Pharmacogenetic phenoconversion modeling of drug-drug-gene interactions on CYP2C19 activity: effects of comedication by genotype on escitalopram concentrations

Linear phenoconversion model quantifying the reduction in *CYP2C19* activity induced by inhibitory comedications, by genetic phenotype, using therapeutic drug monitoring data

This work develops a statistical phenoconversion model on a large real-world sample (n = 2,852) of escitalopram therapeutic drug monitoring data, to quantify drug-drug-gene interactions on CYP2C19 activity. Reprocessing of high-resolution mass spectra identifies 17 phenoconverting comedications, reducing CYP2C19 activity by about one third for a full substrate. The magnitude of phenoconversion correlates strongly (R² = 0.55) with the fractional contribution of CYP2C19 to each comedication's metabolism, when the mechanism is competitive inhibition. A Bayesian approach provides well-calibrated credibility intervals.

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

This quantitative phenoconversion model on 2,852 real-world patients fills a concrete gap: most CPIC recommendations reason on genotype alone, without integrating inhibitory comedication. The correlation between phenoconversion magnitude and CYP2C19 fractional contribution is mechanistically coherent and usable to adjust TDM thresholds. medRxiv preprint status — to be confirmed after peer review, but the methodology is robust.