Transcript Patrinos_UAEU_Dec2014

Next generation
Pharmacogenomics
George P. Patrinos
Associate Professor; University of Patras, Department of Pharmacy, Patras, Greece
Member and National Representative, CHMP Pharmacogenomics
Working Party, European Medicines Agency, London, UK
CHMP Pharmacogenomics Working Party (PGWP)
Disclaimer
Declared conflict of interests: None
The opinions expressed in this presentation do not reflect the policies
and position of the European Medicines Agency
Pharmacogenomics
Exploitation of an individual’s genetic profile to determine
his/her response to a certain drug, in terms of both efficacy
and toxicity, towards achieving individualized (personalized)
therapy.
Historical perspective
Hippocrates (4th century B.C.):
«It is more important to know what kind of person suffers from a
certain disease than knowing from which disease somebody suffers»
F. Vogel (1959): Introduction of the term «pharmacogenetics».
φάρμακον (phármacon) = drug
Homer, Odyssey, ~800 B.C.
“…drugs can be either therapeutic or
poisonous…”
Key points
- Pharmacogenomics for hemoglobinopathies
- Pharmacogenomics in developing countries
- Pharmacogenomics and whole genome sequencing
Pharmacogenomics in developing countries
• Modern medical therapy is a key component of improved health.
• Selection of medications for each indication is a combination of
clinical consensus and access/cost of drugs.
• Medicine prioritization is a high stakes undertaking for developing
countries.
• Drugs are primarily developed in European-derived patients (USA,
Europe, Canada, Australia/New Zealand, South America), consisting
of the source of global safety and dosing information.
• However, very little is known about how drugs will be used
throughout the world.
• Most ‘ethnic differences’ in drug response are based on anecdote
(Drug 'x' doesn't seem to work for Ghanaians) and often on few
patients, although with wide influence.
Pharmacogenomics in developing countries
Stated goals
• To promote the integration of genetic information into the public
health decision making process.
• To enhance the understanding of pharmacogenomics in
developing countries.
• To provide guidelines for medication prioritization for individual
countries, using pharmacogenomic information.
• To facilitate building of local infrastructure for future
pharmacogenomic research studies.
Pharmacogenomics in developing countries
Overview of the study plan
• Collection of 50 (1st tier) or 500 (2nd tier) DNA samples primarily
from each developing nation and also developed countries in Europe.
Only gender, ethnicity, and age are recorded for each sample to
maintain anonymity.
• Genotyping for pharmacogenomically-relevant variants (after data
mining for validated SNPs in key genes).
• Generation of recommendations for medication selection.
• Engage in education and outreach activities to inform the general
public and the healthcare professionals.
Genes & markers in DMET+
1,936 functional mutations in 231 pharmacogenes
CYP2D6
CYP2C9
CYP1A1
CYP1B1
CYP2C19
CYP4F2
MDR1
ABCC1
ABCG2
SLCO1A2
SULT1A1
SULT4A1
50
45
CYP450
enzymes
Phase II
enzymes
478
408
64
66
Drug
transporters
Transcription
regulators &
other enzymes
637
413
DPYD
NAT1
NAT2
GSTT1
UGT1A1
UGT1A9
PPARD
PPARG
AHR
ARNT
RXRA
NR1I2
Pharmacogenomics in developing countries in Europe
Pharmacogenomics 2012, 13(4): 387-392.
Maltese population
Serbian population
Dalabira et al., unpublished
The Global Pharmacogenomics Map
McLeod HL, Patrinos GP et al., subm.
Clinically relevant pharmacogenomics profiles
Warfarin
Simvastatin
Amodiaquine
McLeod HL, Patrinos GP et al., subm.
Distribution of predicted warfarin dose
McLeod HL, Patrinos GP et al., subm.
Customized pharmacogenomic testing platforms
for developing countries
 European populations display significant differences in >130
pharmacogenomic biomarkers each.
 Replication of these findings in larger population samples to
establish common grounds for pharmacogenomic testing in
developing countries.
Pharmacogenomics and
Whole Genome Sequencing
Is the analysis of known pharmacogenomics
markers in known pharmacogenes enough to
determine one’s personalized pharmacogenomics
profile?
