Transcript Pharmacogenetics in Organon R&D: what’s it all about?

Pharmacogenetics
Hype or Hope?
Lecture 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
CDD Course June 8, 2006
2
First: what is the definition of
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
CDD Course June 8, 2006
3
Drug Discovery Path
Intermezzo:
The Drug Discovery Pathway
and its problems
Target
Discovery
Lead
Discovery
No. of years:
0
Exploratory
Development
Phase I, IIa
2
No. of compounds:
300.000
No. of projects:
57
Lead
Optimization
36
Full
Development
Phase III, IV
4
6
7
9
12
15
4-5
2-3
1-2
1
22
14
8
2
1
4.5
Market
14
90% attrition
Data based on several leading pharma
{Brown, 2003, Drug Disc. Today 8, 23}
1 NCE:
14 years
USD 800 million
CDD Course June 8, 2006
5
Trends in success rates by phase
CMR success rates methodology
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
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Current success rate
CDD Course June 8, 2006
Year of entry into phase
6
Reasons for attrition
Attrition as a result of Drug safety and efficacy mainly caused
by:
• Unexpected differences with animal models used in preclinical 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)
CDD Course June 8, 2006
7
What is genetic variance?
Genetic variation
Meat production (6 weeks)
Egg production (6 weeks)
On average, one human to another differs ~0.1%
(=~3.2 106 nucleotides)
CDD Course June 8, 2006
9
D.simulans & D.yakuba
20%
36,000,000 differences
600,000 aa differences
CDD Course June 8, 2006
10
1%
34,000,000 nucleotide differences
290,000 amino acid differences
CDD Course June 8, 2006
11
0.1%
3,400,000 nucleotide differences
12,800 amino acid differences
CDD Course June 8, 2006
12
Variance, 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 an
individual
• Genotype The total genetic makeup of an individual
CDD Course June 8, 2006
13
Definition of a Polymorphism
• 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
CDD Course June 8, 2006
14
Types of Polymorphisms,
Sequence variation
• Single Nucleotide
Polymorphism (SNP):
GAATTTAAG
GAATTCAAG
• Simple Sequence Length
Polymorphism (SSLP):
NCACACACAN
•
Also known as CA-repeat NCACACACACACACAN
•
NCACACACACACAN
• Insertion/Deletion:
CDD Course June 8, 2006
GAAATTCCAAG
GAAA[ ]CCAAG
15
Genome structural variation
In addition, variation in active genes is now found to be much higher than
originally anticipated
Feuk et al. Nature Reviews Genetics 7, 85–97 (2006)
CDD Course June 8, 2006
16
Other types of variation
• DNA methylation sites
• Epigenetic control
• ?
CDD Course June 8, 2006
17
What’s the difference between a
SNP and a 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
CDD Course June 8, 2006
18
Constant mutation rate
• Negative selection
• Neutral
• Positive selection
CDD Course June 8, 2006
19
Heterozygote Advantage: Sickle cell
trait confers resistance to Malaria
Sickled red blood cells
Sickle cell
allele in Africa
1–5%
5–10%
10–20%
CDD Course June 8, 2006
Normal red blood cells
P. Falciparum
malaria in Africa
Malaria
20
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Another 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
CDD Course June 8, 2006
21
Co-evolution
North-Central Europe
CDD Course June 8, 2006
PCA using
cattle diversity
1st component
derived from
the allele
frequencies of
cattle genes
PCA using
cattle diversity
1st component
derived from
the allele
frequencies of
the human
lactose
persistence
genes
22
Mendelian vs. complex diseases
or
Monogenic vs. Polygenic inheritance
CDD Course June 8, 2006
23
Genetic variance measurements
• These days most commonly performed
by measuring the Single Nucleotide
Polymorphisms (SNPs)
CDD Course June 8, 2006
24
SNPs
Paternal allele: CCCGCCTTCTTGGCTTTACA
Maternal allele: CCCGCCTTCTCGGCTTTACA
Paternal allele : CCCGCCTTCTTGGCTTTACA
Maternal allele : CCCGCCTTCTTGGCTTTACA
CDD Course June 8, 2006
25
Distribution of SNPs for 1630 Genes
(Genaissance HapFocus DB)
10
SNPs per kb of DNA
9
8
7
6
5
4
3
2
5.7
5.8
4.5
6.1
5' UTR
5' Upstream
Intron
3' UTR
4.1
1
0
Coding
CDD Course June 8, 2006
26
Genaissance principle in a nutshell
HAP™ Markers: gene haplotypes
Exons
Promoters
Chromosom
e
Locus of
Gene
Gene SNPs
Paired
Haplotypes
CDD Course June 8, 2006
SNPs
0
1
0
1
0
1
0
1
0
1
0
1
0
0
1
1
0
1
1
0
~15 SNPs per gene
215 combinations
~18 haplotypes per
gene
27
Mendelian inheritance vs nonMendelian inheritance
Example: autosomal dominant inheritance
CDD Course June 8, 2006
Example: Asthma in the Icelandic population
28
Different methods to measure
variance and inheritance
• Family based
– Traditional linkage analysis
– Allele sharing: IBS/IBD
• Population based
– Case-Control study
– Transmission disequilibrium test (TDT)
– Sibling control
CDD Course June 8, 2006
29
LD vs Risk Ratios
Linkage describes the
phenomenon whereby allele at
neighboring loci are close to one
another on the same
chromosome, they will be
transmitted together more
LOD
frequently than chance.
