Pharmacogenetics Hype or Hope?. CDD Course June 8, 2006 2 Lecture Overview Drug Discovery Path –What are the problems? –What are the causes? What is genetic

Pharmacogenetics Pharmacogenetics

Hype or Hope?Hype or Hope?

2CDD Course June 8, 2006

Lecture OverviewLecture Overview

• Drug Discovery Path– What are the problems?– What are the causes?

• What is genetic variance?– What is the driving force and how does it lead to disease?– How do we measure it?

• Genetic variance in Drug response: pharmacogenetics– How do we measure it?– Are there any examples?– How do we apply it?– Is it feasible? Technically and economically?

• Outlook for the future


First: what is the definition of First: what is the definition of Pharmacogenetics?Pharmacogenetics?

• Pharmacogenetics:

– The genetics of drug response– The patient’s point of view, what a patient does with the drug

• Pharmacogenomics:

– The study of drug-induced gene expression– The drug’s point of view, what a drug does to the patient

Drug Discovery PathDrug Discovery Path


Intermezzo: Intermezzo: The Drug Discovery Pathway The Drug Discovery Pathway

and its problemsand its problems

TargetDiscovery

Lead Discovery

Lead Optimization

ExploratoryDevelopment

FullDevelopment Market

00 22 44 7766 1212 141499No. of years:No. of years:

1 NCE:1 NCE: 14 years14 years

USD 800 millionUSD 800 million

No. of projects:No. of projects:5757 1414 88 4.54.5 22 113636 2222

90% attrition

No. of compounds:No. of compounds:300.000300.000 1515 4-54-5 2-32-3 1-21-2 11

Data based on several leading pharmaData based on several leading pharma{Brown, 2003, Drug Disc. Today 8, 23}{Brown, 2003, Drug Disc. Today 8, 23}

Phase I, IIa Phase III, IV


First human dose to First patient

dose

First patient dose to First pivotal

dose

First pivotal dose to First

submission

First submission to First launch

Su

cces

s r

a te

Year of entry into phaseCurrent success rate

1994

-96

1995

-97

1996

-98

1997

-99

1998

-00

1999

-01

1994

-96

1995

-97

1996

-98

1997

-99

1998

-00

1999

-01

1994

-96

1995

-97

1996

-98

1997

-99

1998

-00

1999

-01

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1994

-96

1995

-97

1996

-98

1997

-99

1998

-00

1999

-01

Trends in success rates by Trends in success rates by phasephase

CMR success rates CMR success rates methodologymethodology


Reasons for attritionReasons for attritionAttrition as a result of Drug safety and efficacy mainly caused

by:

• Unexpected differences with animal models used in pre-clinical research– Animal disease models poorly match (especially in immunological

diseases and CNS) – Animal toxicology and metabolism can differ significantly from

humans• Unexpected variance in the human population with regard

to drug metabolism and efficacy.– Varying degrees of genetic predisposition– Currently difficult to anticipate on (lack of information on

cause/effect to steer prediction)

What is genetic variance?What is genetic variance?


Genetic variationGenetic variation

Egg production (6 weeks)

Meat production (6 weeks)

On average, one human to another differs ~0.1% On average, one human to another differs ~0.1% (=~3.2 10(=~3.2 106 6 nucleotides)nucleotides)


D.simulans & D.yakubaD.simulans & D.yakuba

20%

36,000,000 differences

600,000 aa differences


1%

34,000,000 nucleotide differences290,000 amino acid differences


0.1%

3,400,000 nucleotide differences12,800 amino acid differences


Variance, some definitionsVariance, some definitions• Gene unit of hereditary information that occupies a fixed

position (locus) on a chromosome. Genes achieve their effects by directing the synthesis of proteins.

• Allele An appearance of a gene on either of the 2 chromosomes, or: one member of a pair or series of genes that occupy a specific position on a specific chromosome.

• Haplotype A combination of polymorphisms or genes or other genetic landmarks on one chromosome of anindividual

• Genotype The total genetic makeup of an individual


Definition of a Definition of a PolymorphismPolymorphism

• polymorphism= the inheritance of genes in different forms termed alleles

• alleles have different DNA sequences

• polymorphic locus: the frequency of the most common allele is less than 99%.– 1 allele in 100 alleles– 100 alleles =50 people– 1 person in 50 (2%) is heterozygous


Types of Polymorphisms,Types of Polymorphisms,Sequence variationSequence variation

• Single Nucleotide Polymorphism (SNP): GAATTTAAG

GAATTCAAG• Simple Sequence Length

Polymorphism (SSLP): NCACACACAN • Also known as CA-repeat NCACACACACACACAN• NCACACACACACAN

• Insertion/Deletion: GAAATTCCAAGGAAA[ ]CCAAG


Genome structural Genome structural variationvariation

Feuk et al. Nature Reviews Genetics 7, 85–97 (2006)

In addition, variation in active genes is now found to be much higher than originally anticipated


Other types of variationOther types of variation

• DNA methylation sites

• Epigenetic control

• ?


