Demel Michael A, Schwaha R, Krämer O, Ettmayer P, Haaksma Eric Ej, Ecker Gerhard F
Department of Medicinal Chemistry, University of Vienna, Emerging Field Pharmacoinformatics, Wien, Althanstrasse 14, Austria.
Expert Opin Drug Metab Toxicol. 2008 Sep;4(9):1167-80. doi: 10.1517/17425255.4.9.1167.
Overexpression of ABC (ATP-binding cassette)-type drug efflux pumps, such as ABCB1, ABCC1 and ABCG2 in cancer cells confers multi-drug resistance (MDR) and represents a major cause of treatment failures in cancer therapy. Furthermore, there is increasing evidence for the important contribution of ABC-transporters to bioavailability, distribution, elimination and blood-brain barrier permeation of drug candidates. This review presents an overview on the different computational methods and models pursued to predict ABC-transporter substrate properties of drug-like compounds. They range from linear discriminant analysis to pharmacophore modelling and machine learning algorithms. Many of these models show a satisfying performance within the study-specific, defined chemical space but general applicability for the whole drug-like chemical space still needs to be proven. First attempts aiming towards selectivity profiling for ligands of the two polyspecific transporters ABCB1 and ABCG2 is also discussed. This might pave the way for a pharmacological profiling of compound series with special focus on their ADMET (absorption, distribution, metabolism, excretion and toxicity) properties.
癌细胞中ABC（ATP结合盒）型药物外排泵，如ABCB1、ABCC1和ABCG2的过表达赋予多药耐药性（MDR），这是癌症治疗中治疗失败的主要原因。此外，越来越多的证据表明ABC转运蛋白对候选药物的生物利用度、分布、消除和血脑屏障渗透具有重要作用。本综述概述了用于预测类药物化合物ABC转运蛋白底物特性的不同计算方法和模型。这些方法从线性判别分析到药效团建模和机器学习算法。许多这些模型在特定研究定义的化学空间内表现出令人满意的性能，但对于整个类药物化学空间的普遍适用性仍有待证明。还讨论了针对两种多特异性转运蛋白ABCB1和ABCG2的配体进行选择性分析的首次尝试。这可能为特别关注其ADMET（吸收、分布、代谢、排泄和毒性）特性的化合物系列的药理学分析铺平道路。
