British journal of clinical pharmacology · 2026
Quantitative prediction of intravenous drug interactions caused by cytochromes P450 inhibitors and inducers.
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Tuloup Vianney, Tod Michel, Bourguignon Laurent
Аннотация
AIMS: Pharmacokinetic 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.
METHODS: Drugs 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.
RESULTS: Data 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).
CONCLUSIONS: Despite 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.
METHODS: Drugs 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.
RESULTS: Data 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).
CONCLUSIONS: Despite 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.