical Pharmacology Professor of Medicine, Genetics and Pharmacology Indiana University School of Medicine December 6, 2012
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Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

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Page 1: Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

Clinical Pharmacogenomics David A Flockhart MD, PhD

Chief, Division of Clinical Pharmacology

Professor of Medicine, Genetics and Pharmacology

Indiana University School of Medicine

December 6, 2012

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Outline

Pharmacogenomics and rationale for the use of pharmacogenomics in the

clinic

Basic principals for the value of pharmacogenomics

The unmet disease burden and the opportunity.

Pharmacogenomic opportunities and their clinical utility in cancer,

cardiovascular disease and psychiatry

Avoiding pitfalls and optimizing clinician uptake

Page 3: Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

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Outline Pharmacogenomics and rationale for the use of pharmacogenomics in the

clinic

Basic principals for the value of pharmacogenomics

The unmet disease burden and the opportunity.

Pharmacogenomic opportunities and their clinical utility in cancer,

cardiovascular disease and psychiatry

Avoiding pitfalls and optimizing clinician uptake

Page 4: Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

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The Genetic Evolution

(Arrow pointing down to the left)

One Gene, One SNP

One Gene, Multiple Variants

Multiple Genes, Multiple Variants

Pathways, Multiple Variants

All Variants in the Genome

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SNP Variability in The Human Genome

December 2011

2.85 billion base pairs

~22,000 genes

1.7% of the genome codes for protein

3.3% of the genome is as conserved as the 1.7% that codes for

protein

On average 1 SNP/1.2kb

10 - 15 million SNPs that occur at > 1% frequency

~450,000 SNPs in Multiply Conserved Regions

Copy number variations exist in 5-7.5% of the germline genome

Most tumor DNA sequence is identical to that of the host

4-5% of the genome is in areas with high copy number variation

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Genome Wide Association as a Tool for Personalized

Medicine: Effect Sizes are generally low

Graph of diseases/traits studied

Fletcher RL, Flockhart DA, 2011

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Pharmacogenomic Genome-Wide Association

Studies

Warfarin : CYP2C9

Clopidogrel: CYP2C19

Simvastatin: SLC101

Exemestane and Anastrozole:

Tamoxifen :

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Drugs and Their Available Pharmacogenetic Tests

(As of September, 2011. most are monogenic tests)

Abacavir HLA *B5701

Alcoho l ADH and ALDH

Interferon α I l28B

Clopidogrel CYP2C19

Venlafaxine CYP2D6

Codeine and hydrocodone CYP2D6

Tamoxifen CYP2D6

Methadone CYP2B6

Vincristine CYP3A5

Tacrolimus CYP3A5

Cyclophosphamide CYP2B6

Metformin OATP3

Imatinib BCR-ABL

5-Fluorouracil DPYD-TYMS

Clozapine 2 SNPs in HLA-DQB1

Irinotecan UGT1A1

Azathioprine and Mercaptopurine TPMT

Warfarin CYP2C9 and VKCoR

Carbamazepine HLA-B* 1502

QT-prolonging Drugs Familion™

Adapted from Flockhart DA et al. Clin Pharmacol Ther. 2009

Jul;86(1):109-13

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Many are Available But Which Tests Are

Valuable?

Analytical Validity

Laboratory Integrity

Clinical Validity

Accurate biomarker prediction

Clinical Utility

Would it change what you do?

i.e. change a drug or dose

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Principal Factors that Influence the Clinical Value of

Any Pharmacogenomic Test

- Is there large variance is treatment outcomes?

- Are alternative therapies available?

- What’s the current predictive value?

Graph of Clinical value of a pharmacogenetic test and current

clinical ability to predict response

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Principles for Valuable Pharmacogenetic Tests

1. Large variance in treatment outcomes

2. Alternative therapies are available

3. Current predictive ability is low

4. Significant clinical consequences

5. Economically viable

Graph of clinical value of pharmagogenetic test

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Determining Whether Variability is Present:

The Simple Polymorphic Distribution

Chart showing population frequency and activity (Phenotype)

and antimode.

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The Value of Normit Distribution Plots: (an example is shown)

Population Distribution of CYP2C19 phenotype (an example is

shown)

Flockhart et al: Clin Pharmacol Ther

1995;57:662-669

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Skewed Distribution

The Mean is Not the Same as the Mode

A chart is shown indicating the population frequency and

activity (Phenotype).

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Example 1 of a Skewed Distribution: Heterogeneity in

response to Inhaled Corticosteroids

A bar chart is shown indicating the Patients, %, and the % change

in FEV, from baseline for the adult study, CAMP and ACRN.

