Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv.

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY SAR and QSAR in Environmental Research Pub Date : 2024-01-01 Epub Date: 2024-01-29 DOI:10.1080/1062936X.2024.2304803
P V Pogodin, E G Salina, V V Semenov, M M Raihstat, D S Druzhilovskiy, D A Filimonov, V V Poroikov
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Abstract

Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.

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基于配体的结核分枝杆菌 H37Rv 抑制剂的虚拟筛选和生物学评价。
我们需要新的抗霉菌化合物来扩展现有的治疗药物工具箱,因为现有的治疗药物有时并不有效。在我们的研究中,我们从 ChEMBL 数据库 24.1 版中提取、过滤和汇总了各种化合物的抗霉菌活性数据。这些训练集用于使用 PASS 和 GUSAR 软件创建分类和回归模型。利用这些 (Q)SAR 模型筛选了由大约 200,000 个化合物组成的 IOC 化学库,以选出可能具有抗霉菌活性的新型化合物。QikProp 工具(薛定谔)用于预测 ADME 特性,并找到具有可接受 ADME 特征的化合物。结果,选出了 20 个化合物进行进一步的生物学评估,其中 13 个是异烟肼的希夫碱。为了使筛选出的化合物更加多样化,我们采用了亚结构筛选法,又筛选出了 10 个化合物,其中没有一个是异烟肼的席夫碱。在针对结核杆菌菌株 H37Rv 的 REMA 试验中,对虚拟筛选出的 30 个化合物进行了生物评估。12 个化合物的 MIC 值低于 20 µM(从 2.17 µM 到 16.67 µM),18 个化合物的 MIC 值大大高于 20 µM。所发现的抗霉菌剂代表了不同的化学类别。
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来源期刊
CiteScore
5.20
自引率
20.00%
发文量
78
审稿时长
>24 weeks
期刊介绍: SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.
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