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预测体内药物相互作用的体外方法。

In vitro approaches to predicting drug interactions in vivo.

作者信息

von Moltke L L, Greenblatt D J, Schmider J, Wright C E, Harmatz J S, Shader R I

机构信息

Department of Pharmacology and Experimental Therapeutics, Tufts University School of Medicine, Boston, MA 02111, USA.

出版信息

Biochem Pharmacol. 1998 Jan 15;55(2):113-22. doi: 10.1016/s0006-2952(97)00239-6.

Abstract

In vitro metabolic models using human liver microsomes can be applied to quantitative prediction of in vivo drug interactions caused by reversible inhibition of metabolism. One approach utilizes in vitro Ki, values together with in vivo values of inhibitor concentration to forecast in vivo decrements of clearance caused by coadministration of inhibitor. A critical limitation is the lack of a general scheme for assigning intrahepatic exposure of enzyme to inhibitor or substrate based only on plasma concentration; however, the assumption that plasma protein binding necessarily restricts hepatic uptake is not tenable. Other potential limitations include: flow-dependent hepatic clearance, "mechanism-based" chemical inhibition, concurrent induction, or a major contribution of gastrointestinal P450-3A isoforms to presystemic extraction. Nonetheless, the model to date has provided reasonably accurate forecasts of in vivo inhibition of clearance of several substrates (desipramine, terfenadine, triazolam, alprazolam, midazolam) by coadministration of selective serotonin reuptake-inhibitor antidepressants and azole antifungal agents. Such predictive models deserve further evaluation, since they may ultimately yield more cost-effective and expeditious screening for drug interactions, with reduced human drug exposure and risk.

摘要

使用人肝微粒体的体外代谢模型可用于定量预测由代谢可逆抑制引起的体内药物相互作用。一种方法是利用体外Ki值以及抑制剂浓度的体内值来预测因同时给予抑制剂而导致的体内清除率下降。一个关键的局限性是缺乏仅基于血浆浓度来确定酶在肝内对抑制剂或底物暴露程度的通用方案;然而,认为血浆蛋白结合必然会限制肝脏摄取的假设是站不住脚的。其他潜在的局限性包括:血流依赖性肝清除率、“基于机制”的化学抑制、同时诱导,或胃肠道P450-3A同工型对首过提取的主要贡献。尽管如此,迄今为止该模型已对同时给予选择性5-羟色胺再摄取抑制剂抗抑郁药和唑类抗真菌药后几种底物(地昔帕明、特非那定、三唑仑、阿普唑仑、咪达唑仑)的体内清除抑制情况做出了相当准确的预测。此类预测模型值得进一步评估,因为它们最终可能会产生更具成本效益且更快捷的药物相互作用筛选方法,同时减少人体药物暴露和风险。

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