Quantitative prediction of intravenous drug interactions caused by cytochromes P450 inhibitors and inducers.

aimsPharmacokinetic interaction studies typically focus on oral administration, but intravenous (IV) administration bypasses intestinal degradation and hepatic first-pass metabolism, leading to distinct drug-drug interaction (DDI) magnitude. This study aimed to develop a predictive model for DDIs involving IV-administered drugs.

methodsDrugs metabolized by the five major cytochrome P450 enzymes were analysed. Data from IV DDI studies, including area under the concentration-time curve (AUC) ratios of object drugs with and without precipitants, were collected. A Bayesian approach estimated IV contribution ratios (CR) from published oral CR values. Predictions were validated by assessing whether predicted-to-observed ratios fell within 50-200% of observed values.

resultsData for 33 drugs and 87 AUC ratios involving 26 precipitants were analysed. In the training set, 95% of AUC ratios were within the acceptability range, with a mean bias of 0.09 mg·h/L and 16% imprecision. In the validation set, 85% of predictions were within range, with a bias of -0.012 mg·h/L and 27.3% imprecision. Mean AUC ratios for inhibitors were 2.28 (oral) and 1.78 (IV). For inducers, mean AUC ratios were 0.209 (oral) and 0.62 (IV).

conclusionsDespite the limited dataset, the model demonstrated robust performance with 85% of predictions validated. It expands the DDI-predictor framework for quantitative prediction of cytochrome-mediated drug-drug interactions affecting drug exposure to intravenously administered object drugs, thereby providing a tool to predict and understand IV-specific DDIs effectively.