Transcript Diapositiva 1

Prospettive della
farmacogenetica e della
farmacogenomica
Stefano Vella
Dipartimento del Farmaco
Istituto Superiore di Sanità
Pharmacogenetics
& Pharmacogenomics
• An opportunity to improve drug development
• Choice of drug targets
• An opportunity to improve clinical care
• Individualized medicines through stratification
• More rational decisions on therapeutic options
Pharmacogenetics
& Pharmacogenomics
• An opportunity to improve drug development
• Choice of drug targets
• An opportunity to improve clinical care
• Individualized medicines through stratification
• More rational decisions on therapeutic options
Druggable Genome Predictions
Druggability Prediction Method
GPCRs
No. Molecular
Targets
Targets of approved NCEs
170
Sequence homology to NCE
drug targets
945
21%GPCRs
21%
32%
Kinases
GPCRs
Kinases
Proteases
Proteases
Transporters
32%
Transporters
Ion Channels
Ion Channels
Transferases
Transferases
Targets of chemical leads with
activities (binding affinities)
below 10uM
707
Targets of Ro5 chemical leads
with activities (binding affinities
<= 10uM)
587
Sequence homology to targets
with chemical leads*
2921
Feature-based druggability
sequence probability prediction
2325
Structured-based prediction
427
Sequence homology to proteins
predicted druggable by
structure-based method
3541
Predicted Drugglable
Genome (small molecules)
3505
Human Genome
24000
Other
Enzymes
Other
Enzymes
Kinases
15%
15%
Phosphatases
Phosphatases
Cytochrome
Cytochrome
P450P450
Nuclear Hormone Recep
Nuclear Hormone Receptors
Phospholipases
Phospholipases
Phosphodiesterases
1%
1%
Proteases
2%
7%
3%
2%
6%
5%
Ion Channels
3%
5%
Transporters
7%
6%
Phosphodiesterases
Other Receptors
CellReceptors
Adhesion
Other
Chemokines
Cell
Adhesion
Other/Unclassified
Chemokines
Other/Unclassified
Gene family distributions of predicted druggable genome
Future Drug Target Space
Human Genome
24000
6465
~2400
~145 160
170
578
~320
1769
*Zambrowicz & Sands, Nature Drug Disc. Rev. (2003), 2,38-51C
**Genetic association linkage data estimated by text-mining from entity co-occurrence within Medline abstracts. Data
produced by Anna Gaulton and Andrew Hopkins, using a modified version of Lucene, by Lee Harland, to text-mine Medline,
3505
Accessible Genome: protein therapeutics
Druggability Prediction Method
No. of
Molecular
targets
Targets of approved antibodies
15
Targets of approved biologicals
59
Secreted protein (high confidence)
1384
Secreted proteins (low confidence)
6560
Transmembrane predictions (high
confidence)
973
Transmembrane predictions (low
confidence)
1407
Unique, combined transmembrane
and secreted predictions (high
confidence)
2287
Feature-based biological target
sequence probability prediction
1637
Total unique genes predicted
to be accessible via protein
therapeutics
3258
1516 genes likely to encode proteins
drugable by both small molecules and
protein therapeutics
Total number druggable by
protein therapeutics
Total number druggable
by small molecule
= 3258 genes
therapeutics
= 3505 genes
Human
Genome
24000
Stages of HIV-1 Life Cycle
Targeted by Anti-HIV Drugs
In: Gulick RM, Topics HIV Med, 2002;10(4).
The International AIDS Society–USA
Chemokine Co-receptors in HIV
Entry
• HIV gains entry into cells that express CD4 and
1 of 2 secondary receptors, either:
– C-C chemokine receptor 5 (CCR5)
• Expressed on monocytes and T cells
– C-X-C chemokine receptor 4 (CXCR4)
• Expressed on T cells, B cells, monocytes, and
neutrophils
1Deng
H, et al. Nature. 1996;381(6584):661-666.
Y, et al. Science. 1996;272:872-877.
2Feng
HIV Attachment and Fusion
Targets for Inhibition
CD4
Binding
CD4
binding
inhibitors
Co-receptor
Binding
gp41
Virus-Cell
Fusion
CCR5
antagonists
Fusion
inhibitors
gp120
V3 loop
CD4
Cell
Membrane
CCR5/CXCR4
(R5/X4)
Adapted from Moore JP, et al.
Proc Natl Acad Sci U S A. 2003;100:10598-10602.
