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使用动物模型研究CYP3A和P-糖蛋白介导的药物相互作用。

Examination of CYP3A and P-glycoprotein-mediated drug-drug interactions using animal models.

作者信息

Marathe Punit H, Rodrigues A David

机构信息

Metabolism and Pharmacokinetics, Bristol-Myers Squibb, Pennington, NJ, USA.

出版信息

Methods Mol Biol. 2010;596:385-403. doi: 10.1007/978-1-60761-416-6_17.

Abstract

With the advent of polytherapy for cancer treatment it has become prudent to minimize, as much as possible, the potential for drug-drug interactions (DDI). Toward this end, the metabolic and transporter pathways involved in the disposition of a drug candidate (phenotyping) and potential for inhibition and induction of drug-metabolizing enzymes and transporters are evaluated in vitro. Such in vitro human data can be made available prior to human dosing and enable in vitro to in vivo-based predictions of clinical outcomes. Despite some success, however, in vitro systems are not dynamic and sometimes fail to predict drug-drug interactions for a variety of reasons. In comparison, relatively less effort has been made to evaluate predictions based on data derived from in vivo animal models. This chapter will attempt to summarize different examples from the literature where animal models have been used to predict cytochrome P450 3A (CYP3A)- and P-glycoprotein-based DDI. When employing data from animal models one needs to be aware of species differences in enzyme- and transporter-activity leading to differences in pharmacokinetics, clearance pathways as well as species differences in selectivity and affinity of probe substrates and inhibitors. Because of these differences, in vivo animal studies alone, cannot be predictive of human DDI. Despite these caveats, the information obtained from validated in vivo animal models may prove useful when used in conjunction with in vitro-in vivo extrapolation methods. Such an integrated data set can be used to select drug candidates with a reduced DDI potential.

摘要

随着癌症治疗联合疗法的出现,尽可能降低药物相互作用(DDI)的可能性已成为审慎之举。为此,在体外评估参与候选药物处置的代谢和转运途径(表型分析)以及药物代谢酶和转运蛋白的抑制和诱导潜力。此类体外人体数据可在人体给药前获取,并能实现基于体外到体内的临床结果预测。然而,尽管取得了一些成功,但体外系统缺乏动态性,有时会因各种原因无法预测药物相互作用。相比之下,基于体内动物模型数据进行预测的评估工作相对较少。本章将试图总结文献中的不同实例,其中动物模型已被用于预测基于细胞色素P450 3A(CYP3A)和P-糖蛋白的药物相互作用。在采用动物模型数据时,需要注意酶和转运蛋白活性的种属差异,这些差异会导致药代动力学、清除途径的不同,以及探针底物和抑制剂的选择性和亲和力的种属差异。由于这些差异,仅靠体内动物研究无法预测人体药物相互作用。尽管存在这些警告,但从经过验证的体内动物模型获得的信息与体外-体内外推方法结合使用时可能会很有用。这样一个综合数据集可用于选择具有较低药物相互作用潜力的候选药物。

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