Pharmacokinetic recall study of Estonian Biobank participants with novel genetic variants in CYP2C19 and CYP2D6

TL;DR

This study explores how rare genetic variants in CYP2C19 and CYP2D6 affect drug metabolism in Estonian Biobank participants.

Contribution

The study provides first in vivo evidence linking novel genetic variants in CYP2C19 and CYP2D6 to poor drug metabolism.

Findings

Partial gene deletions in CYP2C19 are linked to poor metabolizer phenotypes in Estonians and Finns.

A novel missense variant in CYP2D6 reduces metabolic activity and is confirmed in vivo.

Inhibitor exposure increases metabolic ratios for both CYP2C19 and CYP2D6.

Abstract

CYP2C19 and CYP2D6 are involved in the hepatic metabolism of approximately 35–40% of clinically used drugs. 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 using omeprazole and metoprolol as respective probe drugs. Plasma concentrations of these drugs and their metabolites were measured at 10 time points, and parent drug-to-metabolite ratios were calculated to determine enzymatic activity. Long-read sequencing enabled high-resolution star allele calling. Our results provide the first in vivo confirmation that partial gene and intragenic deletions in CYP2C19 (CYP2C19*37 and CYP2C19*42), enriched in Estonians and Finns, are associated with poor metaboliser phenotypes (P < 1.2 × 10−7). Additionally, we offer in vivo evidence of reduced metabolic activity of the CYP2D6*124 allele and a novel missense variant (c.940C>A) in exon 6 of CYP2D6. Furthermore, we observed that inhibitor exposure was significantly associated with higher metabolic ratios for both CYP2C19 (P = 3.0 × 10−6) and CYP2D6 (P = 0.02). Our findings emphasise the importance of identifying genetic variants in CYP2C19 and CYP2D6 beyond commonly assessed star alleles and that profiling for drug interactions can provide more precise assignments of metabolic phenotypes and improve personalised treatment.

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