CCR5 D32
CCR5 wild type 
 CCR5 D32
Normal
Heterozygotes
Homozygotes
wt/wt
wt/D32
D32/D32
2 normal copies
1 copy of D32
2 copies of D32
Delayed disease
progression
“Resistant” to
HIV infection
Standard disease
progression
CCR5-tropic HIV (R5 virus)
• Nearly all new sexually transmitted HIV-1 infections
are with R5 virus
• Infect dendritic cells, macrophages, and T cells
• Predominate throughout infection
– X4 virus may appear over time, but ~50% of
patients with HIV-1 subtype B who die from
AIDS have only R5 virus1,3
– A tropism shift from R5 to X4 virus is associated
with the presence of basic amino acids at
codons 11 and/or 25 of the V3 loop of gp1204
Maraviroc (UK-427,857) Activity Results:
Mean Reduction in Viral Load over Time
Last day of dosing
Change from baseline
(log10 HIV-1 copies/mL)
0.5
0.0
n
Maraviroc dose
Placebo 015
4
Placebo 007
12
25 mg QD
8
50 mg BID
8
100 mg QD
8
100 mg BID
7
150 mg BID Fast 8
150 mg BID Fed 8
300 mg QD
8
300 mg BID
8
-0.5
-1.0
-1.5
-2.0
Baseline 5
10
15
20
Time (day)
Study 1007/1015
25
30
35
40
Pharmacogenetics
& Pharmacogenomics
• An opportunity to improve drug development
• Choice of drug targets
• Optimization of clinical trials
• An opportunity to improve clinical care
• Individualized medicines through stratification
• More rational decisions on therapeutic options
Are Drugs Effective?
Disease
Efficacy
Alzheimer’s
Analgesics
Cardiac arrhythmia
Depression
Diabetes
Hepatitis C
Incontinence
Migraine
Oncology
30%
80%
60%
60%
55%
45%
40%
50%
25%
Annual Rx Cost
$ 1,500
$ 1,350
$ 650
$ 700
$ 1,300
$ 5,000
$ 1,000
$ 600
$ 3,500
Prescribed drugs are generally effective in
about 50% of patients.
Are Drugs Safe?
Adverse drug reactions (ADRs)
represent the 4th leading cause of
hospitalization (2 million/yr) and are
responsible for 100,000 deaths/yr in
the U.S.
Pharmacogenetics of Phase I Drug Metabolism
Weinshilboum R. N Engl J Med 2003;348:529-537
Pharmacogenetics of Nortriptyline
Weinshilboum R. N Engl J Med 2003;348:529-537
Pharmacogenetics of Acetylation
Weinshilboum R. N Engl J Med 2003;348:529-537
Genetic Polymorphisms in Drug Target Genes That Can Influence Drug Response
Evans W and McLeod H. N Engl J Med 2003;348:538-549
Tamoxifen and Breast Cancer
• 1971: some breast tumors express the estrogen
receptor (ER), which drives tumor growth
• Tamoxifen (ER receptor antagonist) was first
administered regardless of tumor ER status
• Ligand binding assay for ER status introduced –
complex assay requiring fresh tissue
• Immunohistochemical assays – variable results
1995
ErbB2 expression
is associated with metastatic
breast cancer!
Reactivity on tumour samples
About 25-30% of women who have metastatic breast cancer
overexpress HerB2(EGF) receptor
Relapse-free Survival (Panel A) and Overall Survival (Panel B) among Women with Breast Cancer,
According to HER2 Amplification Status on FISH
Pritchard K et al. N Engl J Med 2006;354:2103-2111
Herceptin (TrastuzuMAb)
(anti-HER MAbs)
1999:Approved
HER-2/neu Genetic Test
Current genetic testing uses fluorescence markers (FISH
technology) – look for increased copies of HER-2/neu gene
with fluorescent DNA probes – labor-intensive and expensive
Gene amplified
Normal
HER-2/neu positive patients
Most responsive to therapy
HER2 testing is covered by and required for most drug benefit plans
Real-time quantitative PCR for detection of
HER-2/neu gene amplification
10-fold amplified Her-2/neu
non-amplified Her-2/neu
Polygenic Determinants of Drug Response
Evans W and McLeod H. N Engl J Med 2003;348:538-549
Potential of pharmacogenetics: the right
dose of the right drug, the first time
All patients with same diagnosis
Non-responders
and toxic
responders
Treat with alternative
drug or dose
Responders and patients
not predisposed to toxicity
Treat with
conventional
drug or dose
Possible Designs for
Pharmacogenomic
Clinical Trials
Retrospective Design
Randomized, Double Blind, Placebo-controlled Trial
Treatment period
Placebo
Genetic
analysis
Drug A
(+)
(-)
(+)
(-)
entry
sampling



Patient numbers in a arm maybe unbalanced?