NO
Whole Genome Sequence Analysis
Existing pharmacogenomic testing platforms
• Polymerase Chain Reaction-based
 Home-brew
 CE-IVD mark
• Microarray-based methods
 AmpliChip CYP450 assay (Roche)
 DMET™ plus assay (Affymetrix)
Pharmacogenomics and Whole Genome
Sequencing
• Pilot: 69 whole genomes (CG69 collection)
• Follow-up: 413 whole genomes (adult Caucasians)
 All genomes were sequenced 110x using the CG platform (DNB)
 Analysis of all variants (known and novel in ADMET-related
genes; Inclusion of variants with the highest quality score only)
 In silico analysis of novel variants
 Independent whole-genome sequence analysis of a 7-member
Greek family in the ADMET-related genes
Functional variants in the entire 482
genomes collections
Our analysis in the 231 ADMET-related genes revealed:
 408,951 variants that are unique or in varying frequencies
 26,807 variants in exons and proximal regulatory regions
 18,058 variants in each individual
 16,485 novel (not annotated in dbSNP) potentially functional
variants in the entire genome (961 variants with freq >1%)
 4,480 novel (not annotated in dbSNP) potentially functional
variants in the exome.
Mizzi et al., Pharmacogenomics, submitted (revised)
Functional variants in the entire 482
genomes collections
Our analysis in the 231 ADMET-related genes revealed:
 408,951 variants that are unique or in varying frequencies
 26,807 variants in exons and proximal regulatory regions
 18,058 variants in each individual
 16,485 novel (not annotated in dbSNP) potentially functional
variants in the entire genome (961 variants with freq >1%)
 4,480 novel (not annotated in dbSNP) potentially functional
variants in the exome.
 Several variants in CYP2D6, CYP2C9, CYP2C19, VKORC1, and TPMT
likely to have a damaging effect on the protein (Sift algorithm)
Mizzi et al., Pharmacogenomics, submitted (revised)
Novel variants in the key pharmacogenes
Total Variants
Novel variants
Mizzi et al., Pharmacogenomics, submitted (revised)
Novel variants in the key pharmacogenes
CYP2D6
Total Variants
Novel variants
Variants with freq>20%
DMET variants
Mizzi et al., Pharmacogenomics, submitted (revised)
Pharmacogenomics and Whole Genome
Sequencing – Ethnic differences
69 publicly available Genomes
Ethnicity
ASW
CEU
CHB
GIH
JPT
LWK
MKK
MXL
TSI
YRI
PUR
CEPH/UTAH
Ethnicity Name
African ancestry in Southwest USA
Utah residents with Northern and Western European
Han Chinese in Beijing, China
Gujarati Indian in Houston, Texas, USA
Japanese in Tokyo, Japan
Luhya in Webuye, Kenya
Maasai in Kinyawa, Kenya
Mexican ancestry in Los Angeles, California
Toscans in Italy
Yoruba in Ibadan, Nigeria
Puerto Rican in Puerto Rico YRI
Utah residents with Northern and Western
European ancestry from the CEPH collection
Number of Genomes
5
5
4
4
4
4
4
5
4
10
3
17
© Complete Genomics Inc., USA
Population differences in the CG69
genome collection
DMET Coverage
32
Percentage(%)
31
30
29
28
27
26
25
24
Mizzi et al., Pharmacogenomics, submitted (revised)
Population differences in the CG69
genome collection
Known / Novel variant ratio
18
16
14
12
10
8
6
4
2
0
Mizzi et al., Pharmacogenomics, submitted (revised)
In silico analysis: CYP2D6
Mizzi et al., Pharmacogenomics, submitted (revised)
In silico analysis: TPMT
Mizzi et al., Pharmacogenomics, submitted (revised)
Personalized Pharmacogenomics Profiling
and family genomics
Mizzi et al., Pharmacogenomics, submitted (revised)
Genotype-phenotype correlation
12
INR-No 4
11
INR-No 7
10
9
8
INR
7
6
5
4
3
2
1
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Months
Acenocoumarol dosing schemes:
 No 4: 4-9 mg/week (depending on last measurement),
 No 7: 6 mg/week (stable for the last 36 months)
Mizzi et al., Pharmacogenomics, submitted (revised)
Genotype-phenotype correlation
 Average DMET+ coverage: 36.18%
 236 potentially functional (exonic, proximal regulatory) novel
