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

Evaluation of the CYP3A induction risk is important in early drug development stages. This study focused on 4-hydroxycholesterol (4-HC) as an endogenous biomarker of drug-drug interactions (DDIs) caused by CYP3A induction. We investigated a new approach using 4-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 4-HC or 4-HC/cholesterol (4-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 () in the MSPK model was calculated from the induction ratio of 4-HC or 4-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 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 4-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.