Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups

This study provides a whole-body physiologically-based pharmacokinetic (PBPK) model of dextromethorphan and its metabolites dextrorphan and dextrorphan O-glucuronide for predicting the effects of cytochrome P450 2D6 (CYP2D6) drug-gene interactions (DGIs) on dextromethorphan pharmacokinetics (PK). Moreover, the effect of interindividual variability (IIV) within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites was investigated. A parent-metabolite-metabolite PBPK model of dextromethorphan, dextrorphan, and dextrorphan O-glucuronide was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Plasma concentration-time profiles of all three analytes were gathered from published studies and used for model development and model evaluation. The model was evaluated comparing simulated plasma concentration-time profiles, area under the concentration-time curve from the time of the first measurement to the time of the last measurement (AUC(last)) and maximum concentration (C-max) values to observed study data. The final PBPK model accurately describes 28 population plasma concentration-time profiles and plasma concentration-time profiles of 72 individuals from four cocktail studies. Moreover, the model predicts CYP2D6 DGI scenarios with six of seven DGI AUC(last) and seven of seven DGI C-max ratios within the acceptance criteria. The high IIV in plasma concentrations was analyzed by characterizing the distribution of individually optimized CYP2D6 k(cat) values stratified by activity score group. Population simulations with sampling from the resulting distributions with calculated log-normal dispersion and mean parameters could explain a large extent of the observed IIV. The model is publicly available alongside comprehensive documentation of model building and model evaluation.

Automated summary

This study presents a PBPK model of dextromethorphan and its metabolites, which can predict the effects of CYP2D6 drug-gene interactions on dextromethorphan pharmacokinetics. It also investigates the impact of interindividual variability within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites. The model accurately describes plasma concentration-time profiles and predicts DGI scenarios within the acceptance criteria.