Quinoline-based Schiff bases as possible antidiabetic agents: ligand-based pharmacophore modeling, 3D QSAR, docking, and molecular dynamics simulations study.
Shriram D RanadeShankar G AlegaonNayeem A KhatibShankar GhargeRohini S KavalapurePublished in: RSC medicinal chemistry (2024)
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, ( 1 H and 13 C) 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 IC 50 value of 12.95 ± 2.35 μM. Similarly, compound 6b was found to have an IC 50 value of 19.37 ± 0.96 μM as compared to acarbose (IC 50 : 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.
Keyphrases
- molecular docking
- molecular dynamics simulations
- molecular dynamics
- systematic review
- type diabetes
- machine learning
- magnetic resonance
- magnetic resonance imaging
- oxidative stress
- computed tomography
- cardiovascular disease
- high throughput
- protein protein
- risk assessment
- transcription factor
- single cell
- insulin resistance
- glycemic control
- free survival
- data analysis
- dna binding