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The Future of Genetics in Clinical Medicine. Aidan Power Clinical Pharmacogenetics Pfizer Global Research and Development Sandwich, United Kingdom. Visions of the Future.

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the future of genetics in clinical medicine

The Future of Genetics in Clinical Medicine

Aidan Power

Clinical Pharmacogenetics

Pfizer Global Research and Development

Sandwich, United Kingdom

visions of the future
Visions of the Future
  • The humn genome undobtedly offer unprecedented opportunites. All the drugs in the world act on only 479 known molecular targets. If only 10% of the genome represents targets this will produce the possibility of 3000 new molecular entities.
  • Pharmacogenetics aims at understanding thow genetic variation contributes to variation in response to medicines.
  • Over 1000 single gene disorders identified affecting 1-4% of the population.
  • Genetic factors important but environmental, behavioral factors a big influence
on the other hand
On the other hand…

On common diseases

‘It will be difficult, if not impossible, to find the genes involved or develop useful and reliable predictive tests for them.’

And pharmacogenetics

‘Sure, there are few cases where testing patients for certain enzymes involved in drug metabolism may help but it’s ridiculous to suggest that drug senstivity and resistance are wholly determined by inheritied genetic profiles.’

Neil Holtzman, Johns Hopkins University

‘It has been said that the four letters of the genetic code are H, Y, P, and E, and medical providers must realize that the molecular biology business is as adept at promoting its wares as is any other.’

Steve Jones, University College London

and the reality will depend on
And the reality will depend on ...

THE PROGRESS OF SCIENCE AND TECHNOLOGY AND THEIR ABILITY TO IMPACT ON:

  • Understanding of the molecular basis of disease
  • The discovery and development of drugs
  • The delivery of medicines to patients
slide5

Pharmacogenetics

  • Genetic causes of interpatient variability in phenotype
  • Relationship between patient phenotype and genotype
  • Phenotype: Clinical symptoms
  • Pharmacokinetic variability
  • Response to drug
  • -efficacy
  • -side effects
  • Genotype: A genetic marker(s) distinguishing specific variations within a DNA sequence

Responders

Non-responders

pharmaceutical needs discovering new medicines
Pharmaceutical Needs: Discovering New Medicines
  • Discover clinically-relevant “drugable” targets
  • Enhance decision making in R & D
  • Increase candidate survival in Phases I - III
  • Identify and use relevant “markers” in R & D that:
    • parallel or predict disease progression
    • parallel or predict disease severity
    • relate to accepted outcomes measures
    • accelerate drug development and approval
    • increase drug survivability in Phase IV/post-marketing
pharmacogenomics
Pharmacogenomics
  • Identification of novel discovery targets
    • Expedite Identification of ~5,000 “clinically relevant” targets
  • Adding human relevance to targets
    • Improve potential of unprecedented targets
  • Improved clinical trial design and interpretation
    • Genetic stratification of patients
pharmacogenomics1
Pharmacogenomics

SNPs - markers of genetic variation.

Relationships - phenotype-genotype.

  • Phenotype:
    • Disease state
    • Pharmacokinetic variability
    • response to Rx
  • Genotype:
    • A specific variation in a DNA sequence from a “consensus” sequence

Understand - impact of genetics on Rx response outcome.

Targets - clinically important “drugable” targets ® drug candidates

tools for pharmacogenomics
Tools for Pharmacogenomics
  • Access to variation in genes - SNPs
  • Genotyping tools
  • Access to phenotypic data
  • Analysis methodologies
slide10

SNP Identification/Mapping/Use

Candidate Gene Approach

Genome-Wide Map Approach

Genome-Wide Association Studies Using Linkage Disequilibrium: ~60,000 SNP markers at ~50 kb, or ~300,000 markers at ~10 kb intervals

Candidate Gene Association Studies: 5 SNP markers/gene

(~500,000 markers)

candidate gene vs whole genome
Candidate Gene vs. Whole Genome
  • Candidate Gene Approach
    • Hypothesis dependent
    • Drug target or genes in the target pathway
    • Drug metabolizing enzyme genes
    • Genes that play a role in the disease
    • Limited by our understanding of disease
  • Whole Genome SNP Map
    • Hypothesis-independent
    • New statistical methods needed to mine data
strategy in pharmacogenomics
Strategy in Pharmacogenomics

1. Collect Patient DNA from Clinical Trials

2. Identify Genetic Variation

3. Correlate Genetic Variation with Clinical Response

4. Predict Patient Response to Rx Based on Genetic Variation

collecting dna general approaches
Collecting DNA: General Approaches

Aims:

  • collect samples from relevant clinical trials
  • obtain widest possible remit for use
  • avoid retrospective collection - incomplete, inefficient
  • primary purpose PG (2° purpose disease analysis)

Principles:

  • obtain IEC/regulatory approval
  • obtain specific informed consent
  • participation in clinical trial not dependent upon donation of sample for hypothesis generation
informed consent
Informed Consent
  • Utilizes a separate consent for donation of a blood sample which will be anonymized prior to analysis.
  • Participation is optional.
  • Consent to “use a small sample of my blood to study the chemicals which make up all of my genes and contain my genetic information.”
  • Purpose for collecting sample is defined.
  • Clearly states that information identifying the subject will not be included with the blood sample.
  • NO INFORMATION WILL BE MADE AVAILABLE TO SUBJECT OR ANY OTHER PARTICIPANT OR MY PHYSICIANS.
categories for genetic research samples and data
Categories for Genetic ResearchSamples and Data*
  • Identified Samples/Dataare those labeled with personal identifiers such as Name or Social Security Number. Use of a clinical trial subject number does not make the sample/data identified.
  • Coded Samples/Dataare those labeled with a clinical trial subject number that can be traced or linked back to the subject only by the investigator. Samples do not carry any personal identifiers.
  • De-Identified Samples/Dataare double coded and labeled with the unique second number. The link between the clinical study subject number and the unique second number is maintained, but unknown to investigators and patients. Samples do not carry any personal identifiers.
  • Anonymized Samples/Dataare double coded and labeled with the unique second number. The link between the clinical study subject number and the unique second number is deleted. Samples do not carry any personal identifiers.
  • Anonymous Samples/Dataare those that do not have any personal identifiers and identification of the subject is unknown. Anonymous samples may have population information (e.g., the samples may come from patients with diabetes, but no additional individual clinical data).
  • ____________________________________________________________________________

*From the Pharmacogenetics Working GroupWorking Paper 1

slide16

Anonymization Process

Study Data

Central Lab

1

2b

Phenotype

Processing

Sample

Processing

De-Identified

Study Data

3

2a

slide17

Scientific Approaches

Establish

Genotyping

Assays

Prospective

Clinical

Trial

Statistical

Analysis

Clinical

Trial Ends

Time

Get DNA and

Drug Response

Phenotypes

Genotype

Develop

Hypotheses:

Candidate Gene

vs. SNP Map

table 1 examples of poor non responders following therapy
Table 1. Examples of Poor/Non Responders Following Therapy*

Disease Drug ClassPoor/Non Responders(%)

Cancer (breast, lung, brain) Various 70 – 100

Diabetes Sulfonylureas 25 – 50

Asthma Beta-2 agonist 40 – 75

OA/RA NSAID, COX-2 20 – 50

Duodenal Ulcer Proton pump 20 – 90

Hypertension Thiazides 50 – 75

Beta-blockers 20 – 30

ACE inhibitors 10 – 30

Angiotensin IIs 10 – 30

Hyperlipidemia HMGCoA reductase inhibitors 30 – 75

Depression SRRIs 20 – 40

Tricyclics 25 – 50

Migraine Serotonin 25 – 50

BPH Steroid 5a-reductase 40 – 100

*from BM Silber, Pharmacogenomics, Biomarkers, and the Promise of Personalized Medicine, in Pharmacogenomics,

W. Kalow and U. Meyer, editors, Marcel Dekker publishers, New York, 2000, in press.

generating hypotheses dmes or drug transporter mechanisms
Generating Hypotheses:DMEs or Drug Transporter Mechanisms
  • Are there genetic differences in key drug metabolism pathways?
  • Do transporter protein genotypes influence bioavailability?
  • Are levels of active metabolites influenced by genetic variation?
  • Do allele frequencies vary among ethnic groups?
generating hypotheses disease genes
Generating Hypotheses:Disease Genes
  • Are there known genetically-defined patient subpopulations with more uniform disease characteristics?
  • Are there known genetic markers for populations at-risk for the disease?
  • Are there known genetic predictors of clinical outcomes?
  • Are there known genetic differences among ethnic groups?
generating hypotheses drug target or related pathways
Generating Hypotheses:Drug Target or Related Pathways
  • Which genotypes used in Discovery’s screens/assays? Are they found in the disease population?
  • What are the functional consequences of different genotypes?
  • Does drug binding/activity differ among variants?
  • Any genetic differences in related pathways influencing drug activity (e.g., ligand turnover; upstream/ downstream signaling)?
  • Do any inherited diseases result from mutations in drug’s target?
  • Do allele frequencies vary among ethnic groups?
pharmacogenetics getting the right drug to the right patient
Pharmacogenetics: Getting the right drug to the right patient
  • Sources of variability in drug response:
    • diagnosis of disease
    • disease severity
    • compliance with pharmacotherapy
    • genetic profile: disease, drug metabolism, drug target
sources of genetic variation and drug response
Sources of genetic variation and drug response
  • Disease pathways
  • Drug metabolism
  • Drug target
disease pathway genes and pharmacogenetics adducin
Disease pathway genes and pharmacogenetics: adducin
  • Gly460Trp variant of -adducin associated with increased renal tubular absorption of sodium
    • also associated with  renin activity
  • Positive and negative association studies in hypertensives
  • Frequency in hypertensives:
    • ~ 20% in Europeans, ~ 65% in Japanese
  • In response to diuretics, the average BP drop is twice as great in Trp heterozygotes

