Synergistic Effects of Inflammation and Drug Interactions on CYP3A5*3/*3 Phenoconversion in Antipsychotic Metabolism

Background: Traditional genotype-guided dosing often fails to predict real-time variability in the metabolic phenotype during complex polypharmacy. This secondary analysis of a retrospective cohort aims to elucidate mechanisms underlying real-time phenoconversion during antipsychotic therapy, focusing on homozygous loss-of-function CYP3A5*3/*3 non-expressors. Methods: Using an additive phenoconversion model that integrates a genotype-derived baseline with environmental modifiers for drug–drug interactions (DDI), systemic inflammation (CRP), and renal function (eGFR), we demonstrate that the expressed metabolic phenotype is a dynamic, context-dependent construct that can markedly diverge from the genotype-predicted state. Objectives: Our data show that patients with CYP3A5*3/*3 and CYP3A inhibitors (e.g., ritonavir) had a quetiapine plasma concentrations reached 1850 ng/mL, corresponding to 3.7-fold above the internationally accepted therapeutic reference range of 100–500 ng/mL. Acute systemic inflammation (CRP > 50 mg/L) induced a functional poor metabolizer phenotype (Pact < −0.9) in individuals with a genotypic normal metabolizer status. In contrast, strong inducers such as carbamazepine, phenytoin, and heavy smoking promoted an ultra-rapid metabolizer state (CLind > 4.0 L/h, quetiapine < 30 ng/mL), consistent with treatment failure. In this cohort, the additive Pact model showed a strong association with observed clearance and identified clinically relevant phenoconversion mechanisms not predicted from genotype alone. Conclusions: These results support a dynamic, multi-parametric approach that integrates pharmacogenomics, therapeutic drug monitoring, biomarker profiling (CRP, eGFR), and structured DDI assessment to enable higher-resolution, real-time phenotype tracking and more informed dose individualization in high-risk psychiatric polypharmacy.