Two basic hypotheses are tested:
H0: free recombination, q = 1/2
H1: linkage, q < 1/2
where q is the recombination fraction
and
L is a likelihood function
score : Z(q) = log10 [L(q) / L(1/2)]
lod score > 3: evidence of linkage
2 < lod score < 3: suggestive evidence of linkage
-2 < lod score < 2: uninformative of linkage
lod score < -2: exclusion of linkage
Recurrence Risk Ratio (λ)
Disease
Frequency in relatives of affected person Cystic fibrosis
λr = ------------------------------------------------------Type I diabetes
schizophrenia
Population frequency
Type II diabetes
r denotes the degree of relationship
CDD Course June 8, 2006
λ s
500
15
8.6
3.5
30
Genetic Variance in Drug
Response
“…more than 90% of drugs
only work in 30-50% of
people.”
Allen D. Roses GlaxoSmithKline
CDD Course June 8, 2006
32
Where do drugs interact with proteins?
•
CDD Course June 8, 2006
33
Goldstein et al. Nature Rev. Gen. 4, 937 (2003)
Pharmacogenetic Basis for
Differences in Medicine Response
• 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
CDD Course June 8, 2006
34
Adverse Drug Reactions
Incidence and Cost
1973
28% of hospitalized patients
had adverse drug reactions
Miller Am J Hosp Pharm
30:584-592
1979
17% 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: 12001205
1995
Drug-related morbidity & mortality
estimated at $76.6 billion
Johnson & Bootman Arch Intern Med 1995; 155: 1949-56
2000
In 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
CDD Course JunePharmacoeconomics
8, 2006
21(9):623-50
35
Cytochrome P450 enzymes
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%)
CDD Course June 8, 2006
36
Meyer, Nature Reviews Genetics 2004
CDD Course June 8, 2006
37
The consequences of outlier cytochrome P450
CYP2D6-dependent drug metabolism.
CDD Course June 8, 2006
38
Other examples of metabolizing
enzymes
CDD Course June 8, 2006
39
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)
CDD Course June 8, 2006
40
Any examples?
Some Products where PGx
makes a difference
CDD Course June 8, 2006
42
Biotech 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
CDD Course June 8, 2006
43
b2 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
CDD Course June 8, 2006
44
Function 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
CDD Course June 8, 2006
45
ADRB2 Haplotypes
13 SNPs found in 12 haplotypes out of 8,192 possible haplotypes
Divergence between ethnic groups
CDD Course June 8, 2006
46
Phylogeny of ADRB2 Haplotypes
• Based only on the
multi-ethnic panel
• Some haplotypes
related to others by
recombination
CDD Course June 8, 2006
47
Functional Analysis of ADRB2 Haplotype Pairs
• Mean responses by
haplotype pair varied by >2fold
• 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
CDD Course June 8, 2006
48
Expression Analysis of ADRB2 Haplotypes
CDD Course June 8, 2006
49
How do we apply it?
Genetic Marker = Biomarker
• 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
CDD Course June 8, 2006
51
Mendelian
(Monogenic)
Disorders/Drug
response
Compared With
Peltonen and McKusick. Science. 2001;291:1224-1229.