What’s the difference What’s the difference between a SNP and a between a SNP and a

mutation?mutation?• None!

• We generally say a mutation is disease (phenotype) related

• SNPs and mutations are just a sign of variance, constant mutation rate with evolutionary consequences


Constant mutation rate Constant mutation rate

• Negative selection

• Neutral

• Positive selection

20CDD Course June 8, 2006 Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

Sickle cellallele in Africa

1–5%5–10%10–20%

P. Falciparummalaria in Africa

Malaria

Normal red blood cellsSickled red blood cells

Heterozygote Advantage: Sickle Heterozygote Advantage: Sickle cell trait confers resistance to cell trait confers resistance to

MalariaMalaria


Another exampleAnother example

• Lactose intolerance

• Study in the Finish population

• Demonstrated that actually this is the wild type, whereas lactose tolerance is caused by a recent mutation


Co-Co-evolutionevolution

North-Central Europe

PCA using cattle diversity1st component derived from the allele frequencies of cattle genes

PCA using cattle diversity1st component derived from the allele frequencies of the human lactose persistence genes


Mendelian vs. complex diseasesMendelian vs. complex diseasesoror

Monogenic vs. Polygenic Monogenic vs. Polygenic inheritanceinheritance


Genetic variance Genetic variance measurementsmeasurements

• These days most commonly performed by measuring the Single Nucleotide Polymorphisms (SNPs)


SNPsSNPs

Paternal allele: CCCGCCTTCTTGGCTTTACA

Maternal allele: CCCGCCTTCTCGGCTTTACA

Paternal allele : CCCGCCTTCTTGGCTTTACA

Maternal allele : CCCGCCTTCTTGGCTTTACA


Distribution of SNPs for 1630 Distribution of SNPs for 1630 Genes (Genaissance HapFocus Genes (Genaissance HapFocus

DB)DB)

4.15.7 5.8 4.5 6.1

0

1

2

3

4

5

6

7

8

9

10

Coding 5' UTR 5' Upstream Intron 3' UTR

SN

Ps

per

kb

of

DN

A


Gene SNPs01

01

01

01

01 ~15 SNPs per gene

215 combinations

Genaissance principle in a Genaissance principle in a nutshellnutshell

HAP™ Markers: gene HAP™ Markers: gene haplotypeshaplotypes

ChromosomeLocus of Gene

Exons

Promoters

SNPs

PairedHaplotypes

0 1 0 0 1

1 0 1 1 0

~18 haplotypes per gene


Mendelian inheritance vs Mendelian inheritance vs non-Mendelian inheritancenon-Mendelian inheritance

Example: autosomal dominant inheritance Example: Asthma in the Icelandic population


Different methods to Different methods to measure variance and measure variance and

inheritanceinheritance• Family based

– Traditional linkage analysis– Allele sharing: IBS/IBD

• Population based– Case-Control study– Transmission disequilibrium test (TDT)– Sibling control


LD vs Risk RatiosLD vs Risk RatiosLinkage describes the phenomenon whereby allele at neighboring loci are close to one another on the same chromosome, they will be transmitted together more frequently than chance. LOD score : Z(q) = log10 [L(q) / L(1/2)]

Two basic hypotheses are tested:

H0: free recombination, q = 1/2

H1: linkage, q < 1/2

where q is the recombination fractionand L is a likelihood function

lod score > 3: evidence of linkagelod score > 3: evidence of linkage

2 < lod score < 3: suggestive evidence of linkage2 < lod score < 3: suggestive evidence of linkage

-2 < lod score < 2: uninformative of linkage -2 < lod score < 2: uninformative of linkage

lod score < -2: exclusion of linkagelod score < -2: exclusion of linkage

Recurrence Risk Ratio (λ)Recurrence Risk Ratio (λ)

Frequency in relatives of affected personFrequency in relatives of affected person

λλr r = -------------------------------------------------------= -------------------------------------------------------

Population frequencyPopulation frequency

r denotes the degree of relationshipr denotes the degree of relationship

Disease λ s

Cystic fibrosis 500 Type I diabetes 15 schizophrenia 8.6 Type II diabetes 3.5

Genetic Variance in Drug Genetic Variance in Drug ResponseResponse


“…“…more than 90% of more than 90% of drugs only work in 30-50% drugs only work in 30-50%

of people.” of people.”

