1 pharmacogenomics: changing the paradigm aidan power md clinical pharmacogenomics pfizer global...

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Pharmacogenomics: Changing The Paradigm

Aidan Power MD

Clinical Pharmacogenomics

Pfizer Global Research and Development

2Kitasato-Harvard Symposium Oct2003

PresentationPresentation

Why do genetics/pharmacogenomics? Types of studies Uses in drug development

Drug Discovery Drug Development

Applications of gene expression The future?


The route to a new medicine…The route to a new medicine…

DiscoveryDiscovery

Exploratory DevelopmentExploratory Development

Full Full DevelopmentDevelopment

RegistrationRegistration


…is a long one…is a long one

IdeaIdea Marketed Marketed DrugDrug

YearsYears

11-15 Years11-15 Years

DiscoveryDiscovery Exploratory DevelopmentExploratory Development Full DevelopmentFull Development

Phase I Phase II Phase III

00 151555 1010

Patent life 20 yearsPatent life 20 years

Phase IV


…and an expensive one!…and an expensive one!

SGPSGP ABTABT AHPAHP BMYBMY LLYLLY MRKMRK JNJJNJ

934934

PHAPHA GSKGSK

1,1161,116

1,4021,4021,4991,499 1,6451,645 1,7401,740

1,9161,916

2,4872,4872,6602,660

3,3323,332

1,9551,955

AZNAZN

2,2812,281

AVEAVE

$ Millions spent in 9 months in 2001

It costs >$800 million to get a drug to market


Pharmacogenomics can help!Pharmacogenomics can help!

Creating opportunities to increase the value of the drugs we develop using genetics Obtain greater understanding of disease

Predict disease severity, onset, progression Identify genetic subtypes of disease Aid in discovery of new drug targets

Distinguish subgroups of patients who respond differently to drug treatment

Aid interpretation of clinical study results


0% 50% 100%

HDL level

RheumatoidArthritis

Schizophrenia

Huntington'sDisease

Genes

Environment

Heritability: The proportion of the disease that is due to genetic factors

We Are Studying Genetic Diseases…We Are Studying Genetic Diseases…


Gene 1

Gene 2

Environment

Few genes and environmental factors each contributing a large

risk.

Gene 1

Environment

Gene 5

Gene 4

Gene 2

Gene 3

Many genes and environmental factors each contributing a small

risk.

Complex Phenotypes – What Can We Expect? Complex Phenotypes – What Can We Expect?


Pharmacogenomics at PfizerPharmacogenomics at Pfizer

The study of genome-derived data, including human genetic variation, RNA and protein

expression differences, to predict drug response in individual patients or groups of patients.

Pharmacogenomics includes Pharmacogenetics


Polymorphism: A genetic variation that is observed at a

frequency of >1% in a population

Types of Polymorphisms Single Nucleotide

Polymorphism (SNP): GAATTTAAGGAATTCAAG

Simple Sequence Length Polymorphism (SSLP): NCACACACAN

NCACACACACACACANNCACACACACACAN

Insertion/Deletion: GAAATTCCAAGGAAA[ ]CCAAG

Markers of Genetic VariationMarkers of Genetic Variation


DiseaseResponder

ControlNon-responder

Allele 1 Allele 2

Marker A is associated with Phenotype

Marker A:

Allele 1 =

Allele 2 =

Human Genetic Association Study DesignHuman Genetic Association Study Design


Disease PopulationN=500

Matched Control PopulationN=500

122~3,000,000 common SNPs across genome

• Representing every gene

P v

alu

e

1 22

Informatics to ID gene(s) mapped to associated SNP

Regions of association

Chromosomal Location

Whole Genome AssociationsWhole Genome Associations


Applying PharmacogenomicsApplying Pharmacogenomics

.

DISEASE GENETICS

TARGETVARIABILITY

SELECTINGRESPONDERS

PHARMACO-GENETICS

Discovery Development

Choosing the Best Targets

Better Understanding of Our Targets

Improving Early

Decision Making

Predicting Efficacy and

Safety


Target PrioritisationTarget Prioritisation

HDL modulation– A significant market

So many targets– Which is the best?

Locus specific genetic association study Candidate genes screened for polymorphism Correlate genotypes with HDL levels Increase CIR in the target


Cholesteryl Ester Transfer ProteinCholesteryl Ester Transfer Protein

• Spans 22 kb on human chromosome 16

• Several polymorphisms identified

• Implicated in modulation of HDL levels

• SNPs genotyped in 110 healthy subjects


CETP Association Study (1)CETP Association Study (1)

Distance in bases from transcription start

R-s

qu

are

fro

m A

NO

VA

0 5000 10000 15000 20000

0.0

0.0

50

.10

0.1

50

.20

CETP massHDL

Association of CETP markers and baseline phenotype

VNTR

-629/promoter


Clinical Study PopulationClinical Study Population

ACCESS data set samples available 54-week Phase IIIb open label assessment of

the safety and efficacy of Atorvastatin –3916 patients randomised into 5 treatment

groups Subjects with coronary heart disease (CHD)

and/or CHD risk factors 4 pretreatment visits, data on blood pressure,

lipids etc including HDL level


CETP Association Study (2)CETP Association Study (2)

Genetic variation in CETP Associated with protective HDL levels Increasing CIR for target Additional information obtained