Cusi et al (1997); Manunta et al (1998)

slide26

Drug response and DME variation

Adapted from Evans and Relling (1999)

slide27

Target genes and drug reponse

Adapted from Evans and Relling (1999)

slide28

Pharmacogenetics and ethnicity

Adapted from Evans and Relling (1999)

ethnicity and genetic variation drug response
Ethnicity and genetic variation drug response
  • Individual drug response vs ethnicity
    • For DMEs the key difference is a genetic one
    • Similarly for other genes
  • Clinical trials can take account of key genetic variation
    • Where correlation between genetic variation and drug response is close this can give greater understanding of ethnic differences
potential benefits for pharmacogenomic data
Potential Benefits for Pharmacogenomic Data
  • Portfolio Management in Early Development
    • confirm molecular mechanism of action
    • increase clinical confidence in rationale
    • evidence of pharmacodynamic response
    • rational dose selection
    • path to proof of concept
    • cost saving by identifying non-viability early
    • requirement to show effect on disease progression
    • identification of novel indications
  • Feedback to Discovery
    • target validation
    • ID new target pathways
hurdles challenges to the implementation of pharmacogenetics
Hurdles/Challenges to the Implementation of Pharmacogenetics
  • Predictive power of genetic testing in relation to drug response
  • Cost, availability, utility of diagnostics
  • Societal responses
    • public attitudes
    • regulatory/legal frameworks
how genomics and proteomics may change medicine and therapeutics in the next 20 years
How Genomics and Proteomics May Change Medicine and Therapeutics in the Next 20 Years

2000

2010

2020

Approval of 1st NCE linked to genetically-based Point-of-Care (POC) Diagnostic (Dx)

Robust and Economical HT Genotyping Platforms (1 MM/day)

Robust HT Haplotyping Tools

Sequencing of Human Genome Complete

10% of NCEs have genetic “POC” Dx

40,000 Gene Structures/Proteins Known; all SNPs in Genes Identified

Genes/Proteins Involved in Top 20 Common Diseases Defined

HT Gene Function Technology

Widespread Medical Screening with SNP Chips

High-Risk SNPs IDed; Prophylactic Rx Approved

Pharmas Working on 3,000 Targets

30% NCEs have Genetic “POC” Dx

-

slide33

New Millennium: Personalized Medicines

Disease Susceptibility Genes/Targets

Biomarkers Linked to Disease

Right Rx and Doseat the Right Time

Genomic, Genetic, Haplotype, Links

Gene Links to Efficacy/SAEs

slide35

Visions of the Future...

‘As genome technology moves from the laboratory to the health care setting, new methods will make it possible to read the instructions contained in an individual

person\'s DNA. Such knowledge may foretell future disease and alert patients and their health care providers to undertake better preventive strategies.’

Francis Collins, NIH

‘Preventive medicine is an economic necessity, and genomic medicine represents the best route we have to preventive medicine…pharmacogenomics will become part of routine therapeutics in some fields within 3-5 years.’

Gordon Duff, University of Sheffield

‘We are on the verge of being able to identify inherited differences between individuals which can predict each patient’s response to a medicine. This ability will have far-reaching benefits in the discovery, development and delivery of new medicines.’

Allen Roses, GlaxoSmithKline

genetics and identification of novel genes

Families with disease

Disease Gene

Normal Gene

Disease

Controls

Genetics and Identification of Novel Genes

Chromosomes

ATT-GCG-ACG

Genetic research to

identify region in the

genome that contains

disease causing gene

ATT-GGG-ACG

Affected

Normal

  • New gene leads to:
  • Novel Drug Target
  • Genetic marker of drug
  • response variation
  • Increase in the understanding
  • of disease biology

Populations of disease sufferers

and

healthy controls

the research environment
The Research Environment

Discovery and Development of Medicines

Approved

Drug

Gene

Function

Protein

Target

Drug

Candidate

Screening/

Chemistry

Gene

Efficacy

Discovery:Development:

Identify clinically relevant targets Confirm drug candidate’s mechanism of action

Confirm target relevance in chronic disease

Confirm drug response

slide38

Pharmacogenomics

  • Human Genetics
      • SNPs
      • Haplotypes
      • Sequencing
  • Expression Profiling
      • Specific transcript levels
      • Total RNA profiling
  • Proteomics
      • Specific biochemical markers
      • Protein profiling
  • Phenotype
  • Drug response
  • Disease

Prediction

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