Allen D. Roses GlaxoSmithKlineAllen D. Roses GlaxoSmithKline


Where do drugs interact with Where do drugs interact with proteins?proteins?

•

Goldstein et al. Nature Rev. Gen. 4, 937 (2003)


Pharmacogenetic Basis for Pharmacogenetic Basis for

Differences in Medicine Differences in Medicine ResponseResponse

• Patients may have altered drug efficacy or greater risk of drug-related adverse events stemming from polymorphisms in:– genes encoding the drug target– genes encoding drug metabolizing enzymes– genes related to drug clearance mechanisms– genes causally linked to the disease and hence

causally related to drug efficacy – genes causally linked to mechanisms

underlying adverse events


2000In the US the cost of problems linked to drug

use in the ambulatory setting exceeded $US177 billion in the year 2000

Rodriguez-Monguio R et al 2003 Pharmacoeconomics 21(9):623-50

2000In the US the cost of problems linked to drug

use in the ambulatory setting exceeded $US177 billion in the year 2000

Rodriguez-Monguio R et al 2003 Pharmacoeconomics 21(9):623-50

197328% of hospitalized patients had adverse drug reactions

Miller Am J Hosp Pharm 30:584-592

197328% of hospitalized patients had adverse drug reactions

Miller Am J Hosp Pharm 30:584-592

197917% of hospitalized children had adverse drug-attributed events

Mitchell AA et al Am J Epid

110: 196-204

197917% of hospitalized children had adverse drug-attributed events

Mitchell AA et al Am J Epid

110: 196-204 1994

2,216,000 serious adverse drug reactions in hospitalized patients

Lazarou et al 1998; 279: 1200-1205

19942,216,000 serious adverse drug reactions in hospitalized patients

Lazarou et al 1998; 279: 1200-1205 1995

Drug-related morbidity & mortality estimated at $76.6 billion

Johnson & Bootman Arch Intern Med 1995; 155: 1949-56

Adverse Drug Reactions Adverse Drug Reactions Incidence and CostIncidence and Cost


Cytochrome P450 Cytochrome P450 enzymesenzymes

Four phenotypes identified:– Poor metabolizer: lack the functional

enzyme (7%)– Intermediary metabolizers: heterozygous

for one deficient allele or carry two alleles that cause reduced activity

– Extensive metabolizers: two normal alleles– Ultra rapid metabolizers: multiple gene

copies (5,5%)


Meyer, Nature Reviews Genetics 2004


The consequences of outlier cytochrome The consequences of outlier cytochrome P450 CYP2D6-dependent drug P450 CYP2D6-dependent drug

metabolism. metabolism.


Other examples of Other examples of metabolizing enzymesmetabolizing enzymes


These associations were compiled from the literature by using the keywords „pharmacogenetics“ OR pharmacogenomcis“, „association study“ AND „drug response“, „polymorphism“ AND „drug response“.

Goldstein et al. Nature Rev. Gen. 4, 937 (2003)

Any examples?Any examples?


Some Products where PGx Some Products where PGx makes a differencemakes a difference


Biotech examplesBiotech examples

• Anti cancer drug Herceptin (MoAb)ERBB2 is a 185-kda (mw) tyrosine kinase receptor that might be

overexpressed in 25–30% of human breast cancers.

overexpression of erbb2 is associated with enhanced tumour aggressiveness and a high risk of relapse and death effective treatment was shown in patient over expressing ERBB2

• Anti TNF (Infliximab)SNP in the promotor seems to be associated with reduced

response with Infliximab (more TNF compensates for anti-TNF


2 Adrenergic Receptor2 Adrenergic Receptor

• G Protein-coupled receptor expressed throughout the body• Receptor for catecholamines (epinephrine and norepinephrine)• Drug target in the treatment of heart failure and asthma• Also thought to play a role in obesity and possibly diabetes


Function of ADRB2 VariantsFunction of ADRB2 Variants

• Ile164, Gly16 and the Gly16/Gln27 combination are associated with depressed exercise performance in heart failure (Wagoner et al., 2000)• Gly16 associated with nocturnal asthma (OR 3.8)• Gly16 associated with increased agonist-promoted downregulation of ADRB2• Glu27 markedly associated with obesity (OR 10), Glu27 homozygotes had 50% larger fat cells


