in vitroとin vivo情報を活用した網羅的薬物相互作用の予測

抄録

Because drug therapy typically involves multiple concurrent treatments, there is a need for a framework that can systematically predict drug interactions (DIs). For this reason, we focused on pharmacokinetic DIs, collecting all available information on DIs involving 5 CYP molecular species and treating in vitro and in vivo observations equally to construct a framework for comprehensive and quantitative prediction of drug exposure by using MCMC method. The change in drug exposure (AUC change rate; AUCR) and in vitro observations of 67 substrates and 30 inhibitors for five CYP molecular species were collected for model building. Regarding the drug combinations for which clinical trials (in vivo) have been conducted, the AUCR prediction accuracy was within the 1/2- fold to 2-fold confidence interval. As in vitro parameters could help to estimate the contribution of CYP molecular species, indeed, the AUCR accuracy improved depending on the amount of in vitro information. The estimation of 2010 AUCRs, including unknown combinations, suggested that a considerable number of combinations are overlooked by the current FDA and PMDA classification based solely on in vivo AUCRs. In the future, the lack of in vivo information can be compensated for by increasing in vitro information and using it appropriately for DI management.