Pharmacogenetic phenoconversion modeling of drug–drug–gene interactions on CYP2C19 activity: effects of comedication by genotype on escitalopram concentrations
Abstract
Background: : . Polypharmacy is an important source of phenoconversion caused by drug interactions potentially modulated by genetic variability.
Aims: : . To develop a linear phenoconversion model for TDM data and provide quantitative estimates of drug-drug-gene interactions (DDGIs) in the pharmacogenetic phenotype groups of CYP2C19.
Methods: : . Escitalopram TDM data in a large real-world sample (n=2,852) was analysed for phenoconversion of CYP2C19 activity. Co-medication was identified by reprocessing high-resolution mass-spectra (Orbitrap). We developed a statistical model to identify inhibition from co-medication in the CYP2C19 and in alternative elimination pathways. We extended the model to estimate the inhibition ensuing from individual co-medications, using a single model for all data to account for multiple co-medications and confounders simultaneously. A Bayesian approach allowed us to stabilize the fit and provide well-calibrated credibility intervals.
Results: : . Reprocessing of TDM analyses identified 17 co-medications, which were shown to phenoconvert CYP2C19 activity proportionally to the activity in non-medicated phenotypes. Phenoconversion decreased the original CYP2C19 activity by about one third for a co-medication that corresponded to a 100% substrate of CYP2C19. The extent of CYP2C19 phenoconversion correlated strongly with the fractional contribution of CYP2C19 to the metabolism of the specific co-medication reported in the pharmacogenetic literature…
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