Weiss ST et al. Hum Molec Genetics 2004; 13:1353-11359

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The Problem with Mean Response Data: Heterogeneity in

Response to Medicines in Clinical Trials

Shows the Frequency of various responses in the RCT treated

population

Evan B, Flockhart DA et al. (2010)

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Consequences of Variable Treatment Response

-- The Unmet Medical Need

1. Cancer: Poor outcomes due to poor therapeutic efficacy and

the development of resistance

2. Cardiovascular Disease: High morbidity and mortality due to

persistent disease progression

3. Psychiatry: Inadequate treatment due to side effects, poor

compliance and lack of efficacy biomarkers

Page 18: Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

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Consequences of Variable Treatment Response

-- The Unmet Medical Need

1. Cancer: Poor outcomes due to poor therapeutic efficacy and

the development of resistance

2. Cardiovascular Disease: High morbidity and mortality due to

persistent disease progression

3. Psychiatry: Inadequate treatment due to side effects, poor

compliance and lack of efficacy biomarkers

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Pharmacogenomic Opportunities in Cancer

- Tamoxifen for Breast Cancer as an example

– Therapeutic ceiling in endocrine therapy

– Imperfect prediction of response

– Roadmap for prognostic tests

- ER, PR, HER2

- Precedent for multiplex arrays

– Highly organized tissue collection (tumor)

– Multiple retrospective trials to study

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Variability in Tamoxifen Therapy Outcome

Graph Illustration

EBCTCG: Lancet 365:1687, 2005

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Existing Biomarkers of Endocrine Treatment Response

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A Mechanistic Basis for a Tamoxifen Biomarker:

CYP2D6 Generates the Active Tamoxifen Metabolites

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CYP2D6 Pharmacogenetics

Frequency histogram for Debrisoquine/4-Hydroxydebrisoquine

metabolic ratio in UMs, EMs, and PMs.

Dahl ML et al. J Pharmacol Exp Ther. 1995 Jul;274(1):516-20

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Tamoxifen CYP2D6 Pharmacogenetic Testing

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PharmGKB at a Tool:

Irinotecan Pathway

Illustration of this pathway.

www.pharmgkb.org

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UGT1A1 TA Repeat Genotype Altered

Irinotecan Neutropenia and Activity in Colon Cancer

Graphic illustration

McLeod H. et al, 2003

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Irinotecan Pharmacogenomic Testing

- Analytical Validity OK

- Clinical Validity OK

- Clinical Utility unclear because the tests value is limited to

specific dosing regimes

- Not in widespread use

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Thiopurine Methyl Transferase

- Cause of Highly Variable Toxicity : Granulocytopenia with

Mercaptopurines:Azathioprine and 6-Mercaptopurine Used

in:

- Acute Lymphocytic Leukemia in Children

- Inflammatory Bowel Disease in Adults

Homozygous mutants are 0.2% of Caucasian Populations

Heterozygotes are ~ 10%

Homozygous wild type is 90% – Metabolism of Azathioprine

– 6-Mercaptopurine

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Variability in Thiopurine Methyl Transferase

Frequency histogram for TPMT activity in 298 unrelated adults.

From: Weinshilboum et al. JPET;222:174-81. 1982

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TPMT Pharmacogenomic Testing

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Biotech Pharmacogenomics

Bevacizumab (Avastin™)

Interferon and IL 28b

Erlotinib and K-Ras

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Bevacizumab in Breast Cancer

Improvement in PFS/ORR did not translate into OS

benefit

Chart of progression-free survival.

ORR

(measurable disease)

49.2% vs. 25.2%

P<0.001

Miller et al. NEJM 357:2666;2007

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Detailed chart from Miller et al. NEJM 357:2666;

2007 with the following sentence across it.

We need something better!

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Pharmacogenetic Biomarkers VEGF -2578 AA & -

1154 AA genotypes in combination arm

outperformed control

Graph and illustration

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Bevacizumab Pharmacogenomic Testing

- Analytical Validity OK

- Clinical Validity OK

- Clinical Utility Requires Validation

in Other Trials

Could a Pharmacogenomics Approach bring Avastin™ Back?

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Current Pharmacogenomic Testing in Cancer

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Consequences of Variable Treatment Response

-- The Unmet Medical Need

1. Cancer: Poor outcomes due to poor therapeutic efficacy and

the development of resistance

2. Cardiovascular Disease: High morbidity and mortality due to

persistent disease progression

3. Psychiatry: Inadequate treatment due to side effects, poor

compliance and lack of efficacy biomarkers

Page 38: Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

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Pharmacogenomics Opportunities in

Cardiovascular Disease

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CYP2C19 generates the active metabolite of

Clopidogrel (Plavix™)

Simon et al. NEJM December 23rd, 2008

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Comparison of prasugrel 60 mg and clopidogrel 600

mg loading dose exposure of active metabolite by

CYP2C19 genetic classification. Box represents

median, 25th, and 75th percentiles and whiskers

represent the most extreme values within 1.5 times

inter-quartile range of the box. AUC, area under the

concentration–time curve; EM, extensive

metabolizer; RM, reduced metabolizer.

