Abstract Background: CYP2C19 and CYP2D6 collectively account for the hepatic metabolism of approximately 35–40% of clinically used drugs. Their genetic polymorphisms significantly influence drug response, including the risk of adverse drug reactions. However, the genetic basis for interindividual differences remains incompletely understood, with missing heritability attributed to factors such as rare and structural variants. Here, we identified previously uncharacterised genetic variants and evaluated drug-drug interactions, which together can contribute to more accurate phenotype predictions. Methods: We conducted an in vivo phenotyping study encompassing 114 Estonian Biobank participants to evaluate the functional impact of rare or novel single-nucleotide and structural variants in the CYP2C19 and CYP2D6 genes. Participants received a single oral dose of omeprazole (CYP2C19 probe drug) and metoprolol (CYP2D6 probe drug). Plasma concentrations of these drugs and their metabolites were measured at 10 different time points, and parent drug-to-metabolite ratios were calculated to determine enzymatic activity. Use of CYP2C19 or CYP2D6 inhibitors was assessed using medication purchase records and plasma concentration data. Long-read sequencing enabled high-resolution star allele calling. Results: We provide the first in vivo confirmation that the partial gene deletion CYP2C19*37, enriched in Estonians and Finns, is associated with a CYP2C19 poor metaboliser phenotype (P-7). Additionally, we offer in vivo insights into the recently defined CYP2D6*124 allele and a novel missense variant (chr22:42127887 G>T) in exon 6 of CYP2D6, both of which are associated with reduced metabolic activity of CYP2D6. Furthermore, we observed that inhibitor exposure was significantly associated with higher metabolic ratios for both CYP2C19 (P=3.0x10-6) and CYP2D6 (P=0.02) possibly explaining phenoconversion. Conclusions: Our findings emphasise the importance of identifying genetic variants in CYP2C19 and CYP2D6 beyond commonly assessed star alleles, as well as the need to further evaluate drug–drug interactions with weak or moderate evidence, to infer personalised metabolic phenotypes. Combining in vivo phenotyping with long-read sequencing and profiling for drug interactions can provide more precise assignments of metabolic phenotypes, which can ultimately improve personalised treatment recommendations.
Krebs et al. (Tue,) studied this question.