Utilization of Computational Tools for the Discovery of Schiff Base-based 1, 3, 4-thiadiazole Scaffold as SGLT2 Inhibitors

Q3 Medicine
Shivani Sharma, Amit Mittal, Navneet Khurana
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引用次数: 0

Abstract

Background: High or abnormal blood sugar levels are the hallmark of diabetes mellitus (DM), a metabolic disorder that will be one of the major causes of mortality in 2021. SGLT2 inhibitors have recently shown beneficial effects in the treatment of diabetes by reducing hyperglycemia and glucosuria. Objective: Molecular docking and ADMET studies of Schiff base- based 1, 3, 4-thiadiazole scaffold as SGLT2 inhibitors. objective: Molecular docking and ADMET studies of Schiff base based 1, 3, 4-thiadiazole scaffold as SGLT2 inhibitors. Methods: Chem draw Ultra 16.0 software was used to draw the structures of newly designed molecules of Schiff base-based 1, 3, 4-thiadiazole, which were then translated into 3D structures. For the molecular docking study, AutoDock Vina 1.5.6 software was employed. Lazar in silico and Swiss ADME predictors were used to calculate in silico ADMET characteristics. Results: We have designed 111 novel Schiff base-based 1, 3, 4-thiadiazole derivatives as SGLT2 inhibitors. A total of 10 compounds from the thiadiazole series were found to have higher binding affinity to the SGLT2 protein than dapagliflozin. SSS 56 had the best docking scores and binding affinities, with -10.4 Kcal/mol, respectively. In silico ADMET parameters demonstrated that the best binding compounds were found to be non-carcinogenic with LogP = 2.53-4.02. result: We have designed 111 novel Schiff base based 1, 3, 4-thiadiazole derivatives as SGLT2 inhibitors. A total of 10 compounds from the thiadiazole series were found to have higher binding affinity to the SGLT2 protein than dapagliflozin. SSS 56 had the best docking scores and binding affinities, with -10.4 Kcal/mol, respectively. In silico ADMET parameters demonstrated that best binding compounds found to be non-carcinogenic with LogP = 2.53-4.02. Conclusion: Novel Schiff base-based 1, 3, 4-thiadiazole were designed and binding affinity was assessed against SGLT2 protein, which resulted in a new lead molecule with a maximal binding affinity and estimated to be noncarcinogenic with an optimal partition coefficient (iLogP = 2.53- 4.02). conclusion: Novel Schiff base based 1, 3, 4-thiadiazole were designed and binding affinity were assessed against SGLT2 protein which resulted in a new lead molecule with a maximal binding affinity and estimated to be noncarcinogenic with an optimal partition coefficient (iLogP = 2.53-4.02).
利用计算工具发现基于希夫碱的1,3,4 -噻二唑支架作为SGLT2抑制剂
背景:高血糖或异常血糖水平是糖尿病(DM)的标志,糖尿病是一种代谢紊乱,将成为2021年死亡的主要原因之一。SGLT2抑制剂最近显示出通过降低高血糖和低血糖治疗糖尿病的有益效果。目的:以希夫碱为基础的1,3,4 -噻二唑支架作为SGLT2抑制剂的分子对接和ADMET研究。目的:基于希夫碱的1,3,4 -噻二唑支架作为SGLT2抑制剂的分子对接和ADMET研究。方法:采用Chem draw Ultra 16.0软件绘制新设计的希夫碱类1,3,4 -噻二唑分子的结构,并将其转化为三维结构。分子对接研究采用AutoDock Vina 1.5.6软件。使用Lazar in silico和Swiss ADME预测器计算计算机ADMET特征。结果:设计了111个新的希夫碱类1,3,4 -噻二唑类SGLT2抑制剂。从噻二唑系列中共发现10个化合物与SGLT2蛋白的结合亲和力高于达格列净。sss56的对接分数和结合亲和力最高,分别为-10.4 Kcal/mol。在硅ADMET参数表明,发现最佳的结合化合物是非致癌的,LogP = 2.53-4.02。结果:设计了111个基于希夫碱的1,3,4 -噻二唑类SGLT2抑制剂。从噻二唑系列中共发现10个化合物与SGLT2蛋白的结合亲和力高于达格列净。sss56的对接分数和结合亲和力最高,分别为-10.4 Kcal/mol。在硅ADMET参数表明,发现的最佳结合化合物是非致癌的,LogP = 2.53-4.02。结论:设计了基于Schiff碱的新型1,3,4 -噻二唑,并对其与SGLT2蛋白的结合亲和力进行了评价,得到了一种结合亲和力最大、无致癌性的新型导联分子(iLogP = 2.53 ~ 4.02)。结论:设计了基于希夫碱的新型1,3,4 -噻二唑,并对其与SGLT2蛋白的结合亲和力进行了评价,得到了一种结合亲和力最大、无致癌性的新型导联分子(iLogP = 2.53 ~ 4.02)。
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
18
审稿时长
>12 weeks
期刊介绍: In recent years a breakthrough has occurred in our understanding of the molecular pathomechanisms of human diseases whereby most of our diseases are related to intra and intercellular communication disorders. The concept of signal transduction therapy has got into the front line of modern drug research, and a multidisciplinary approach is being used to identify and treat signaling disorders. The journal publishes timely in-depth reviews, research article and drug clinical trial studies in the field of signal transduction therapy. Thematic issues are also published to cover selected areas of signal transduction therapy. Coverage of the field includes genomics, proteomics, medicinal chemistry and the relevant diseases involved in signaling e.g. cancer, neurodegenerative and inflammatory diseases. Current Signal Transduction Therapy is an essential journal for all involved in drug design and discovery.
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