Eur Heart J (2009) 30 (14): 1744-1752

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Carriers of a CYP2C19 Genetic Variant

Experienced More Cardiovascular Events

Graph

Mega JL et al, NEJM, April, 2009

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Data

Simon T et al: Clinical Pharmacology & Therapeutics

(2011) 90 4, 561–567

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Pharmacogenomics Opportunities in

Cardiovascular Disease

• Warfarin for Deep Venous Thrombosis and Atrial Fibrillation

as an example

– A large population of patients treated

– Great variability in both efficacy and bleeding

outcomes

– Samples from many large prospective trials available

– International Normalized Ratio available as an existing

biomarker

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VKORC1 Haplotype and CYP2C9 Genotype changed

Warfarin Dose Primary cohort: UW (N=185);

Replication cohort: Wash U (N=368).

All participants were Caucasian.

Rieder et al. N. Eng J. Med 2005;352: 2285-2293[

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Pharmacogenomics Opportunities in

Cardiovascular Disease :Warfarin

• Analytical Validity OK

• Clinical Validity OK

• Clinical Utility

– limited because a viable alternative (INR) is available

in many, but not all practice settings.

• Not in widespread use.

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2SNPs: 10 possible hapoltypes

Ying-Hong Wang PhD,

Indiana University School of Medicine

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Observed b1AR Haplotypes in Caucasians and

African American Women (WISE study)

Terra et al. Clin. Pharmacol. Ther. 71:70 (2002)

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Of 10 theoretical diplotypes, only 4 were present in

the study population

Chart of haplotypes and diplotypes.

Ying-Hong Wang PhD, Indiana University School of Medicine

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Diplotype predicted Beta-blocker effect: Single SNP

analysis Would Miss It.

Graph of change in diastolic blood pressure as a function of

diplotype.

Johnson et al. Clin Pharmacol & Ther. 2003,74:44-52.

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Consequences of Variable Treatment Response

-- The Unmet Medical Need

1. Cancer: Poor outcomes due to poor therapeutic efficacy and

the development of resistance

2. Cardiovascular Disease: High morbidity and mortality due to

persistent disease progression

3. Psychiatry: Inadequate treatment due to side effects, poor

compliance and lack of efficacy biomarkers

Page 51: Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

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Frequency of Unresolved Symptoms Following

Initial SSRI Treatment

Graph

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Pharmacogenomic Opportunities in Psychiatry

• Venlafaxine for Depression as an example

– Blockbuster drug for a common disease

– Variability in efficacy and toxicity

– Prolonged time before efficacy is evident

– Need for biomarkers of treatment response

– Exclusive Metabolism by CYP2D6

– Large prospective trials with germline DNA available

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Venlafaxine (VEN) Is Metabolized To

O-Demethylated-Venlafaxine (ODV) By CYP2D6

Br J Clin Pharmacol. 1996;41(2):149-156

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CYP2D6 Genotype Associated with Venlafaxine

Efficacy

Lobello KW et al. J Clin Psychiatry 71:11, Nov. 2010

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Promising pharmacogenomic opportunities exist in

cancer, cardiovascular disease and psychiatry.

How to implement?

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Pharmacogenomic Pitfalls to Avoid

1. Inadequate testing of genetic variation

2. Inadequate analysis of known genetic variants

3. Poorly defined biomarker phenotypes

4. Inappropriate attempts to validate associations in

the wrong populations

5. Ineffective communication with the clinical

community

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Home Page of the Human Cytochrome P450 (CYP)

Allele Nomenclature Committee

www.cypallesles.ki.se

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Graph

Page 59: Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

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Iterative Statistical Analyses

From Discovery To Clinic Validation

Illustrations

Lang Li and David A. Flockhart, 2010

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Outline

Pharmacogenomics and rationale for the use of

pharmacogenomics in the clinic

Basic principals for the value of pharmacogenomics

The unmet disease burden and the opportunity.

Pharmacogenomic opportunities in cancer,

cardiovascular disease and psychiatry

Avoiding pitfalls and optimizing clinician uptake

Page 61: Pharmacogenomics - PRINCIPLES OF CLINICAL PHARMACOLOGY

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Optimizing Clinical Uptake

1. Simple and user-friendly informatic approaches

2. Education and raising awareness

3. Team approaches involving the whole

therapeutic alliance

4. Prospective trials of pharmacogenomic-guided

therapy versus standard-of-care

5. “Act local, think global”

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A Tool to Aid Clinical Implementation

Graph

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Optimizing Clinical Uptake

1. Simple and user-friendly informatic approaches

2. Education and raising awareness

3. Team approaches involving the whole

therapeutic alliance

4. Prospective trials of pharmacogenomic-guided

therapy versus standard-of-care

5. “Act local, think global”