Implications of inter-correlation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: A case study with repaglinide

Aims:
Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolising enzymes. 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 using physiologically-based pharmacokinetic (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 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).

Conclusions:
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.