Abstract

Development of potential antitubercular molecules is a challenging task due to the rapidly emerging drug-resistant strains of Mycobacterium tuberculosis (M.tb). Structure-based approaches hold greater benefit in identifying compounds/drugs with desired polypharmacological profiles. These methods can be employed based on the knowledge of protein binding sites to identify the complementary ligands. In this study, polypharmacology guided computational drug repurposing approach was applied to identify potential antitubercular drugs. 20 important druggable protein targets in M.tb were considered from the target library of Molecular Property Diagnostic Suite–Tuberculosis (MPDS^TB–http://mpds.neist.res.in:8084) for virtual screening. FDA approved drugs were collected, preprocessed and docked in the active sites of the 20 M.tb targets. The top 300 drug molecules from each target (20 × 300) were filtered-in and subsequently screened for possible antitubercular and antimycobacterial activity using PASS tool. Using this approach, 34 drugs with predicted antitubercular and anti-mycobacterial activity were identified along with good binding affinity against multiple M.tb targets. Interestingly, 21 out of the 34 identified drugs are antibiotics while 4 drug molecules (nitrofural, stavudine, quinine and quinidine) are non-antibiotics showing promising predicted antitubercular activity. Most of these molecules have the similar privileged antimycobacterial drugs scaffold. Further drug likeness properties were calculated to get deeper insights to M.tb lead molecules. Interestingly, it was also observed that the drugs identified from the study are under different stages of drug discovery (i.e., in vitro, clinical trials) for the effective treatment of various diseases including cancer, degenerative diseases, dengue virus infection, tuberculosis, etc. Krasavin et al., 2017 synthesized nitrofuran analogues with appreciable MICs (22–23 µM) against M.tb H37Rv. These experiments further add to the credibility of the drugs identified in this study (TB).

Graphic Abstract

中文翻译：

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抗结核药物的药物重新定位：计算机多药理学方法

摘要

由于结核分枝杆菌( M.tb )耐药菌株的迅速出现，开发潜在的抗结核分子是一项具有挑战性的任务。基于结构的方法在识别具有所需多药理学特征的化合物/药物方面具有更大的优势。这些方法可以基于蛋白质结合位点的知识来识别互补配体。在这项研究中，应用多药理学指导的计算药物再利用方法来识别潜在的抗结核药物。M.tb中的20 个重要的可药用蛋白靶点来自 Molecular Property Diagnostic Suite–Tuberculosis (MPDS ^TB ) 靶点库–http://mpds.neist.res.in:8084）用于虚拟筛选。FDA 批准的药物被收集、预处理并停靠在 20 M .tb目标的活动位点。过滤来自每个靶标 (20 × 300) 的前 300 个药物分子，随后使用 PASS 工具筛选可能的抗结核和抗分枝杆菌活性。使用这种方法，确定了 34 种具有预测的抗结核和抗分枝杆菌活性的药物以及对多种M.tb的良好结合亲和力目标。有趣的是，34 种已鉴定的药物中有 21 种是抗生素，而 4 种药物分子（呋喃西林、司他夫定、奎宁和奎尼丁）是非抗生素，显示出有希望的抗结核活性。这些分子中的大多数都具有类似的特权抗分枝杆菌药物支架。计算了进一步的药物相似性，以更深入地了解M.tb先导分子。有趣的是，还观察到，该研究确定的药物处于药物发现的不同阶段（即体外试验、临床试验），可有效治疗各种疾病，包括癌症、退行性疾病、登革热病毒感染、肺结核等。 Krasavin 等人， 2017 年合成了具有明显 MIC (22–23 µM) 的硝基呋喃类似物结核分枝杆菌H37Rv。这些实验进一步增加了本研究 (TB) 中确定的药物的可信度。

图形摘要