Pharmacophore derived 3D-QSAR, molecular docking, and simulation studies of quinoxaline derivatives as ALR2 inhibitors

Abstract

Aldose Reductase 2 (ALR2), a key enzyme of the polyol pathway, plays a crucial role in the pathogenesis of diabetic complications. Quinoxaline scaffold-based compounds have been identified as potential ALR2 inhibitors for the management of diabetic complications. In the present work, molecular dynamic simulation studies in conjugation with pharmacophore mapping and atom-based 3D-QSAR were performed on a dataset of 99 molecules in comparison with Epalrestat (reference) to mark the desirable structural features of quinoxaline analogs to generate a probable template for designing novel and effective ALR2 inhibitors. The most potent compound 81 was subjected to MD simulation studies and found to be stable, with better interactions with the binding pocket as compared to Epalrestat. The MM-GBSA and MM-PBSA calculations showed that compound 81 possessed binding free energies of −35.96 and −4.92 kcal/mol, respectively. Atom-based 3D-QSAR yielded various pharmacophoric features with excellent statistical measures, such as correlation coefficient (R² value), F-value (Fischer ratio), Q² value (cross-validated correlation coefficient), and Pearson’s R-value for training and test sets. Furthermore, the pharmacophore mapping provided a five-point hypothesis (AADRR) and docking analysis revealed the active ligand-binding orientations on the active site’s amino acid residues TYR 48, HIE 110, TRP 111, and TRP 219. The results of this study will help in designing potent inhibitors of ALR2 for the management of diabetic complications.

Communicated by Ramaswamy H. Sarma

Keywords:

• Aldose reductase 2 inhibitors
• 3D-QSAR
• molecular docking
• molecular dynamics simulations
• pharmacophore mapping
• quinoxalinone

Acknowledgements

S.T. and Y.S. acknowledge the Department of Science and Technology (DST)-SERB for Startup Research Grant (SRG) (SRG/2022/000006). N.K. isgrateful to the All-India Council for Technical Education (AICTE) for providing GPAT fellowship. The authors acknowledge the Department of Science and Technology (DST) for providing the Departmental DST-FIST grant (SR/FST/LSI-656/2016) to the Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, India.

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Authors’ contributions

S.T. conceptualized the present research. The computational methodology and formal analysis were performed by Y.S., N.K., and S.K. MD simulation and analysis were performed by S.T., S.S., and Y.S. The interpretation of all the results and original draft of manuscript was prepared by S.T., S.S., Y.S., N.K., and S.K.

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No potential conflict of interest was reported by the author(s).

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The authors reported no funding associated with the work featured in this article.