ADRB2 HaplotypesADRB2 Haplotypes

13 SNPs found in 12 haplotypes out of 8,192 possible haplotypesDivergence between ethnic groups


Phylogeny of ADRB2 HaplotypesPhylogeny of ADRB2 Haplotypes

• Based only on the multi-ethnic panel• Some haplotypes related to others by recombination


Functional Analysis of ADRB2 Haplotype PairsFunctional Analysis of ADRB2 Haplotype Pairs

• Mean responses by haplotype pair varied by >2-fold• Response significantly related to haplotype pair• Response not related to individual SNPs• Note that the 2/4 haplotype pair has a B2AR agonist response halfway between that of the 2/2 pair and the 4/4 pair

FEV = Forced Expiration Volume


Expression Analysis of ADRB2 HaplotypesExpression Analysis of ADRB2 Haplotypes

How do we apply it?How do we apply it?


Genetic Marker = Genetic Marker = BiomarkerBiomarker

• A genetic marker is a predictive biomarker

• The predictive power of a genetic marker is determined by the strength of the underlying genetics in a disease and drug response

• The value lies in focused therapies and focused clinical development


Mendelian Mendelian (Monogenic) (Monogenic)

Disorders/Drug Disorders/Drug response response

Compared With Compared With

Complex Complex (Polygenic)(Polygenic)

Disorders/Drug Disorders/Drug responseresponsePeltonen and McKusick. Science. 2001;291:1224-1229.


GenotypingGenotyping

1.Candidate gene(s)• Develop SNP genotypes and haplotypes for each

implicated gene(s)• Genes selected based on

– Biological paradigms– Knowledge of disease pathogenesis– Genomic data mining

2.Total genome analysis• Screen patients (and controls) in attempt to identify

a genomic region containing the drug response gene(s)

– Use positional cloning to isolate and characterize the underlying gene


DiseaseResponder

ControlNon-responder

Allele 1 Allele 2

Marker A is associated with Phenotype

Marker A:

Allele 1 =

Allele 2 =

Human Genetic Association Human Genetic Association Study DesignStudy 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 Whole Genome AssociationsAssociations

56

Blocks vs BinsBlocks vs Bins

57

Block Tags vs Bin TagsBlock Tags vs Bin Tags

• 6 Bin Tags• 4 Haplotype Tags

58

Gene X ExampleGene X Example

Source SNPs Bins Bins missedHapMap 40 23^ 5Perlegen 40 15' 13Journals 9 8 20

Total 66* 28*

^ - 19 haplotype tags for these 23 bins.‘ - Equals 16 HapMap bins.* - Includes 1 AA SNP (MAF = 0.05) from Genaissance not seen in the other three sources.

NB – There are approximately 300 SNPs across the gene genomic interval in dbSNP and the PG Genetics Database.

59

Selection ConsiderationsSelection Considerations

Sources Effect on protein Extent of LD (D’ vs r2)

True measure of gene size Number and length of bins/blocks

Tag bins or tag haplotypes SNP coverage in gaps between bins/blocks Power

Number of SNPs Allele Frequency

60

Power CalculationPower Calculation

61

Selection ConsiderationsSelection Considerations

Sources Effect on protein Extent of LD (D’ vs r2)

