nahprortxed fonoitamrof foyticoeV)netorpgnmaeraevitaer(00.0Individual responses to drugs vary widely. Lack of drug efficacy can lead to V68GR296CW128XR296CC191LL231PN285SP34SR296CR296CR296C123456789i / l il /S486T400S486T350300S486T250G340RS486T200R334Q150R414CS486T10050P430LS486TR497H1.00.53.5Dextromethorphan concentrations (M)1.523Novel CYP2D6 haplotypes identified in 990 Japanese subjects100 important pharmacokinetics-related genes, including 62drug-metabolizing enzymes and 37 drug transporters*1*128*129*130*131*132*133*134*135*136*137ABCB1CYP1A1CYP2D6CYP4B1ABCB4CYP1A2CYP2E1CYP4F2ABCB11CYP1B1CYP2J2CYP4F3ABCC1CYP2A6CYP2S1CYP4F8ABCC2CYP2A13CYP2W1CYP4F12ABCC3CYP2B6CYP3A4CYP4Z1ABCC4CYP2C8CYP3A5CYP11A1ABCG2CYP2C9CYP3A7CYP17A1CES1CYP2C18CYP3A43CYP19A1CES2CYP2C19CYP4A11CYP26A1DPYDNAT1SLC19A1FMO1NAT2SLC22A1FMO2NUDT1SLC22A2FMO3NUDT15SLC22A3FMO4PORSLC22A4FMO5SLC10A1SLC22A5GSTA1SLC10A2SLC22A6GSTM1SLC15A1SLC22A8GSTP1SLC15A2SLC22A9GSTT1SLC16A7SLC22A11Michaelis-Menten plots of novel CYP2D6 variant proteins involved in the metabolism of dextromethorphanNull alleles: *128 (V68G), *129 (W128X), *130(C191L)SLC22A12SLC47A2UGT1A3SLC28A1SLCO1B1UGT1A4SLC28A2SLCO1B3UGT1A5SLC28A3SLCO2B1UGT1A6SLC29A1SLUT1A1UGT1A7SLC29A2SLUT1A2UGT1A8SLC29A3SLUT1E1UGT1A9SLC31A1SLUT2B1UGT1A10SLC46A1TPMTUGT2B7SLC47A1UGT1A1VKORC13.02.02.54.0*134 (R340Q)*1 (wild-type)*135 (R414C)*137 (R497H)*136 (P430L)*133 (G340R)*10*132 (N285S)*131 (L231P)inadequate disease control and is furthermore a waste of resources; con-versely, adverse drug reactions (ADRs) are frequent and often unpredictable. Many germline polymorphisms, which are called pharmacogenomics (PGx) biomarkers, have been identified in genes that affect efficacy or ADR risk for various drugs. In Japan, the National Health Insurance System currently covers only three germline genetic tests, for UGT1A1, NUDT15 and CYP2C9, to pre-dict drug responses prior to drug administration. We conduct genomic analyses for the identification of PGx biomarkers useful for predicting drug responses.A next-generation sequencing (NGS) panel, PKseq, can comprehensively and accurately analyze common and rare variants of 100 pharmacokinetics (PK)-related genes with higher sensitivity and specificity compared to whole-genome and whole-exome sequencing. Indeed, when we applied the PKseq technology to the determination of haplotypes of CYP2D6, very important drug-metabolizing enzymes for clinical therapeutics, in 990 Japanese subjects, 14 novel variants and 10 novel haplotypes were identified that affected the in vitro metabolic activities of CYP2D6. Using our experience in developing PKseq, we recently developed a novel targeted NGS panel, “corePGseq” con-sisting of 14 clinically important PK-related genes and 4 HLA genes associated with the risk of developing serious ADRs. In fact, the corePGseq panel allowed us to identify the HLA-DPB1*02:01:02 allele as associated with the risk of wheat-dependent exercise-induced anaphylaxis (WDEIA). Compared to PKseq, corePGseq also reduces the analysis burden and cost of reagents and is expected to be applied to pharmacogenetic testing in clinical settings.Figure: Determination of novel CYP2D6 hap-lotypes using the targeted next-generation sequencing panel, PKseqPharmacokinetic (PK) variabilities in intestinal absorption, hepatic drug metabolism, and biliary and renal excretion are often responsible for inter-individual differences in drug efficacy and risk of adverse drug reactions. PKseq is a highly efficient and accurate next-generation sequencing (NGS) platform for the resequencing of PK-related genes. PKseq identified 14 novel CYP2D6 variants, and ten novel haplo-types were registered as CYP2D6*128 to *137 alleles in the Pharmacogene Variation Consortium (PharmVar) database. Based on the in vitro Vmax/Km value of each allele, *128, *129, *130, *131, *132, and *133 were predicted to be non-functional alleles.Recent Major PublicationsFukunaga K, Chinuki Y, Hamada Y, Fukutomi Y, Sugiyama A, Kishikawa R, Fukunaga A, Oda Y, Ugajin T, Yokozeki H, Harada N, Suehiro M, Hide M, Nakagawa Y, Noguchi E, Nakamura M, Matsunaga K, Yagami A, Morita E, Mushiroda T. Genome-wide association study reveals an associa-tion between the HLA-DPB1*02:01:02 allele and wheat-dependent exercise-induced anaphylaxis. Am J Hum Genet 105, 1540-1548 (2021)Fukunaga K, Kato K, Okusaka T, Saito T, Ikeda M, Yoshida T, Zembutsu H, Iwata N, Mushiroda T. Functional Charac-terization of the Effects of N-acetyltransferase 2 Alleles on N-acetylation of Eight Drugs and Worldwide Distribution of Substrate-Specific Diversity. Front Genet 12, 652704 (2021)Fukunaga K, Hishinuma E, Hiratsuka M, Kato K, Okusaka T, Saito T, Ikeda M, Yoshida T, Zembutsu H, Iwata N, Mushiroda T. Determination of novel CYP2D6 haplotype using the targeted sequencing followed by the long-read sequencing and the functional characterization in the Japanese popula-tion. J Hum Genet 66, 139-149 (2021)Invited presentationsMushiroda T. “Current status and challenges of clinical implementation of pharmacogenomics” The 42nd Annual Scientific Meeting of the Japanese Society of Clinical Phar-macology and Therapeutics (Sendai, Japan) December 2021Mushiroda T. “Pharmacogenomics in non-psychiatric disor-ders” The 117th Annual Meeting of the Japanese Society of Psychiatry and Neurology (Kyoto, Japan) September 2021Mushiroda T. “Prediction of risk of severe adverse drug reac-tions based on pharmacogenomics” The 28th Annual Meet-ing of Non-Profit Organization Human and Animal Bridging Research Organization (Online) June 2021Mushiroda T. “A recent update of pharmacogenetic tests” The 4th Pharmacogenomics Seminar (Online) June 2021Hikino K. “Individualized drug therapy and pharmacoge-nomics in pediatrics” The 47th Annual Meeting of the Japan Society of Developmental Pharmacology and Therapeutics (Online) Match 2021Laboratory for PharmacogenomicsTeam Leader: Taisei Mushiroda
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