Quantitative Prediction of Drug-Drug Interactions Caused by CYP3A Induction Using Endogenous Biomarker 4β-Hydroxycholesterol.

Abstract

Evaluation of the CYP3A induction risk is important in early drug development stages. This study focused on 4β-hydroxycholesterol (4β-HC) as an endogenous biomarker of drug-drug interactions (DDIs) caused by CYP3A induction. We investigated a new approach using 4β-HC for quantitative prediction of DDIs caused by CYP3A induction based on the mechanistic static pharmacokinetic (MSPK) model. The induction ratio, i.e., the ratio of plasma 4β-HC or 4β-HC/cholesterol (4β-HC/C) with and without a coadministered CYP3A inducer, and the ratio of the area under the plasma concentration-time curve (AUCR), i.e., the ratio of the AUC of plasma CYP3A substrate drugs with and without a coadministered CYP3A inducer, were collected. The scaling factor (d) in the MSPK model was calculated from the induction ratio of 4β-HC or 4β-HC/C based on the systemic term in the MSPK model. The AUCR of 18 CYP3A substrates with and without coadministration of seven CYP3A inducers were then predicted by substituting the calculated d value into the MSPK model. This approach showed that approximately 84% of the predicted AUCR values were within a twofold range of the observed values, showing that this approach can be a good tool to quantitatively predict DDIs caused by CYP3A induction. SIGNIFICANCE STATEMENT: A concise approach to predict drug interactions with adequate accuracy is preferable in the early drug development stage. In this study, a new approach using 4β-hydroxycholesterol for quantitative prediction of drug-drug interactions caused by CYP3A induction was investigated. The predictability was verified using seven CYP3A inducers and 18 substrates.