Transcript Pharmacogenomics: Changing The Paradigm

Pharmacogenomics:
Changing The Paradigm
Aidan Power MD
Clinical Pharmacogenomics
Pfizer Global Research and Development
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Presentation
 Why
do genetics/pharmacogenomics?
 Types of studies
 Uses in drug development
 Drug
Discovery
 Drug Development
 Applications
 The
of gene expression
future?
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The route to a new medicine…
Registration
Full
Development
Exploratory Development
Discovery
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…is a long one
Discovery
Exploratory Development
Phase I
0
Full Development
Phase II
Phase III
10
5
Phase IV
15
Years
11-15 Years
Marketed
Drug
Idea
Patent life 20 years
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…and an expensive one!
It costs >$800 million to get a drug to market
3,332
2,487
$ Millions spent in
9 months in 2001
2,660
2,281
1,916 1,955
1,402
934
SGP
1,740
1,499 1,645
1,116
ABT
AHP
BMY
LLY
MRK
PHA
AZN
AVE
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JNJ
GSK

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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
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We Are Studying Genetic Diseases…
Heritability: The proportion of the disease that is due
to genetic factors
Huntington's
Disease
Schizophrenia
Genes
Environment
Rheumatoid
Arthritis
HDL level
0%
50%
100%
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Complex Phenotypes – What Can We Expect?
Gene 1
Gene 1
Gene 2
Gene 5
Gene 2
Environment
Gene 3
Gene 4
Environment
Few genes and environmental
factors each contributing a large
risk.
Many genes and environmental
factors each contributing a small
risk.
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Pharmacogenomics 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
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Markers of Genetic Variation
Polymorphism: A genetic variation that is observed at a
frequency of >1% in a population
Types of Polymorphisms



Single Nucleotide
Polymorphism (SNP):
Simple Sequence Length
Polymorphism (SSLP):
Insertion/Deletion:
GAATTTAAG
GAATTCAAG
NCACACACAN
NCACACACACACACAN
NCACACACACACAN
GAAATTCCAAG
GAAA[ ]CCAAG
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Human Genetic Association Study Design
Disease
Responder
Allele 1
Control
Non-responder
Allele 2
Marker A:
Allele 1 =
Allele 2 =
Marker A is associated
with Phenotype
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Whole Genome Associations
Disease Population
N=500
1
Matched Control Population
N=500
~3,000,000 common SNPs across genome
• Representing every gene
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P value
Regions of
association
1
Chromosomal Location
22
Informatics to ID gene(s) mapped to associated SNP
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Applying Pharmacogenomics
Discovery
DISEASE
GENETICS
Choosing
the Best
Targets
.
Development
TARGET
VARIABILITY
Better
Understanding
of Our Targets
SELECTING PHARMACORESPONDERS GENETICS
Improving
Early
Decision
Making
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Predicting
Efficacy and
Safety
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Target 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
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Cholesteryl Ester Transfer Protein
-629/Prom
VNTR-1946
+279/In1
1 2
Taq1B
+16/Ex14 +9/3'
+199/In12 +82/Ex15
+383/In8
345
67 8
9
10
MspI
11
12 13 14
15 16
I405V R451Q
• Spans 22 kb on human chromosome 16
• Several polymorphisms identified
• Implicated in modulation of HDL levels
• SNPs genotyped in 110 healthy subjects
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CETP Association Study (1)
Association of CETP markers and baseline phenotype
0.20
-629/promoter
CETP mass
HDL
0.15
0.10
0.05
0.0
R-square from ANOVA
VNTR
0
5000
10000
15000
20000
Distance in bases from transcription start
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Clinical 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
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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
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Challenges 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”
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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
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Target Variation – 5HTT
 Variation
in promoter sequence
 44bp insertion/deletion (L and S alleles)
Long
SLC6A4 expression
(528bp)
Short
SLC6A4 expression
(484 bp)
Long/Long
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Short/Short
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Association With Drug Response?
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5HTT 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% )
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Case 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.
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CGI Response by Genotype
100
Sertraline group: Percentage of CGI responders
by week and 5HT T LPR genotype
60
21
31
24
40
percent
80
SS o r SL gen oty p e
LL g en oty pe
67
60
26
20
63
30
0
66
64
P=.01
1
P=.01
2
4
6
8
s tud y w ee k
• L/L genotypes respond more rapidly to Sertraline
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CGI Response by Genotype
100
Placebo group: Percentage of CGI responders
by week and 5HTT LPR genotype
60
40
percent
80
SS or SL genoty pe
LL genoty pe
74
81
23
20
78
22
21
81
83
0
23
1
22
2
4
6
8
s tudy w eek
• Response time to placebo not significant
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Clinical 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?
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Pharmacogenomics
Human Genetics
• SNPs
• Haplotypes
• Sequencing
Expression Profiling
• Specific transcript levels
• Total RNA profiling
Phenotype
• Drug response
Proteomics
• Specific biochemical
markers
• Protein profiling
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Prediction
• Disease
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Cancer: a Model for PG Approaches
Genetics of Cancer

Accumulation of
molecular events
Phenotype of Cancer

Stages of phenotype
– LOH
– Oncogene activation
– Tumor suppressor
inactivation
– cytogenetic alterations
–
–
–
–
–
–
dysplasia/premalignant
differentiation
invasive
metastases
Outcomes
Response
Accumulation of molecular events
Tumor Phenotype
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Genomic Technologies: Somatic
Isolate RNA
Isolate DNA
Fluorescent label
Amplify region of interest
Oligonucleotide
Hybridization
Can these approaches provide clues into the
state and future of tumor pathogenesis?
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Somatic Expression Signals
Expression-based signature
Genomic profile vs IPI
Ash et al. Distinct types of diffuse B-cell lymphoma identified by
gene expression profiles. Nature 2000, 403:503-11
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Breast Cancer Profiling for Prognosis
Working with Agilent
to develop microarray
based diagnostic
A Gene-Expression Signature
as a Predictor of Survival in
Breast Cancer. van de Vijver
etal NEJM 2002 347:1999-2009
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Towards 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
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What the future holds…
1990s
2000s
Linkage studies
Beyond
Regulatory scrutiny
Sequencing
Candidate gene association studies
‘omics’ integration
Large scale SNP detection
Whole genome association studies
Pharmacogenetics
Personalized sequencing
Precision therapies
Pharmacogenomic diagnostics
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Acknowledgements
John Thompson
Patrice Milos
Maruja Lira
Suzin McElroy
Albert Seymour
Katey Durham
Hakan Sakul
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