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α1-肾上腺素能受体亚型的选择性药效团设计

Selective pharmacophore design for alpha1-adrenoceptor subtypes.

作者信息

MacDougall Iain J A, Griffith Renate

机构信息

School of Environmental and Life Sciences, The University of Newcastle, Australia.

出版信息

J Mol Graph Model. 2006 Sep;25(1):146-57. doi: 10.1016/j.jmgm.2005.12.001. Epub 2006 Jan 6.

Abstract

Alpha1-adrenoceptors are G-protein coupled receptors found in a variety of vascular tissues and responsible for vasoconstriction. Selectivity for each of the three subtypes is an important consideration in drug design in order to minimise the possibility of side effects. Using Catalyst we developed ligand-based pharmacophores from alpha(1a,b,d)-selective antagonists available in the literature using three separate training sets. Four-feature pharmacophores were developed for the alpha(1a) and alpha(1b) subtype-selective antagonists and a five-feature pharmacophore was developed for the alpha(1d) subtype-selective antagonists. The alpha(1a) pharmacophore represents both class I and II compounds with good predictivity for other compounds outside the training set as well. The alpha(1b) pharmacophore best predicts the activity of prazosin analogues as these make up the majority of alpha(1b)-selective antagonists. Unexpectedly, no positive ionisable feature was incorporated in the alpha(1b) pharmacophore. The alpha(1d) pharmacophore was based primarily on one structural class of compounds, but has good predictivity for a heterogeneous test set. Preliminary docking studies using AutoDock and optimised alpha1-adrenoceptor homology models, conducted with the antagonists prazosin (32) and 66, showed good agreement with the findings from the pharmacophores.

摘要

α1肾上腺素能受体是一种G蛋白偶联受体,存在于多种血管组织中,负责血管收缩。在药物设计中,考虑三种亚型各自的选择性非常重要,这样可以将副作用的可能性降至最低。我们使用Catalyst软件,依据文献中已有的α(1a,b,d)选择性拮抗剂,通过三个独立的训练集,开发了基于配体的药效团。针对α(1a)和α(1b)亚型选择性拮抗剂开发了四特征药效团,针对α(1d)亚型选择性拮抗剂开发了五特征药效团。α(1a)药效团代表了I类和II类化合物,对训练集之外的其他化合物也具有良好的预测性。α(1b)药效团对哌唑嗪类似物的活性预测效果最佳,因为这些类似物构成了大多数α(1b)选择性拮抗剂。出乎意料的是,α(1b)药效团中没有包含正离子化特征。α(1d)药效团主要基于一类化合物的结构,但对一个异质测试集具有良好的预测性。使用AutoDock和优化的α1肾上腺素能受体同源模型,对拮抗剂哌唑嗪(32)和66进行的初步对接研究,与药效团的研究结果显示出良好的一致性。

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