– Linkage disequilbruim– Ethinic diversity

Studies in larger populations required


Challenges of Studying DepressionChallenges of Studying Depression

Complex multi-factorial polygenic trait Genetic heterogeniety Phenotype is variable & subjective 30-50% non responders to drug Placebo response rates are high (50%) Many trials “fail”


SSRIs SSRIs

Selective Serotonin Reuptake Inhibitors Impacted on treatment of depression Improved tolerability and efficacy

BUT– Not all patients benefit

The challenge for new compounds– Increased efficacy– Reduction in adverse events– Differentiation


Target Variation – 5HTTTarget Variation – 5HTT

Variation in promoter sequence 44bp insertion/deletion (L and S alleles)

ShortSLC6A4 expression

(484 bp)

Long SLC6A4 expression

(528bp)

Long/Long Short/Short


Association With Drug Response?Association With Drug Response?


5HTT and Sertraline Response5HTT and Sertraline Response

Does genotype influence time to response Study R-0552

– 8 week, double-blind, placebo-controlled study of sertraline in elderly depressed outpatients with DSM-IV major depression 66 sites within the US Anonymized DNA samples collected to test for genotype

effect on time-to-response to sertraline 4-14 day washout period prior to randomization Age >60 HAM-D 18 HAM-D and CGI-I measures of response Predominantly Caucasian (95% )


Case control evaluationCase control evaluation

Responders defined as: HAM-D

50% reduction in HAM-D from baseline

CGI-I Individual with a score of 1 or 2

Response defined at each time point post-baseline and evaluated for a significant difference in response between the LL and SL/SS groups.

– Direct association testing a functional polymorphism for effect on response.


CGI Response by GenotypeCGI Response by Genotype

02

04

06

08

01

00

1

pe

rce

nt

64

30

2

66

26

4

63

24

6

60

21

8

67

31

SS or SL genotypeLL genotype

Sertraline group: Percentage of CGI responders by week and 5HTTLPR genotype

study week

P=.01 P=.01

• L/L genotypes respond more rapidly to Sertraline


CGI Response by GenotypeCGI Response by Genotype

02

04

06

08

01

00

1

pe

rce

nt

83 23

2

8122

4

7822

6

74

21

8

81

23

SS or SL genotypeLL genotype

Placebo group: Percentage of CGI responders by week and 5HTTLPR genotype

study week

• Response time to placebo not significant


Clinical Impact of PG EffectClinical Impact of PG Effect

Enhancing study population to increase the probability of earlier response

– Enrich LL in POC study to provide maximum probability of successful phase II trial.

– POC study exclusively in LL group to make Go/No Go decision on test drug

– Smaller trials?

Differentiation over comparator based on response time– Design study with equal representation of alleles across each

test arm

Population Stratification– Do S-allele carriers have a distinct disease?


Human Genetics• SNPs• Haplotypes• Sequencing

Expression Profiling• Specific transcript

levels• Total RNA profiling

Proteomics• Specific biochemical

markers• Protein profiling

Phenotype• Drug response

• Disease

Prediction

PharmacogenomicsPharmacogenomics


Genetics of Cancer Accumulation of

molecular events– LOH– Oncogene activation– Tumor suppressor

inactivation– cytogenetic alterations

Accumulation of molecular events

Tumor Phenotype

Phenotype of Cancer Stages of phenotype

– dysplasia/premalignant– differentiation– invasive– metastases– Outcomes– Response

Cancer: a Model for PG ApproachesCancer: a Model for PG Approaches


Isolate DNAIsolate RNA

Fluorescent label

OligonucleotideHybridization

Can these approaches provide clues into the state and future of tumor pathogenesis?

Amplify region of interest

Genomic Technologies: SomaticGenomic Technologies: Somatic


Somatic Expression Signals Somatic Expression Signals

Expression-based signatureGenomic profile vs IPI

Ash et al. Distinct types of diffuse B-cell lymphoma identified by gene expression profiles. Nature 2000, 403:503-11


A Gene-Expression Signature as a Predictor of Survival in Breast Cancer. van de Vijver etal NEJM 2002 347:1999-2009

Working with Agilent to develop microarray based diagnostic

Breast Cancer Profiling for PrognosisBreast Cancer Profiling for Prognosis


Towards Precision PrescribingTowards Precision Prescribing

Identifying molecular subtypes of disease Understanding genetic basis of response to

treatment Integrating genetics with other technologies

– Transcriptomics, Proteomics, Metabonomics, Imaging, Pop. PK/PD modelling

A combined approach to diagnosis & prescription


1990s 2000s Beyond

Linkage studies

Sequencing

Candidate gene association studies

Large scale SNP detection

Whole genome association studies

Regulatory scrutiny

Pharmacogenetics

Personalized sequencing

Precision therapies

Pharmacogenomic diagnostics

‘omics’ integration

What the future holds…What the future holds…


AcknowledgementsAcknowledgements

John Thompson

Patrice Milos

Maruja Lira

Suzin McElroy

Albert Seymour

Katey Durham

Hakan Sakul

1 pharmacogenomics: changing the paradigm aidan power md clinical pharmacogenomics pfizer global...

Documents

development slide

pharmacogenetics slide

market slide

genome associations

environment gene

drug years years

development phase

genetic factors