Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies. (May 2023)

• Record Type: Journal Article
• Authors: Singh, Rahul; Kumar, Parvin; Sindhu, Jayant; Devi, Meena; Kumar, Ashwani; Lal, Sohan; Singh, Devender
• Is Part Of: Computers in biology and medicine. Volume 157(2023)
• Publication Date: 2023-05
• Subjects: α-Amylase -- 2-Aryliminothiazolidin-4-one -- Molecular docking -- Molecular dynamics -- QSARINS -- 2D-QSAR -- Consensus modelling -- ADMET; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106776
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: α-Amylase (EC.3.2.1.1) is a ubiquitous digestive endoamylase. The abrupt rise in blood glucose levels due to the hydrolysis of carbohydrates by α-amylase at a faster rate is one of the main reasons for type 2 diabetes. The inhibitors prevent the action of digestive enzymes, slowing the digestion of carbs and eventually assisting in the management of postprandial hyperglycemia. In the course of developing α-amylase inhibitors, we have screened 2-aryliminothiazolidin-4-one based analogs for their in vitro α-amylase inhibitory potential and employed various in silico approaches for the detailed exploration of the bioactivity. The DNSA bioassay revealed that compounds 5c, 5e, 5h, 5j, 5m, 5o and 5t were more potent than the reference drug (IC60 value = 22.94 ± 0.24 μg mL −1 ). The derivative 5o with –NO2 group at both the rings was the most potent analog with an IC60 value of 19.67 ± 0.20 μg mL −1 whereas derivative 5a with unsubstituted aromatic rings showed poor inhibitory potential with an IC60 value of 33.40 ± 0.15 μg mL −1 . The reliable QSAR models were developed using the QSARINS software. The high value of R 2 ext = 0.9632 for model IM-9 showed that the built model can be applied to predict the α-amylase inhibitory activity of the untested molecules. A consensus modelling approach was also employed to test the reliability and robustness of the developed QSAR models. Molecular docking and molecular dynamics were employed to validate the bioassay results by studying the conformational changes and interaction mechanisms. A step further, these compounds also exhibited good ADMET characteristics and bioavailability when tested for in silico pharmacokinetics prediction parameters. Graphical abstract: Image 1 Highlights: Evaluated α-amylase inhibitory activity of 2-aryliminothiazolidin-4-ones. GA-MLR based 2D-models were developed to predict the inhibitory activity. Models validated by various fitting, internal and external validation parameters. Molecular docking and molecular dynamics were employed to validate bioassay results. In silico ADMET analysis revealed good bioavailability.