True measure of gene size Number and length of bins/blocks

Tag bins or tag haplotypes SNP coverage in gaps between bins/blocks Power

Number of SNPs Allele Frequency

62

X gene structure 02012006X gene structure 02012006

CA

CHB/JPT

rs1799914J/C 10%

+198

rs486779826%

J/C 46%+2203

rs68640%

J/C 15%+1402

rs1251822213%

J/C 24%+2934

rs70374832%

J/C 38%+4067

BOTH

rs216863114%

-5900

rs68599703%

-9891

rs6878159J/C 26%

-6299

rs2674023%

-13001

rs247102038%

J/C 17%-18794

rs26740546%

-14450

rs1355077J/C 39%-20211

rs1496133J/C 44%-23178

rs2644645J/C 35%-26084

rs686272128%

-25710

rs17065069J/C 15%-25300

63

CA

JPT/CHB

BOTH

Y gene structure 02072006Y gene structure 02072006

rs34607633%

J/C 35%-8042

rs180904913%

J/C 32%+2161

rs259496638%

+24552

rs34607018%

+3151

rs346078 33% +27116

rs101811130%

J/C 31%-1202

rs12629094J/C 7%-45787

rs762575624%

J/C 27%-51007

rs34759435%

J/C 32%-51196

rs124884104%

-54972

rs187495922%

J/C 36%-53464

rs34759640%

J/C 41%-41217

rs1191505016%

J/C 30%-35491

rs6773737J/C 27%-35402

rs4660646J/C 14%

-9553

rs2606731J/C 27%

-9109

rs347606J/C 20%-36418

rs4684787 29% +279141

rs9876898 18% +267237

rs17534941 4% +127798

rs7621218 J/C 36% +141289

rs7623889 4% +176663rs9818393 J/C 14% +208944

rs7623147 3% +219309

rs11128552 16% J/C 38% +239231rs2442793 5% +246971

rs2443706 16% +249781rs2447607 38% +251303

rs3856794 20% +261381

rs2344826 11% +261903

rs2594992 J/C 35% +60273

rs3816380 15% +81249rs1375204 6% +86992

rs12630869 J/C 6% +89530

rs9310379 37% +133419

rs4684776 J/C 43% +142499

rs9848833 J/C 8% +145801rs11707842 22% +172140rs9816564 J/C 37% +174573

rs13081468 5% +28029

rs2606757 J/C 38% +57439rs17034276 J/C 8% +58075

64

CYP2D6 gene structure 02092006CYP2D6 gene structure 02092006

CA

CHB/JPT

rs962001810%

+77761

rs1058172N/S%+3266

rs3915951N/S%+3158

rs11568728N/S%+1848

rs1058164SYN%+1662

BOTH

rs482207510%

+176388

rs600256148%

+179744rs5751188

6%J/C 44%+180908

rs600256033%

+182062

rs962353133%

+7250rs5758589

45%J/C 35%+8412

rs106275331%

J/C 6%+146137

rs2743467J/C 26%+24430

rs2070903J/C 20%+21998

rs4467371J/C 42%+16878

rs112603J/C 30%-149135rs9611766

20%J/C 32%-149689

rs599613520%

-102508

rs121658468%

-110348

rs80506J/C 34%-150190

rs13487148%

-113766rs5758686

24%J/C 24%-116427

rs13487347%

J/C 39%-118616

rs575123219%

-25354

rs93063568%

-28140rs738257

24%-30348

rs17002868J/C 22%-45516

rs21426956%

-42298

rs48221008%

-143836

rs13488324%

J/C 38%-132537

rs81426734%

-57303

rs4822079J/C 14%+144336

rs1333095%

+140342

rs133330J/C 18%+129492

rs13333724%

J/C 13%+122253

Is it feasible? Are there risks?Is it feasible? Are there risks?How can we calculate the benefits?How can we calculate the benefits?


Genetic Pedigree of World Genetic Pedigree of World PopulationsPopulations

Adapted from Cavalli-Sforza and Feldman. Nat Genet. 2003


Haplotype block Haplotype block differences among differences among

populationspopulations


SNPs in different SNPs in different populationspopulations


Effect of sampling in Effect of sampling in association studiesassociation studies


Genotyping children with Genotyping children with ALLALL

From : Veenstra et al., AAPS Pharmsci 2000;


A simple Health A simple Health Economics ModelEconomics Model

BCG report, 2001


What is the prevalence of What is the prevalence of the the

genetic variant?genetic variant?• Genetic testing is essentially a screening strategy• Thus, the frequency of the variant allele in the

population being tested will be a critical factor • Example:

– prevalence of a genotype is 0.5%, – 200 patients must be tested to identify 1 patient with a

variant allele, on average

• Sensitivity enhanced by methods used in CEA– e.g., calculating an incremental cost effectiveness ratio



Hypothetical AnalysisHypothetical Analysis

• Varied the following parameters:– cost of the test ($5 to $250)– mortality due to severe myleosuppression (5% to 25%)– prevalence of patients with a TPMT (thiopurine s-

methyltransferase ) deficient genotype (0.3%, 0.5%, and 1.0%)

• These 3 parameters are representative of 3 of the dimensions that affect the cost-effectiveness of genetic testing: – economic (cost of test)– genetic (genotype prevalence)– clinical (mortality of myleosuppression)



Genotype prevalence Genotype prevalence 0.3%0.3%

25% 21% 17% 13% 9%5%

$5

$80

$150

$225

$0

$50,000

$100,000

$150,000

Incremental cost-effectiveness ratio

($/QALY)

