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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 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 !!