Inhibition of in vitro metabolism of simvastatin by itraconazole in humans and prediction of in vivo drug-drug interactions

被引:45
作者
Ishigam, M
Uchiyama, M
Kondo, T
Iwabuchi, H
Inoue, S
Takasaki, W
Ikeda, T
Komai, T
Ito, K
Sugiyama, Y
机构
[1] Univ Tokyo, Grad Sch Pharmaceut Sci, Bunkyo Ku, Tokyo 1130033, Japan
[2] Sankyo Co Ltd, Drug Metab & Pharmacokinet Res Labs, Shinagawa Ku, Tokyo 140, Japan
[3] Sankyo Co Ltd, Prod Strategy Dept, Shinagawa Ku, Tokyo 140, Japan
[4] Kitasato Univ, Sch Pharmaceut Sci, Minato Ku, Tokyo 108, Japan
关键词
drug-drug interaction; CYP3A4; HMG-CoA reductase inhibitor; simvastatin; pravastatin; itraconazole;
D O I
10.1023/A:1011077109233
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Purpose. To evaluate an interaction between simvastatin and itraconazole in in vitro studies and to attempt a quantitative prediction of in vivo interaction in humans. Methods. The inhibitory effect of itraconazole on simvastatin metabolism was evaluated using human liver microsomes and the K-i values were calculated for the unbound drug in the reaction mixture. A physiologically-based pharmacokinetic model was used to predict the maximum in vivo drug-drug interaction. Results, Itraconazole competitively inhibited the metabolism of simvastatin to M-1 and M-2 With K-i values in the nM range. The area under the curve (AUC) of simvastatin after concomitant dosing with itraconazole was predicted to increase ca. 84-101-fold compared with that without administration of itraconazole. Taking into consideration the fact that this method predicts the maximum interaction, this agrees well with the clinical observation of a 19-fold increase. A similar prediction based on the K-i value without taking into account the drug adsorption to microsomes, led to an underevaluation of the interaction. Conclusions, It was demonstrated that the competitive inhibition of CYP3A4-mediated simvastatin metabolism by itraconazole is the main cause of the drug interaction and that a K-i value corrected for drug adsorption to microsomes is the key factor in quantitatively predicting the maximum in vivo drug interactions.
引用
收藏
页码:622 / 631
页数:10
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