e-PGA: an integrated electronic Pharmacogenomics Assistant for Personalized Medicine www.emodia.gr K. Lakiotaki1, E. Kartsaki1, A. Kanterakis1, T. Katsila2, G. P. Patrinos2, G. Potamias1 1Institute of Computer Science, Foundation for Research & Technology – Hellas, Heraklion, Crete 2Department of Pharmacy, University of Patras, Hellas The Clinical Problem eMoDiA: electronic Molecular Diagnostic Assistant Pharmacogenomics (PGx) holds promise to personalize medical interventions by determining genetic influence in drug response and enabling tailor-made drug prescription according to an individual’s genetic makeup. I am about to prescribe fluoropyrimidine to a patient who is Poor Metabolizer of this drug. Are there any recommendations? Integrates heterogeneous PGx information from several valid PGx resources (PharmGKB, Ensembl …) Offers automated personalized PGx translation (genotype-to- phenotype) services Provides a user friendly interface for submitting newly discovered PGx related gene- variants and alleles Motivations. Drug response varies among individuals, ranging from expected beneficial effects to adverse reactions and sometimes to even fatal events Different populations carry different profiles of rare and common genetic variants. Towards Population Pharmacogenomics (PPGx): What do SNPs tell us about drug response? In this work we study how 65 pharmacogenes - genes enabled in the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs - and their variants (508 SNP biomarkers) vary among 2504 genomes across 26 populations. Start with at least a 50% reduction in starting dose followed by titration of dose based on toxicity or pharmacokinetic test Patient eMoDiA: From Genotype to Phenotype to Recommendations eMoDiA: Translation Service eMoDiA: Explore Service Methodology. We developed a genotype to phenotype translation algorithm, which infers metabolizer phenotypes from individual genetic (SNP) profiles. For each pharmacogene, and based on available (PharmGKB) haplotype/allele tables, an individual’s genotype-profile is matched against the available gene-alleles. Next, each inferred allele is assigned to a metabolizer phenotype, according to available “look up” tables. [the algorithm was verified with the Affymetrix© DMET Plus respective translation results] PGx Variation among 1kG Populations ABCB1 ABCC2 South Asian Ancestry (SAS): 19% African Ancestry (AFR): 26% European Ancestry (EUR): 20% Americas Ancestry (AMR): 19% East Asian Ancestry (EAS): 20% http://www.1000genomes.org 65 pharmacogenes, 508 SNPs, 328 haplotypes found in 1kG samples 22 core PharmADME genes (4 Transporters, 12 Phase I, 6 Phase II), 26 extended PharmADME genes (1 Transporter, 2 Modifiers, 9 Phase I, 14 Phase II) 15/65 included in FDA’s Pharmacogenomic Biomarkers in Drug Labeling ACB GWD ESN MSL YRI LWK ASW CHS CDX KHV CHB JPT PJL BEB STU ITU GIH GBR FIN IBS CEU TSI PUR CLM PEL MXL The 1000 Genomes (1kG) Project aims to provide a deep characterization of human genome sequence variation. 2504 individuals from 26 populations Total variant sites ~80M Chr: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X phGenes phGenes matched to heterozygous or homozygous variant haplotypes AFR AMR EAS EUR SAS SLC25A27 UGT1A10 UGT2B15 CHRNA5 CYP2A13 CYP2C19 CYP3A43 CYP4A22 SLC22A1 SLCO1B1 SULT1A2 SULT1C2 SULT2A1 SULT4A1 CYP1A1 CYP2W1 ADRB1 CYP1A2 CYP1B1 CYP2A6 UGT1A8 UGT1A9 ADRB2 CYP2B6 CYP2C8 CYP2C9 CYP2D6 CYP2E1 CYP2F1 CYP2R1 CYP2S1 CYP3A4 CYP3A5 CYP3A7 CYP4B1 CYP4F2 HMGCR P2RY12 SCNN1B UGT1A1 UGT1A3 UGT1A4 UGT1A5 UGT1A6 UGT1A7 VKORC1 ABCB1 ABCC2 IGFBP3 PIK3CA BRCA1 COMT HTR2C SCN1A SCN5A APOE DPYD CFTR G6PD NAT1 NAT2 TPMT CDA DDC LDLR Potential abnormal metabolism in most phGenes CYP2D6 CYP2C19 NAT2 CYP2B6 : statistically significant phenotypic difference among populations in 29 genes CYP2C19-a liver enzyme that acts on 10-15% of drugs in current clinical use, including the antiplatelet clopidogrel (Plavix)-haplotypes, are matched to only 5 individuals assigning a V/V phenotype (although phSNP coverage is 78%). Same holds for 2 individuals in NAT2, a gene encoding an enzyme that functions to both activate and deactivate arylamine and hydrazine drugs and carcinogens (phSNP coverage is 63%). R/R: combination of 2 wild type haplotypes =>normal metabolic status R/V: a combination of 1 wild type and 1 variant haplotype =>intermediate metabolic status V/V: a combination of 2 variant haplotypes=>poor or ultra-rapid metabolic status Abnormal metabolism Population Pharmacogenomics Analysis Conclusions We adequately (sample coverage>90%) assigned PGx phenotypes in 33 out of 65 pharmacogenes. We found statistically significant phenotypic difference among 1kG populations in 29 pharmacogenes EAS AFR EUR AMR PEL SAS PharmacoSNP allele frequencies reflect population structures The PEL population diverges from its relevant ancestral region (AMR) Population of African Ancestry, South and East Asian Ancestry can be accurately clustered Populations of Americas Ancestry are not easily differentiated from European Ancestry populations GBR IBS PCA IBS FIN TSI PUR PJL GIH Component 2 CLM ACB ASW ITU STU CHB GWD JPT LWK MXL BEB CDX MSL YRI ESN CHS KHV PEL Component 1 The reported work was funded by the eMoDiA (electronic Molecular Diagnostics Assistant) project (11SYN_10_145) under the "Competitiveness and Entrepreneurship» (OPCE ΙΙ; Greek-EU) operational program Contact: George Potamias (potamias@ics.forth.gr)