{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:59:53Z","timestamp":1785373193042,"version":"3.55.0"},"reference-count":94,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:00:00Z","timestamp":1775260800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:00:00Z","timestamp":1775260800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Br J Clin Pharmacol"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Background<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Pharmacokinetic interaction studies typically focus on oral administration, but intravenous (IV) administration bypasses intestinal degradation and hepatic first\u2010pass metabolism, leading to distinct drug\u2013drug interaction (DDI) magnitude. This study aimed to develop a predictive model for DDIs involving IV\u2010administered drugs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Drugs metabolized by the five major cytochrome P450 enzymes were analysed. Data from IV DDI studies, including area under the concentration\u2010time 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\u2010to\u2010observed ratios fell within 50\u2013200% of observed values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2009mg\u00b7h\/L and 16% imprecision. In the validation set, 85% of predictions were within range, with a bias of \u22120.012\u2009mg\u00b7h\/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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Despite the limited dataset, the model demonstrated robust performance with 85% of predictions validated. It expands the DDI\u2010predictor framework for quantitative prediction of cytochrome\u2010mediated drug\u2013drug interactions affecting drug exposure to intravenously administered object drugs, thereby providing a tool to predict and understand IV\u2010specific DDIs effectively.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bcp.70548","type":"journal-article","created":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T08:20:47Z","timestamp":1775290847000},"page":"2682-2692","source":"Crossref","is-referenced-by-count":0,"title":["Quantitative prediction of intravenous drug interactions caused by cytochromes P450 inhibitors and inducers"],"prefix":"10.1002","volume":"92","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7644-5848","authenticated-orcid":false,"given":"Vianney","family":"Tuloup","sequence":"first","affiliation":[{"name":"Pharmacy Department CHRU Tours  Tours France"},{"name":"Pharmacy Faculty Tours University  Tours France"},{"name":"Inserm 1327 ISCHEMIA 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