CDD Course June 8, 2006
Complex
(Polygenic)
Disorders/Drug
response
52
Genotyping
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
CDD Course June 8, 2006
53
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
CDD Course June 8, 2006
54
Whole Genome Associations
Disease Population
N=500
Matched Control Population
N=500
~3,000,000 common SNPs across genome
• Representing every gene
1
22
P value
Regions of
association
1
Chromosomal Location
22
Informatics to ID gene(s) mapped to associated SNP
CDD Course June 8, 2006
55
Blocks vs Bins
56
Block Tags vs Bin Tags
• 6 Bin Tags
• 4 Haplotype Tags
57
Gene X Example
Source
HapMap
Perlegen
Journals
Total
SNPs
40
40
9
66*
Bins
23^
15'
8
28*
Bins missed
5
13
20
^ - 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.
58
Selection 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
59
Power Calculation
60
Selection 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
61
X gene structure
rs703748
rs4867798
32%
26%
J/C 38%
J/C 46%
+4067
+2203
rs12518222
13%
J/C 24%
+2934
rs686
40%
J/C 15%
+1402
rs1799914
J/C 10%
+198
02012006
rs2168631
14%
-5900
rs6878159
J/C 26%
-6299
CA
CHB/JPT
BOTH
rs6859970 rs267402 rs2471020
3%
3%
38%
-9891 -13001
J/C 17%
-18794
rs267405 rs1355077
46%
J/C 39%
-14450
-20211
rs1496133
J/C 44%
-23178
rs17065069
J/C 15%
-25300
rs6862721
28%
-25710
rs2644645
J/C 35%
-26084
62
Y gene structure 02072006
rs12629094
J/C 7%
-45787
rs346076
33%
J/C 35%
-8042
rs7625756
rs2606731
24%
J/C 27%
J/C 27%
-9109
-51007
rs4660646
J/C 14%
rs347594
-9553
35%
rs6773737
J/C 32%
J/C 27%
-51196
-35402
rs1874959 rs347596 rs11915050
22%
40%
16%
J/C 36% J/C 41% J/C 30%
-53464
-41217
-35491
rs12488410
4%
-54972
rs347606
J/C 20%
-36418
rs1018111 rs1809049 rs346070 rs2594966
30%
13%
18%
38%
J/C 31% J/C 32% +3151 +24552
-1202
+2161
rs346078 33% +27116
rs13081468 5% +28029
rs2606757 J/C 38% +57439
rs17034276 J/C 8% +58075
rs2594992 J/C 35% +60273
rs3816380 15% +81249
rs1375204 6% +86992
rs12630869 J/C 6% +89530
rs17534941 4% +127798
rs9310379 37% +133419
rs7621218 J/C 36% +141289
rs4684776 J/C 43% +142499
CA
rs9848833 J/C 8% +145801
rs11707842 22% +172140
rs9816564 J/C 37% +174573
JPT/CHB
rs7623889 4% +176663
rs9818393 J/C 14% +208944
rs7623147 3% +219309
BOTH
rs11128552 16% J/C 38% +239231
rs2442793 5% +246971
rs2443706 16% +249781
rs2447607 38% +251303
rs3856794 20% +261381
rs2344826 11% +261903
rs9876898 18% +267237
rs4684787 29% +27914163
CYP2D6 gene structure
02092006
rs4822075
10%
+176388
rs133337 rs9620018
rs1058172 rs11568728 rs1058164 rs5751232
rs5996135
rs134883
24%
10%
N/S%
N/S%
SYN%
19%
20%
24%
J/C 13% +77761
+3266
+1848
+1662
-25354
-102508
J/C 38%
+122253
-132537
rs6002561
rs2743467
rs9623531 rs3915951
rs9306356
rs12165846
rs4822100
48%
J/C 26%
33%
N/S%
8%
8%
8%
rs133330
+179744
+24430
+7250
+3158
-28140
-110348
-143836
J/C 18%
rs5751188rs1062753
rs2070903 rs5758589
rs738257
rs8142673 rs134871
rs112603
+129492
6%
31%
J/C 20%
45%
24%
4%
48%
J/C 30%
CA
J/C 44% J/C 6%
+21998 J/C 35%
-30348
-57303
-113766
-149135
+180908 +146137 rs133309
+8412
rs2142695rs17002868
rs5758686 rs134873
rs9611766
5%
6%
J/C 22%
24%
47%
20%
rs6002560 rs4822079
rs4467371
+140342
-42298
-45516
J/C
24%
J/C
39%
J/C
32%
CHB/JPT
33%
J/C 14%
J/C 42%
-116427 -118616
-149689
+182062
+144336
+16878
rs80506
BOTH
J/C 34%
-150190
64
Is it feasible? Are there risks?
