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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
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Declared conflict of interests: None
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
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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.
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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».
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φάρμακον (phármacon) = drug
Homer, Odyssey, ~800 B.C. “…drugs can be either therapeutic or poisonous…”
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Key points Pharmacogenomics for hemoglobinopathies
Pharmacogenomics in developing countries Pharmacogenomics and whole genome sequencing
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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.
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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.
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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.
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Genes & markers in DMET+
1,936 functional mutations in 231 pharmacogenes 50 CYP450 enzymes DPYD NAT1 NAT2 GSTT1 UGT1A1 UGT1A9 CYP2D6 CYP2C9 CYP1A1 CYP1B1 CYP2C19 CYP4F2 45 Phase II enzymes 478 408 66 Transcription regulators & other enzymes MDR1 ABCC1 ABCG2 SLCO1A2 SULT1A1 SULT4A1 PPARD PPARG AHR ARNT RXRA NR1I2 64 Drug transporters 637 413
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Pharmacogenomics in developing countries in Europe
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Maltese population Serbian population
Dalabira et al., unpublished
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The Global Pharmacogenomics Map
McLeod HL, Patrinos GP et al., subm.
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Clinically relevant pharmacogenomics profiles
Warfarin Simvastatin Amodiaquine McLeod HL, Patrinos GP et al., subm.
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Distribution of predicted warfarin dose
McLeod HL, Patrinos GP et al., subm.
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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.
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Whole Genome Sequencing Whole Genome Sequence Analysis
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
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Existing pharmacogenomic testing platforms
Polymerase Chain Reaction-based Home-brew CE-IVD mark Microarray-based methods AmpliChip CYP450 assay (Roche) DMET™ plus assay (Affymetrix)
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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
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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)
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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)
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Novel variants in the key pharmacogenes
Total Variants Novel variants Mizzi et al., Pharmacogenomics, submitted (revised)
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Novel variants in the key pharmacogenes
CYP2D6 Total Variants Novel variants Variants with freq>20% DMET variants Mizzi et al., Pharmacogenomics, submitted (revised)
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Pharmacogenomics and Whole Genome Sequencing – Ethnic differences
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69 publicly available Genomes
Ethnicity Ethnicity Name Number of Genomes ASW African ancestry in Southwest USA 5 CEU Utah residents with Northern and Western European CHB Han Chinese in Beijing, China 4 GIH Gujarati Indian in Houston, Texas, USA JPT Japanese in Tokyo, Japan LWK Luhya in Webuye, Kenya MKK Maasai in Kinyawa, Kenya MXL Mexican ancestry in Los Angeles, California TSI Toscans in Italy YRI Yoruba in Ibadan, Nigeria 10 PUR Puerto Rican in Puerto Rico YRI 3 CEPH/UTAH Utah residents with Northern and Western European ancestry from the CEPH collection 17 © Complete Genomics Inc., USA
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Population differences in the CG69
genome collection Mizzi et al., Pharmacogenomics, submitted (revised)
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Population differences in the CG69
genome collection Mizzi et al., Pharmacogenomics, submitted (revised)
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In silico analysis: CYP2D6
Mizzi et al., Pharmacogenomics, submitted (revised)
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In silico analysis: TPMT
Mizzi et al., Pharmacogenomics, submitted (revised)
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Personalized Pharmacogenomics Profiling
and family genomics Mizzi et al., Pharmacogenomics, submitted (revised)
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Genotype-phenotype correlation
INR 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)
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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)
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Genotype-phenotype correlation
Mizzi et al., Pharmacogenomics, submitted (revised)
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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)
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From Pharmacogenomics to Genomic Medicine
Public Health Genomics Genethics Genome informatics Increasing genetics awareness to the public Educating healthcare professionals Economic evaluation in genomic medicine Pharmacogenomics research
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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.
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Towards integrated Pharmacogenomics IT services
Potamias et al., in preparation
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Towards integrated pharmacogenomics IT services
Administrator Install, upgrade and maintain the DB server and application tools Maintain system security Control and monitor user access Back up and restore data Patient Search for PGx information Receive personalized PGx recommendations Access personal PGx information Administrator eMoDiA* PGx Data submitter Submit new alleles Submit new variations Update alleles Update variations Search for PGx information Medical Professional Search for PGx information Access patient data upon authorization Review PGx recommendations Potamias et al., in preparation
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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 - … Variation_n G/G Drug_1 Drug_2 Drug_3 … Drug_n Gene_1 - Gene_2 Gene_3 Gene_n Patient_i Phenotype table Drug_1 Drug_2 Drug_3 … Drug_n Gene_1 IM - Gene_2 PM EM Gene_3 Gene_n Translation algorithm Allele Matching Retrieve DB information Potamias et al., in preparation
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Towards integrated pharmacogenomics IT services
ATTGCTTAGTCTAGGTC TGGCTATTGCGCATTGCTATCGTCAGGCTATCGACTATCGATTCAGTCTGGGCTATCTGCGGATAAAATTTGCTAAAAAAATTGCTTACGCATTCGAGTTAGCATGCATTCAGCTATCGATCATCGATCATCGAGT……………………………………………CATGGGTTGTTGCATCTGAGCTATGGCTTAGCGT …………………………………………… Potamias et al., in preparation
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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)
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Policy maker and stakeholder analysis
Mitropoulou et al., Public Health Genomics, 2014 (in press)
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Stakeholder analysis Mitropoulou et al., Public Health Genomics, 2014 (in press)
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Mapping the Pharmacogenomics educational environment in Europe
175 Departments in 98 Universities Under-graduate curricula Pisanu et al., Public Health Genomics, 2014 (in press)
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Mapping the Pharmacogenomics educational environment in Europe
175 Departments in 98 Universities Post-graduate curricula Pisanu et al., Public Health Genomics, 2014 (in press)
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The economics of pharmacogenomics
102 patients 104 patients Mitropoulou et al., Pharmacogenomics, 2015 (in press)
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The economics of pharmacogenomics No Major Complications
PGx Group Tc days Tmd days No Major Complications B-Mean 5.65 10.35 97.07% B-SD 0.12 0.16 1.39% N-PGx Group 7.11 13.87 89.12% 0.23 2.53% Mitropoulou et al., Pharmacogenomics, 2015 (in press)
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The economics of pharmacogenomics
Cost of Bleeding Cost of INR Cost of warfarin Cost of Test Total Cost PGx Group B-Mean 28.07 € 17.95 € 1.40 € 140.25 € B-SD 15.72 € 0.14 € 0.04 € - 15.74 € N-PGx Group 147,39 € 23,16 € 1,53 € 172,07 € 39,04 € 0,19 € 0,02 € 39,03 € Cost Differences (N-PGx vs PGx) 119,32 € 5,20 € 0,12 € -15,60 € 40,43 € 0,25 € 0,05 € Mitropoulou et al., Pharmacogenomics, 2015 (in press)
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The economics of pharmacogenomics
31000 EUR per QALY * Mitropoulou et al., Pharmacogenomics, 2015 (in press)
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The future before and after Pharmacogenomic testing
Kampourakis et al., EMBO Rep, (5):
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Acknowledgements The gang Collaborators Funding Sources: Joseph Borg
Clint Mizzi Petros Papadopoulos Christina Tafrali Marina Bartsakoulia Theodora Katsila Marianna Georgitsi Milena Radmilovic Argyro Sgourou Katerina Gravia Vicky Hondrou Collaborators 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) Funding Sources:
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Ευχαριστώ πολύ !!
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