TOWARDS ELECTRONIC PHARMACOGENOMIC ASSISTANCE AND TRANSLATION SERVICES G. Potamias1, K. Lakiotaki1, E. Kartsaki1, A. Kanterakis1, G. P. Patrinos2 1Institute of Computer Science, Foundation for Research & Technology – Hellas, Heraklion, Crete {kliolak, ekartsak, kantale, potamias}@ics.forth.gr 2Department of Pharmacy, University of Patras, Hellas, gpatrinos@upatras.gr 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. Personalization Quality of information Cost Updatable Current scientific Knowledge bases × √ Commercial Direct To Consumer (DTC) companies ? eMoDiA: electronic Molecular Diagnostic Assistant Motivations. Drug response varies among individuals, ranging from expected beneficial effects to adverse reactions, and sometimes to even fatal events Various pharmacogenes relate and affect drug response Different populations carry different profiles of rare and common gene variants In this work we study how 69 ADMET genes and their variants (500 SNP biomarkers) affect the PGx metabolizer status of 1000 Genomes (1kG) samples across 14 populations following an elaborative PGx translation process. ★This work is carried out in the context of the eMoDiA (electronic Molecular Diagnostics Assistant) project. To integrate heterogeneous PGx information from several valid PGx resources (PharmGKB, Ensembl …) To offer automated personalized PGx translation (genotype-to- phenotype) services To provide a user friendly interface for submitting newly discovered PGx related gene-variants and alleles eMoDiA: From Genotype to Phenotype Pharmacogene - any evidence based gene related to drug Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET); Tools. Pharmacogenomics Knowledge Base (PharmGKB), collects, curates and disseminates knowledge about the impact of human genetic variation on drug responses. Affymetrix DMET™ Console Analysis Software provides reporting and translation from genotypic data to predicted metabolizer status for the most clinically relevant genes. Methodology. We developed an automated PGx 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] eMoDiA: From Phenotype to Recommendations Gene Diplotype CYP2C9 *1/*1 CYP2C19 *1/*2 TPMT *1S/*1S Phenotype RR RV VV Metabolizer Status Extensive Intermediate Poor or Ultra I am about to prescribe fluoropyrimidine to a patient who is Poor Metabolizer of this drug. Are there any recommendations? Individual / Patient Recommendations Clinical Annotations / Dosing Guidelines Genotype 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 Start with at least a 50% reduction in starting dose followed by titration of dose based on toxicity or pharmacokinetic test (inferred) Phenotypes Drug_1 Drug_2 Drug_3 … Drug_n Gene_1 IM - Gene_2 PM EM Gene_3 Gene_n eMoDia DW Translation algorithm Allele matching PGx Coverage of 1000 Genomes Project 69 pharmacogenes, 499 snps, 565 haplotypes 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) The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation. Launched in 2008. About ~30% (220 out of 728) PGx SNPs are not covered by 1kG In October 2012, the sequencing of 1092 genomes (14 populations) was announced in a Nature publication. CYP2D6, one of the most important pharmacogenes, with a large contribution of genetic variation to the inter-individual variation in enzyme activity, involved in the metabolism of up to 25% of commonly used clinical drugs, is covered by only 50% in 1kG. CFTR gene that is involved in Cystic Fibrosis, maybe the most common life-limiting autosomal recessive disease among people of European heritage, is slightly covered by only 8%. The PGx (SNP) markers are shifted towards the more frequent band of the 1kG MAF spectrum. PGx Profile Variation among 1kG Populations 1 H0: Pharmacogenomics metabolizer status does not vary among 1kG populations at a significance level a=0.0005 (Bonferroni corrected) 4 CYP3A7 2 3 PGx phenotype categories vary among genes – e.g: all individuals are R/R in CYP2R1 and CYP2S1 genes most of the individuals are V/V in CYP2A6 in CYP2C19 and CYP3A4 most of the samples did not match to any (known so-far) PGx diplotype. In 44% of pharmacogenes at least 25% of 1kG samples match to a V/V phenotype 1kG populations exhibit different V/V patterns among pharmacogenes Europeans and Africans show a significant variation on their phenotypic distribution in CYP3A7 [χ2(26, N=1092)=435,77, p<0.00005] The presented wok is supported by the (Greek-funded) eMoDiA project (11SYN_10_145) in the context of the COOPERATION 2011 program