How can we calculate the benefits?
Genetic Pedigree of World
Populations
Adapted from Cavalli-Sforza and Feldman. Nat Genet. 2003
CDD Course June 8, 2006
66
Haplotype block differences
among populations
CDD Course June 8, 2006
67
SNPs in different populations
CDD Course June 8, 2006
68
Effect of sampling in
association studies
CDD Course June 8, 2006
69
Genotyping children with ALL
CDD Course June 8, 2006
From : Veenstra et al., AAPS Pharmsci 2000;
70
A simple Health Economics Model
BCG report, 2001
CDD Course June 8, 2006
71
What is the prevalence of the
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
From : Veenstra et al., AAPS Pharmsci 2000;
CDD Course June 8, 2006
72
Hypothetical 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 smethyltransferase ) 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)
From : Veenstra et al., AAPS Pharmsci 2000;
CDD Course June 8, 2006
73
Genotype prevalence 0.3%
Deficient genotype prevalence 0.3%
$150,000
100000-150000
$100,000
50000-100000
Incremental costeffectiveness ratio
($/QALY)
0-50000
$50,000
$225
$150
$0
25%
$80
21%
17%
13%
Attributable mortality of severe
myleosuppresion
CDD Course June 8, 2006
9%
Cost of test
$5
5%
QALY, equivalent to 1 year of perfect health
From : Veenstra et al., AAPS Pharmsci 2000;
74
Genotype prevalence 1.0%
Deficient genotype prevalence 1.0%
$150,000
100000-150000
$100,000
50000-100000
Increm ental costeffectiveness ratio
($/QALY)
0-50000
$50,000
$225
$150
$0
25%
$80
21%
17%
13%
Attributable m ortality of severe
m yleosuppression
CDD Course June 8, 2006
9%
Cost of test
$5
5%
From : Veenstra et al., AAPS Pharmsci 2000;
75
Ethical issues to consider:
Anonymization
Anonymized
Samples
new tube,
remove SID 123,
123
XYZ
RELABEL
Sample
Secure Sample Storage
X
LINK
LINK
123~XYZ
… …
… …
… …
Subject 123
at Study Site
123~XYZ
… …
… …
… …
New Random ID (XYZ)
Generated in Secure File
XYZ
Anonymized
Genomic
Analysis
DELETE LINK
123
XYZ
Copy,
RELABEL,
Clinical data
CDD Course June 8, 2006
Remove SID 123 and
personal identifiers
(eg DOB)
Secure Data in
Pharmacogenomics
Database
Anonymized
Data
76
Predicting Treatment response
and outcome
• Each individual has a unique genetic makeup
• 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
CDD Course June 8, 2006
77
What areas will benefit most 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
CDD Course June 8, 2006
78
Pharmacogenetics and CNS
• There’s a need for better classification
of psychiatric disorders
• Modern life science approaches deliver
endophenotypes:
– Genetics/genomics
– Proteomics
– Imaging
– Etc.
CDD Course June 8, 2006
79
Endophenotypes
Gene
CDD Course June 8, 2006
Allele
Genotype
Endophenotpye Phenotype
80
Pharmacogenetics and imaging
The BDNF Val66Met polymorphism
Figure 1. Statistical maps of ttransformed 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
CDD Course June 8, 2006
81
The BDNF Val66Met polymorphism
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
Pezawas, L. et al. J. Neurosci. 2004;24:10099-10102
CDD
Course
Junefor8,Neuroscience
2006
Copyright
©2004 Society
82
The BDNF Val66Met polymorphism
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 zscores
Pezawas, L. et al. J. Neurosci. 2004;24:10099-10102
CDD
Course
Junefor8,Neuroscience
2006
Copyright
©2004 Society
83
Outlook for the future
Hype or Hope ?
PGx efforts in the world
CDD Course June 8, 2006
85
PGx alliances and numbers
CDD Course June 8, 2006
86
Commercial collaborations based on
pharmacogenetics
Clinical data integration and
personalized medicine
Source: IBM
CDD Course June 8, 2006
88
Integration of discplines
Source: IBM
CDD Course June 8, 2006
89
What do we still need?
• Better disease classification based on
endophenotypes
• Better (low level) data integration
• Better analysis methods (taking genegene interactions into account
• Better statistical methods
• Better trained computational scientists
in the field of Molecular Medicine
CDD Course June 8, 2006
90
Thank you for listening
CDD Course June 8, 2006
91