Attributable mortality of severe myleosuppresion

Cost of test

Deficient genotype prevalence 0.3%

100000-150000

50000-100000

0-50000

QALY, equivalent to 1 year of perfect health



Genotype prevalence Genotype prevalence 1.0%1.0%

25% 21% 17% 13% 9%5%

$5

$80

$150

$225

$0

$50,000

$100,000

$150,000

Incremental cost-effectiveness ratio

($/QALY)

Attributable mortality of severe myleosuppression

Cost of test

Deficient genotype prevalence 1.0%

100000-150000

50000-100000

0-50000



Ethical issues to consider:Ethical issues to consider:AnonymizationAnonymization

Copy,Copy,

RELABEL,RELABEL,

Remove SID 123 and Remove SID 123 and personal identifiers personal identifiers

((eg eg DOB)DOB)

AnonymizedAnonymizedGenomicGenomicAnalysisAnalysis

Anonymized Samples

Anonymized Data

123

Sample

123

Clinical dataSecure Data in

PharmacogenomicsDatabase

XYZ

XYZ

new tube,new tube,

remove SID 123,remove SID 123,

RELABELRELABEL

DELETE LINK

LINK

123~XYZ … …… …… …X

Subject 123 at Study Site

XYZ

Secure Sample Storage

New Random ID (XYZ) Generated in Secure File

LINK

123~XYZ … …… …… …


Predicting Treatment Predicting Treatment response and outcomeresponse and outcome

• Each individual has a unique genetic make-up

• During the course of live our environment shapes our disease risk and drug response

• Pharmacogenetics aims at:– Predicting the treatment efficacy for improved

therapy– Avoiding potential health risks due to side effects– Speed up drug discovery by patient stratification


What areas will benefit most What areas will benefit most from Pharmacogenetics?from Pharmacogenetics?

• Therapeutic areas where treatment response prediction is a question of life and death, e.g. cancer and psychiatric diseases

• Therapeutic areas where little is know about the disease mechanisms and mechanism of action of a drug, e.g. psychiatric disorders


Pharmacogenetics and Pharmacogenetics and CNSCNS

• There’s a need for better classification of psychiatric disorders

• Modern life science approaches deliver endophenotypes:– Genetics/genomics– Proteomics– Imaging– Etc.


AlleleGene Genotype PhenotypeEndophenotpye

EndophenotypesEndophenotypes


Pharmacogenetics and Pharmacogenetics and imagingimaging

The BDNF Val66Met polymorphismThe BDNF Val66Met polymorphismFigure 1. Statistical maps of t-transformed hippocampal volume differences derived by optimized voxel-based morphometry in met relative to val/val-BDNF carriers thresholded at p = 0.05 (corrected) in coronal, sagittal, and axial views, showing bilateral significant hippocampal volume reduction in met-BDNF carriers

Pezawas, L. et al. J. Neurosci. 2004;24:10099-10102

82CDD Course June 8, 2006Copyright ©2004 Society for Neuroscience


Figure 2. Mean differences ({+/-}SEM) in hippocampal volume reduction in met-BDNF carriers relative to val/val-BDNF subjects within regions of statistical significance (p = 0.05) as shown in Figure 1

The BDNF Val66Met polymorphismThe BDNF Val66Met polymorphism

83CDD Course June 8, 2006Copyright ©2004 Society for Neuroscience


Figure 3. Statistical gray matter maps of the entire brain showing volume reductions of met-BDNF carriers in comparison to val/val-BDNF have been transformed from MNI space in Talairach space and converted to z-scores

The BDNF Val66Met polymorphismThe BDNF Val66Met polymorphism

Outlook for the futureOutlook for the future

Hype or Hope ?Hype or Hope ?


PGx efforts in the worldPGx efforts in the world


PGx alliances and PGx alliances and numbersnumbers

Commercial collaborations Commercial collaborations based on pharmacogeneticsbased on pharmacogenetics


Clinical data integration and Clinical data integration and personalized medicinepersonalized medicine

Source: IBM


Integration of discplinesIntegration of discplines

Source: IBM


What do we still need?What do we still need?

• Better disease classification based on endophenotypes

• Better (low level) data integration• Better analysis methods (taking gene-

gene interactions into account• Better statistical methods• Better trained computational scientists

in the field of Molecular Medicine


Thank you for listeningThank you for listening

Documents

Pharmacogenetics Hype or Hope?. CDD Course June 8, 2006 2 Lecture Overview Drug Discovery Path –What are the problems? –What are the causes? What is genetic