Background: Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolising enzymes . Current population-based physiologically-based pharmacokinetic (PBPK) models do not consider inter-correlations between the abundances of different enzymes when using Monte Carlo simulations to generate virtual individuals.
Aim: We investigate, as an example, the impact of CYP3A4-CYP2C8 inter-correlation on the predicted inter-individual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide, a substrate of CYP2C8 and CYP3A4, using PBPK modelling.
Methods: PBPK modelling and simulation was employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming inter-correlations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers . A repaglinide PBPK model was used to predict pharmacokinetic parameters in presence and absence of gemfibrozil, an inhibitor of CYP2C8, in virtual populations, and the results were compared with a clinical DDI study .
Results: Coefficient of variation (CV) of oral clearance was 52.5% in the absence of inter-correlation between CYP3A4-CYP2C8 abundances which increased to 54.2% when incorporating inter-correlation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of inter-correlation between enzymes to 43.8% when incorporating inter-correlation: these CVs were associated with 5th/95th percentiles (2.48−11.29 vs. 2.49−9.69). The range of predicted DDI was larger in the absence of inter-correlation (1.55−77.06) than when incorporating inter-correlation (1.79−25.15), which was closer to clinical observations (2.6−12 ).
Conclusion: The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating inter-correlation led to more consistent estimation of extreme values with those observed in inter-individual variabilities of clearance and DDI. As the inter-correlations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.
 Achour B, et al. Drug Metab Dispos (2014), 42: 500-510.
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