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基于胆固醇酯转移蛋白抑制的冠心病治疗新型治疗药物的设计与开发——方法。

Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - approach.

机构信息

Clinic for Cardiovascular Disease, Clinical Center Nis, Nis, Serbia.

Institute for Cardiovascular Prevention and Rehabilitation Niska Banja, Nis, Serbia.

出版信息

J Biomol Struct Dyn. 2020 May;38(8):2304-2313. doi: 10.1080/07391102.2019.1630319. Epub 2019 Jun 19.

DOI:10.1080/07391102.2019.1630319
PMID:31215331
Abstract

Cholesteryl ester transfer protein (CETP) belongs to the group of enzymes which inhibition have the application in the treatment of cardiovascular diseases. This study presents QSAR modeling for a set of compounds acting as CETP inhibitors based on the Monte Carlo optimization with SMILES notation and molecular graph-based descriptors, and field-based 3D modeling. A 3D QSAR model was developed for one random split into the training and test sets, whereas conformation independent QSAR models were developed for three random splits, with the results suggesting there is an excellent correlation between them. Various statistical approaches were used to assess the statistical quality of the developed models, including robustness and predictability, and the obtained results were very good. This study used a novel statistical metric known as the index of ideality of correlation for the final assessment of the model, and the results that were obtained suggested that the model was good. Also, molecular fragments which account for the increases and/or decreases of a studied activity were defined and then used for the computer-aided design of new compounds as potential CETP inhibitors. The final assessment of the developed QSAR model and designed inhibitors was done using molecular docking, which revealed an excellent correlation with the results from QSAR modeling.Communicated by Ramaswamy H. Sarma.

摘要

胆固醇酯转移蛋白(CETP)属于酶类的一种,其抑制剂在心血管疾病的治疗中有应用。本研究采用基于蒙特卡罗优化的 SMILES 符号和基于分子图的描述符及场的 3D 建模方法,对一组作为 CETP 抑制剂的化合物进行了定量构效关系建模。基于随机分割的方法建立了一个 3D-QSAR 模型,其中一个随机分割用于训练集和测试集,而三个随机分割则用于建立构象独立的 QSAR 模型,结果表明它们之间具有极好的相关性。各种统计方法用于评估模型的统计质量,包括稳健性和可预测性,得到的结果非常好。本研究使用了一种新的统计指标,称为相关理想指数,用于对模型进行最终评估,得到的结果表明该模型是良好的。此外,还定义了导致研究活性增加和/或减少的分子片段,然后将其用于新化合物的计算机辅助设计,作为潜在的 CETP 抑制剂。采用分子对接对所开发的 QSAR 模型和设计抑制剂进行了最终评估,其结果与 QSAR 建模的结果具有极好的相关性。由 Ramaswamy H. Sarma 交流。

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