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涉及细胞色素P450酶的抑制性药物相互作用的机制及意义(综述)

Mechanisms and significance of inhibitory drug interactions involving cytochrome P450 enzymes (review).

作者信息

Murray M

机构信息

School of Physiology and Pharmacology, University of New South Wales, Sydney, NSW 2052, Australia.

出版信息

Int J Mol Med. 1999 Mar;3(3):227-38.

PMID:10028046
Abstract

The cytochrome P450 (CYP) superfamily of enzymes are catalytically competent toward an unusually diverse array of lipophilic chemicals. The major microsomal CYP in human liver, CYP3A4, participates in the oxidative biotransformation of most drugs. Accordingly, it is not surprising that CYP activity is also readily inhibited by many drugs. Such interactions may elicit adverse toxic effects in patients and this situation is exacerbated by the practise of polypharmacy. Clinical studies have suggested that the judicious selection of drugs for combination therapy may be a relatively simple means by which drug interactions can be avoided. Such studies will rely heavily on basic information obtained from biochemical studies that describe the substrate preferences of CYPs and molecular studies on the factors that determine CYP levels in subjects. This review focuses on the information that is emerging on CYP substrate and inhibitor specificity, protein structure from homology modelling approaches and CYP expression in liver as it emerges from molecular analyses. With information of this type, drawn from several different sources, it will eventually be possible to reconcile the likelihood of drug interactions produced by specific combinations of drugs and predict those individuals who are at risk from such interactions, as a consequence of their genetic makeup.

摘要

细胞色素P450(CYP)酶超家族对种类异常繁多的亲脂性化学物质具有催化活性。人类肝脏中的主要微粒体CYP,即CYP3A4,参与了大多数药物的氧化生物转化。因此,CYP活性也很容易受到许多药物的抑制,这并不奇怪。这种相互作用可能会在患者身上引发不良毒性作用,而联合用药的做法会使这种情况更加严重。临床研究表明,合理选择联合治疗药物可能是避免药物相互作用的一种相对简单的方法。此类研究将严重依赖从生化研究中获得的基础信息,这些生化研究描述了CYPs的底物偏好,以及关于决定受试者体内CYP水平的因素的分子研究。本综述重点关注从同源建模方法得出的CYP底物和抑制剂特异性、蛋白质结构以及分子分析得出的肝脏中CYP表达等方面不断涌现的信息。有了从几个不同来源获取的此类信息,最终将有可能协调特定药物组合产生药物相互作用的可能性,并预测那些由于基因组成而有此类相互作用风险的个体。

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