Leveraging large observational studies to discover genetic determinants of drug concentrations: A proof-of-concept study.

Abstract

Large, observational genetic studies are commonly used to identify genetic factors associated with diseases and disease-related traits. Such cohorts have not been commonly used to identify genetic predictors of drug dosing or concentrations, perhaps because of the heterogeneity in drug dosing and formulation, and the random timing of blood sampling. We hypothesized that large sample sizes relative to traditional pharmacokinetic studies would compensate for this variability and enable the identification of pharmacogenetic predictors of drug concentrations. We performed a cross-sectional, proof-of-concept association study to replicate the well-established association between metoprolol concentrations and CYP2D6 genotype-inferred metabolizer phenotypes in participants from the Montreal Heart Institute Hospital Cohort undergoing metoprolol therapy. Plasma concentrations of metoprolol and α-hydroxymetoprolol (α-OH-metoprolol) were measured in samples collected randomly regarding the previous metoprolol dose. A total of 999 individuals were included. The metoprolol daily dose ranged from 6.25 to 400 mg (mean 84.3 ± 57.1 mg). CYP2D6-inferred phenotype was significantly associated with both metoprolol and α-OH-metoprolol in unadjusted and adjusted models (all p < 10^-14 ). Models for metoprolol daily dose showed consistent results. Our study suggests that randomly drawn blood samples from biobanks can serve as a new approach to discover genetic associations related to drug concentrations and dosing, with potentially broader implications for genomewide association studies on the pharmacogenomics of drug metabolism.