(not annotated in dbSNP) variants
Comparison between No 4 & No 7:
 33.44% of potentially functional variants found in No 4 but not in
No 7, including variants in SLCO1B1, UGT1A5, and other
pharmacogenes
 34.29% of potentially functional variants found in No 7 but not in
No 4, including variants in UGT1A1, CYP3A5, ABCB1, ABCG2,
CYP2B6, and other pharmacogenes
Mizzi et al., Pharmacogenomics, submitted (revised)
Genotype-phenotype correlation
Mizzi et al., Pharmacogenomics, submitted (revised)
Genotype-phenotype correlation
 Average DMET+ coverage: 36.18%
 236 potentially functional (exonic, proximal regulatory) novel
(not annotated in dbSNP) variants
Comparison between No 4 & No 7:
 33.44% of potentially functional variants found in No 4 but not in
No 7, including variants in SLCO1B1, UGT1A5, and other
pharmacogenes
 34.29% of potentially functional variants found in No 7 but not in
No 4, including variants in UGT1A1, CYP3A5, ABCC1, ABCG2,
CYP2B6, and other pharmacogenes
Patient No. 4 could ALTER anticoagulation therapy to
clopidogrel to minimize adverse reactions
Patient No. 7 should NOT be treated with clopidogrel
Mizzi et al., Pharmacogenomics, submitted (revised)
Pharmacogenomics research
Economic evaluation in
genomic medicine
Educating healthcare
professionals
Increasing genetics
awareness to the public
Genome informatics
Genethics
Public Health Genomics
From Pharmacogenomics to Genomic Medicine
Genomic Medicine
Aims to build/strengthen collaboration ties between academics,
researchers, regulators, and the general public interested in all aspects of
genomic medicine, focusing in particular on translating results from
research into clinical practice.
www.genomicmedicinealliance.org
Towards integrated Pharmacogenomics IT services
Potamias et al., in preparation
• Search for PGx
information
• Receive personalized
PGx recommendations
• Access personal PGx
information
Administrator
Administrator
Patient
Towards integrated pharmacogenomics IT services
• Install, upgrade and
maintain the DB server
and application tools
• Maintain system security
• Control and monitor user
access
• Submit new alleles
• Submit new variations
• Update alleles
• Update variations
• Search for PGx
information
eMoDiA*
PGx
Medical Professional
Data submitter
• Back up and restore data
• Search for PGx
information
• Access patient data
upon authorization
• Review PGx
recommendations
Potamias et al., in preparation
Towards integrated pharmacogenomics IT services
Patient_i
Patient_i Recommendation table
(Clinical Annotations / Dosing Guidelines)
Genotype
profile
Variation_1
C/C
Variation_2
C/T
Variation_3
-
Drug_1
Drug_2
Drug_3
-
-
-
-
Gene_1
Gene_2
-
Gene_3
…
Drug_n
-
…
…
Variation_n
Gene_n
G/G
-
-
-
Drug_1
Drug_2
Drug_3
…
Drug_n
Gene_1
IM
IM
-
IM
Gene_2
PM
-
-
EM
Gene_3
PM
PM
PM
PM
-
IM
EM
-
…
Gene_n
Retrieve DB
information
Allele
Matching
Translation
algorithm
Patient_i Phenotype table
Potamias et al., in preparation
Towards integrated pharmacogenomics IT services
ATTGCTTAGTCTAGGTC
TGGCTATTGCGCATTGC
TATCGTCAGGCTATCGA
CTATCGATTCAGTCTGG
GCTATCTGCGGATAAAA
TTTGCTAAAAAAATTGC
TTACGCATTCGAGTTAG
CATGCATTCAGCTATCG
ATCATCGATCATCGAGT
……………………………………………
CATGGGTTGTTGCATCT
GAGCTATGGCTTAGCGT
11010001010000100
00010100010001010
01000100010100001
11000100111101100
10010101010001000
00010100010000010
01000111111101001
01001011110011100
10010001111001001
……………………………………………
01001111010001100
10010001010101000
Potamias et al., in preparation
Policy maker and stakeholder analysis
We used the computerized version of the PolicyMaker political
mapping tool to collect and organize important information about
the pharmacogenomics and genomic medicine policy environment
in GREECE, serving as a database for assessments of the policy’s
content, the major players, their power and policy positions, their
interests and networks and coalitions that interconnect them.