Sampling maybe limited in some patients?
Results are not confirmative?
→ Confirmative trial would be necessary
Prospective Design 1
Drug A
randomized
Therapy as usual
Treatment period
sampling
Genetic
analysis
(+)
(-)


To test clinical utility
 PGx test is really necessary?
 Cost-benefit relationship
Results are confirmative
Prospective Design 2
(+)
Genetic
analysis
randomized
Randomized, Double Blind, Placebo-control Trial
Treatment period
Placebo
Drug A
(-) No entry
sampling




Enrichmen
t
approach
Increase analytical power of trial
Results are confirmative
But, data in gene(-) patients can not be obtained
May lose a chance of treatment for (-) patients
Prospective Design 3
Randomized, Double Blind, Placebo-control Trial
Treatment period
Placebo
(+)
randomized
Drug A
Genetic
analysis
Placebo
(-)
Drug A
sampling
Benefit of Pharmacogenomics
• Improving benefit/risk ratio
– More safe, more effective drugs
• Adjusting Dose
– Determine the best dose
• Increasing successful rate of clinical trials
– Focusing on data in responder
More drugs, more appropriate
•Except for monozygotic twins, each person's
genome is unique.
•All physicians will soon need to understand
the concept of genetic variability, its
interactions with the environment, and its
implications for patient care.
•With the sequencing of the human genome,
the practice of medicine has now entered an
era in which the individual patient's genome
will help determine the optimal approach to
care, whether it is preventive, diagnostic, or
therapeutic.
A possible future….
1. Doctor Examines Patient
and Makes Initial Diagnosis
2. Laboratory Buccal Swab or
Blood Sample
Maximum Density Score at Allele
Blood
Sample
3. Genetic Analysis
Haplotype
pairs
-654
0.30
G/A
-367
-1.90
T
-47
-2.70
T
+46
-0.10
A/G
+491
3.71
C
+523
0.23
C/A
B
3.20
G
3.20
C
2.50
C
-2.90
G
3.78
C
3.36
C
2/2
C
3.20
G
-0.20
C/T
0.10
C/T
-2.90
G
3.75
C
0.19
C/A
2/6
D
0.20
G/A
-2.00
T
-2.80
T
-0.10
A/G
3.61
C
0.15
C/A
4/6
E
-3.00
A
-2.00
T
-3.00
T
1.90
A
3.74
C
3.46
C
4/4
F
3.30
G
-0.20
C/T
0.20
C/T
-2.80
G
3.71
C
0.19
C/A
2/6
G
0.20
G/A
-0.10
C/T
0.10
C/T
0.10
A/G
3.68
C
3.40
C
2/4
H
3.40
G
3.80
C
2.50
C
-2.30
G
3.66
C
3.66
C
2/2
A
4/6
Drug Prescribing Based on the Patient’s
Genetic Markers
20
Maximum Density Score at Allele
Blood
Sample
-654
0.30
G/A
-367
-1.90
T
-47
-2.70
T
+46
-0.10
A/G
+491
3.71
C
+523
0.23
C/A
Varient
B
3.20
G
3.20
C
2.50
C
-2.90
G
3.78
C
3.36
C
2/2
C
3.20
G
-0.20
C/T
0.10
C/T
-2.90
G
3.75
C
0.19
C/A
2/6
D
0.20
G/A
-2.00
T
-2.80
T
-0.10
A/G
3.61
C
0.15
C/A
4/6
E
-3.00
A
-2.00
T
-3.00
T
1.90
A
3.74
C
3.46
C
4/4
8
F
3.30
G
-0.20
C/T
0.20
C/T
-2.80
G
3.71
C
0.19
C/A
2/6
6
G
0.20
G/A
-0.10
C/T
0.10
C/T
0.10
A/G
3.68
C
3.40
C
2/4
4
H
3.40
G
3.80
C
2.50
C
-2.30
G
3.66
C
3.66
C
2/2
2
A
4/6
18
16
14
12
10
0
-5.0
10.0
NonResponders
25.0
40.0
55.0
Responders