Crivori Patrizia, Reinach Benedetta, Pezzetta Daniele, Poggesi Italo
Prediction and Modeling, Preclinical Profiling, Preclinical Development, Nerviano Medical Sciences, viale Pasteur 10, 20014 Nerviano, Italy.
Mol Pharm. 2006 Jan-Feb;3(1):33-44. doi: 10.1021/mp050071a.
Multidrug resistance mediated by ATP binding cassette (ABC) transporters such as P-glycoprotein (P-gp) represents a serious problem for the development of effective anticancer drugs. In addition, P-gp has been shown to reduce oral absorption, modulate hepatic, renal, or intestinal elimination, and restrict blood-brain barrier penetration of several drugs. Consequently, there is a great interest in anticipating whether drug candidates are P-gp substrates or inhibitors. In this respect, two different computational models have been developed. A method for discriminating P-gp substrates and nonsubstrates has been set up based on calculated molecular descriptors and multivariate analysis using a training set of 53 diverse drugs. These compounds were previously classified as P-gp substrates or nonsubstrates on the basis of the efflux ratio from Caco-2 permeability measurements. The program Volsurf was used to compute the compounds' molecular descriptors. The descriptors were correlated to the experimental classes using partial least squares discriminant analysis (PLSD). The model was able to predict correctly the behavior of 72% of an external set of 272 proprietary compounds. Thirty of the 53 previously mentioned drugs were also evaluated for P-gp inhibition using a calcein-AM (CAM) assay. On the basis of these additional P-gp functional data, a PLSD analysis using GRIND-pharmacophore-based descriptors was performed to model P-gp substrates having poor or no inhibitory activity versus inhibitors having no evidence of significant transport. The model was able to discriminate between 69 substrates and 56 inhibitors taken from the literature with an average accuracy of 82%. The model allowed also the identification of some key molecular features that differentiate a substrate from an inhibitor, which should be taken into consideration in the design of new candidate drugs. These two models can be implemented in a virtual screening funnel.
由ATP结合盒（ABC）转运蛋白如P-糖蛋白（P-gp）介导的多药耐药性是有效抗癌药物研发面临的一个严重问题。此外，P-糖蛋白已被证明会降低口服吸收、调节肝脏、肾脏或肠道清除，并限制几种药物的血脑屏障穿透。因此，人们对预测候选药物是否为P-糖蛋白底物或抑制剂非常感兴趣。在这方面，已经开发了两种不同的计算模型。基于计算的分子描述符和多变量分析，使用53种不同药物的训练集建立了一种区分P-糖蛋白底物和非底物的方法。这些化合物先前根据Caco-2通透性测量的流出率被分类为P-糖蛋白底物或非底物。使用Volsurf程序计算化合物的分子描述符。使用偏最小二乘判别分析（PLSD）将描述符与实验类别相关联。该模型能够正确预测272种专有化合物外部集合中72%的行为。还使用钙黄绿素-AM（CAM）测定法对上述53种药物中的30种进行了P-糖蛋白抑制评估。基于这些额外的P-糖蛋白功能数据，使用基于GRIND药效团的描述符进行PLSD分析，以模拟对P-糖蛋白具有低抑制活性或无抑制活性的底物与无明显转运证据的抑制剂。该模型能够区分从文献中选取的69种底物和56种抑制剂，平均准确率为82%。该模型还能够识别区分底物和抑制剂的一些关键分子特征，在新候选药物的设计中应予以考虑。这两种模型可以在虚拟筛选流程中实施。
