Quinoline-based Schiff bases as possible antidiabetic agents: ligand-based pharmacophore modeling, 3D QSAR, docking, and molecular dynamics simulations study

喹啉类席夫碱作为可能的抗糖尿病药物:基于配体的药效团建模、3D QSAR、对接和分子动力学模拟研究

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作者:Shriram D Ranade, Shankar G Alegaon, Nayeem A Khatib, Shankar Gharge, Rohini S Kavalapure

Abstract

α-Glucosidase enzyme inhibition is a legitimate approach to combat type 2 diabetes mellitus as it manages to control postprandial hyperglycemia. In this pursuit, a literature search identified quinoline-based molecules as potential α-glucosidase inhibitors. Thus our intended approach is to identify pharmacophoric features responsible for the α-glucosidase inhibition. This was achieved by performing, ligand-based pharmacophore modeling, 3D QSAR model development, pharmacophore-based screening of a rationally designed quinoline-based benzohydrazide Schiff base library, identifying, synthesizing and characterizing molecules (6a-6j) by IR, (1H and 13C) NMR, and mass studies. Further, these molecules were evaluated for α-glucosidase and α-amylase inhibitory potential. Compound 6c was found to inhibit α-glucosidase enzyme with an IC50 value of 12.95 ± 2.35 μM. Similarly, compound 6b was found to have an IC50 value of 19.37 ± 0.96 μM as compared to acarbose (IC50: 32.63 ± 1.07 μM); the inhibitory kinetics of compounds 6b and 6c revealed a competitive type of inhibition; the inhibitory effect can be attributed to its mapped pharmacophoric feature and model validation with a survival score of 5.0697 and vector score of 0.9552. The QSAR model showed a strong correlation with an R 2 value of 0.96. All the compounds (6a-6j) showed no toxicity in L929 cell lines by the MTT assay method. Further, the binding orientation and stability of the molecules were assessed using molecular docking studies and MD trajectory analysis. The energy profile of the molecules with protein as a complex and molecules alone was evaluated using MM/GBSA and DFT calculations, respectively; finally, the pharmacokinetic profile was computed using ADMET analysis.

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