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基于汉施QSBR微扰理论的阿尔茨海默病蛋白质组多靶点挖掘及雷沙吉兰新型噻吩生物电子等排体的实验与理论研究

Multi-Target Mining of Alzheimer Disease Proteome with Hansch's QSBR-Perturbation Theory and Experimental-Theoretic Study of New Thiophene Isosters of Rasagiline.

作者信息

Abeijon Paula, Garcia-Mera Xerardo, Caamano Olga, Yanez Matilde, Lopez-Castro Edgar, Romero-Duran Francisco J, Gonzalez-Diaz Humberto

机构信息

Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain.

Department of Pharmacology, Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain.

出版信息

Curr Drug Targets. 2017;18(5):511-521. doi: 10.2174/1389450116666151102095243.

Abstract

Hansch's model is a classic approach to Quantitative Structure-Binding Relationships (QSBR) problems in Pharmacology and Medicinal Chemistry. Hansch QSAR equations are used as input parameters of electronic structure and lipophilicity. In this work, we perform a review on Hansch's analysis. We also developed a new type of PT-QSBR Hansch's model based on Perturbation Theory (PT) and QSBR approach for a large number of drugs reported in CheMBL. The targets are proteins expressed by the Hippocampus region of the brain of Alzheimer Disease (AD) patients. The model predicted correctly 49312 out of 53783 negative perturbations (Specificity = 91.7%) and 16197 out of 21245 positive perturbations (Sensitivity = 76.2%) in training series. The model also predicted correctly 49312/53783 (91.7%) and 16197/21245 (76.2%) negative or positive perturbations in external validation series. We applied our model in theoretical-experimental studies of organic synthesis, pharmacological assay, and prediction of unmeasured results for a series of compounds similar to Rasagiline (compound of reference) with potential neuroprotection effect.

摘要

汉斯ch模型是药理学和药物化学中定量结构-结合关系(QSBR)问题的经典方法。汉斯ch QSAR方程用作电子结构和亲脂性的输入参数。在这项工作中,我们对汉斯ch分析进行了综述。我们还基于微扰理论(PT)和QSBR方法,为化学数据库(ChEMBL)中报道的大量药物开发了一种新型的PT-QSBR汉斯ch模型。目标是阿尔茨海默病(AD)患者大脑海马区表达的蛋白质。在训练集中,该模型在53783次负微扰中正确预测了49312次(特异性=91.7%),在21245次正微扰中正确预测了16197次(敏感性=76.2%)。在外部验证集中,该模型对负微扰或正微扰的预测正确率也分别为49312/53783(91.7%)和16197/21245(76.2%)。我们将我们的模型应用于有机合成、药理分析的理论-实验研究,以及对一系列与雷沙吉兰(参考化合物)相似且具有潜在神经保护作用的化合物的未测量结果的预测。

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