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基于 3D-QSAR CoMFA/CoMSIA 的构效关系及人β3-肾上腺素能受体选择性芳氧基丙醇胺激动剂的设计及其抗肥胖和抗糖尿病特性。

Structure-Activity Relationships Based on 3D-QSAR CoMFA/CoMSIA and Design of Aryloxypropanol-Amine Agonists with Selectivity for the Human β3-Adrenergic Receptor and Anti-Obesity and Anti-Diabetic Profiles.

机构信息

Escuela de Quimica y Farmacia, Facultad de Medicina, Universidad Andres Bello, Quillota 980, Viña del Mar 2531015, Chile.

Centro de Nanotecnología Aplicada, Facultad de Ciencias, Universidad Mayor, Camino la Pirámide 5750, Huechuraba, Santiago 8580000, Chile.

出版信息

Molecules. 2018 May 16;23(5):1191. doi: 10.3390/molecules23051191.

Abstract

The wide tissue distribution of the adrenergic β3 receptor makes it a potential target for the treatment of multiple pathologies such as diabetes, obesity, depression, overactive bladder (OAB), and cancer. Currently, there is only one drug on the market, mirabegron, approved for the treatment of OAB. In the present study, we have carried out an extensive structure-activity relationship analysis of a series of 41 aryloxypropanolamine compounds based on three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. This is the first combined comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) study in a series of selective aryloxypropanolamines displaying anti-diabetes and anti-obesity pharmacological profiles. The best CoMFA and CoMSIA models presented values of ² = 0.993 and 0.984 and values of ² = 0.865 and 0.918, respectively. The results obtained were subjected to extensive external validation (², ², ², etc.) and a final series of compounds was designed and their biological activity was predicted (best pEC = 8.561).

摘要

肾上腺素能β3 受体在组织中广泛分布,使其成为治疗多种疾病的潜在靶点,如糖尿病、肥胖、抑郁症、膀胱过度活动症(OAB)和癌症。目前,市场上只有一种药物米拉贝隆被批准用于治疗 OAB。在本研究中,我们基于三维定量构效关系(3D-QSAR)技术,对一系列 41 种芳氧基丙醇胺化合物进行了广泛的构效关系分析。这是一系列具有抗糖尿病和抗肥胖药理特性的选择性芳氧基丙醇胺化合物的首次联合比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)研究。最佳 CoMFA 和 CoMSIA 模型的²值分别为 0.993 和 0.984,以及 0.865 和 0.918。所得结果经过广泛的外部验证(²、²、²等),并设计了一系列最终化合物,预测了它们的生物活性(最佳 pEC=8.561)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/738b/6099677/41462e50ed88/molecules-23-01191-g001.jpg

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