Mitropoulou et al., Public Health Genomics, 2014 (in press)
Policy maker and stakeholder analysis
Mitropoulou et al., Public Health Genomics, 2014 (in press)
Stakeholder analysis
Mitropoulou et al., Public Health Genomics, 2014 (in press)
Mapping the Pharmacogenomics educational
environment in Europe
175 Departments in 98 Universities
Under-graduate curricula
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Stand-alone course on pharmacogenomics
Courses discussing pharmacogenomics
Pisanu et al., Public Health Genomics, 2014 (in press)
Mapping the Pharmacogenomics educational
environment in Europe
175 Departments in 98 Universities
Post-graduate curricula
0.50
0.40
0.30
0.20
0.10
0.00
Stand-alone course on pharmacogenomics
Courses discussing pharmacogenomics
Pisanu et al., Public Health Genomics, 2014 (in press)
The economics of pharmacogenomics
102 patients
104 patients
Mitropoulou et al., Pharmacogenomics, 2015 (in press)
The economics of pharmacogenomics
No Major
PGx Group
Tc days
Tmd days
B-Mean
5.65
10.35
97.07%
B-SD
0.12
0.16
1.39%
B-Mean
7.11
13.87
89.12%
B-SD
0.16
0.23
2.53%
Complications
N-PGx Group
Mitropoulou et al., Pharmacogenomics, 2015 (in press)
The economics of pharmacogenomics
Cost of
Cost of Bleeding
Cost of INR
B-Mean
28.07 €
17.95 €
1.40 €
140.25
187.68 €
B-SD
15.72 €
0.14 €
0.04 €
-
15.74 €
B-Mean
147,39 €
23,16 €
1,53 €
-
172,07 €
B-SD
39,04 €
0,19 €
0,02 €
-
39,03 €
warfarin
Cost of Test Total Cost
PGx Group
N-PGx Group
Cost Differences (N-PGx vs PGx)
B-Mean
119,32 €
5,20 €
0,12 €
-140.25
-15,60 €
B-SD
40,43 €
0,25 €
0,05 €
-
40,43 €
Mitropoulou et al., Pharmacogenomics, 2015 (in press)
The economics of pharmacogenomics
100,0%
90,0%
80,0%
70,0%
31000 EUR per QALY
60,0%
*
50,0%
40,0%
30,0%
20,0%
10,0%
0,0%
0€
10.000 €
20.000 €
30.000 €
40.000 €
50.000 €
60.000 €
70.000 €
80.000 €
90.000 € 100.000 €
Wilingness To Pay for a QALY
Mitropoulou et al., Pharmacogenomics, 2015 (in press)
The future before and after
Pharmacogenomic testing
Kampourakis et al., EMBO Rep, 2014 15(5):472-476.
Acknowledgements
The gang
Collaborators
• Joseph Borg
• Clint Mizzi
• Petros Papadopoulos
• Christina Tafrali
• Marina Bartsakoulia
• Theodora Katsila
• Marianna Georgitsi
• Milena Radmilovic
• Argyro Sgourou
• Katerina Gravia
Funding
• Vicky Hondrou
• Sjaak Philipsen (EMC)
• Frank Grosveld (EMC)
• Marina Kleanthous (CING)
• Howard Mc Leod (Moffitt)
• Alison Motslinger (UNC)
• Sonja Pavlovic (IMGGE)
• Rade Drmanac (CGI)
• George Potamias (FORTH)
Sources:
Ευχαριστώ πολύ !!