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dc.contributor.authorBöhm, Ruwen
dc.contributor.authorBruckmueller, Henrike
dc.contributor.authorOswald, Stefan
dc.contributor.authorHübenthal, Matthias
dc.contributor.authorKaehler, Meike
dc.contributor.authorEhmke, Lena
dc.contributor.authorHöcker, Jan
dc.contributor.authorSiegmund, Werner
dc.contributor.authorFranke, Andre
dc.contributor.authorCascorbi, Ingolf
dc.date.accessioned2024-09-30T13:30:17Z
dc.date.available2024-09-30T13:30:17Z
dc.date.issued2024-04-18
dc.description.abstractAlthough great progress has been made in the fine-tuning of diplotypes, there is still a need to further improve the predictability of individual phenotypes of pharmacogenetically relevant enzymes. The aim of this study was to analyze the additional contribution of sex and variants identified by exome chip analysis to the metabolic ratio of five probe drugs. A cocktail study applying dextromethorphan, losartan, omeprazole, midazolam, and caffeine was conducted on 200 healthy volunteers. CYP2D6, 2C9, 2C19, 3A4/5, and 1A2 genotypes were analyzed and correlated with metabolic ratios. In addition, an exome chip analysis was performed. These SNPs correlating with metabolic ratios were confirmed by individual genotyping. The contribution of various factors to metabolic ratios was assessed by multiple regression analysis. Genotypically predicted phenotypes defined by CPIC discriminated very well the log metabolic ratios with the exception of caffeine. There were minor sex differences in the activity of CYP2C9, 2C19, 1A2, and CYP3A4/5. For dextromethorphan (CYP2D6), IP6K2 (rs61740999) and TCF20 (rs5758651) affected metabolic ratios, but only IP6K2 remained significant after multiple regression analysis. For losartan (CYP2C9), FBXW12 (rs17080138), ZNF703 (rs79707182), and SLC17A4 (rs11754288) together with CYP diplotypes, and sex explained 50% of interindividual variability. For omeprazole (CYP2C19), no significant influence of CYP2C:TG haplotypes was observed, but CYP2C19 rs12777823 improved the predictability. The comprehensive genetic analysis and inclusion of sex in a multiple regression model significantly improved the explanation of variability of metabolic ratios, resulting in further improvement of algorithms for the prediction of individual phenotypes of drug-metabolizing enzymes.en_US
dc.identifier.citationBöhm, Bruckmueller, Oswald, Hübenthal, Kaehler, Ehmke, Höcker, Siegmund, Franke, Cascorbi. Phenotype–Genotype Correlation Applying a Cocktail Approach and an Exome Chip Analysis Reveals Further Variants Contributing to Variation of Drug Metabolism. Clinical Pharmacology and Therapeutics. 2024en_US
dc.identifier.cristinIDFRIDAID 2268023
dc.identifier.doi10.1002/cpt.3270
dc.identifier.issn0009-9236
dc.identifier.issn1532-6535
dc.identifier.urihttps://hdl.handle.net/10037/34935
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.journalClinical Pharmacology and Therapeutics
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0en_US
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)en_US
dc.titlePhenotype–Genotype Correlation Applying a Cocktail Approach and an Exome Chip Analysis Reveals Further Variants Contributing to Variation of Drug Metabolismen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
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