COMPUTATIONAL MOLECULAR MODELLING OF 2-AMINOBENZIMIDAZOLE DERIVATIVES: STRONG SUCCESSOR OF HYPOGLYCAEMIC AGENT
DOI:
https://doi.org/10.22159/ijpps.2021v13i9.41877Keywords:
2-aminobenzimidazole, Alpha-glucosidase, DPP4, PPAR Gamma, IGF-1, PASS, Molecular dockingAbstract
Objective: The present study was aimed to design different analogues of 2-aminobenzimidazole and find the binding ability by Insilico method.
Methods: Various soft wares like Chemsketch, Molinspiration, PASS, and Discovery studio were used to design the proposed derivatives. Evaluation of binding activity against different receptors was detected and checked their physicochemical properties for binding.
Results: In this study, we designed different analogs of 2-aminobenzimidazole into a ligand having a binding affinity with alpha-glucosidase, Dipeptidyl–peptidase 4(DPP4), Peroximase proliferator-activated receptor gamma (PPARγ), and Insulin-like growth factor 1(IGF-1) receptor. The designed ten derivatives showed a significant binding capacity to the concerned receptors.
Conclusion: These results pointed that the designed proposed derivatives promising hypoglycaemic activity.
Downloads
References
Sodik N, Salamet E, Khayrulla B, Muhammed N. Evaluation of the antidiabetic activity and chemical composition of geranium collinum root extracts-computational and experimental investigations. Molecules 2017;22:983-2.
Fabiyi Edebor TD, Fasanmade AA. Evaluation of the characteristics of diabetes induced by the administration of alloxan to fructose fed wistar rat. Int J Pharm Sci Res 2019;10:881-9.
Kumar JA, Tiwari AK, Ali AZ, Madhusudhana K, Reddy BS, Ramakrishna S, et al. New antihyperglycemic, alpha-glucosidase inhibitory, and cytotoxic derivatives of benzimidazole. J Enzyme Inhib Med Chem 2010;25:80-6.
Rolee P, Mary B. Dipeptidyl peptidase (DPP-4) inhibitors in the management of diabetes. PT 2010;35:509-13.
Tandrasasmita OM, Wulan DD, Nailufar F, Sinambela J, Tjandrawinata RR. Glucose-lowering effect of DLBS3233 is mediated through phosphorylation of tyrosine and upregulation of PPARγ and GLUT4 expression. Int J Gen Med 2014;14:345–7.
Diogo A, Fonseca IA, Ana C, Maria D. Orphan drugs: major development challenges at the clinical stage. Drug Discovery Today 2019;10:15-7.
Amy C, Anderson. The process of structure based drug design. Chem Bio 2003;10:787-97.
Andrei T, Christopher A, Iiya WA. Docking of protein models. Protein Sci 2002;11:1888-96.
Antonio PP, Philip R. Idiopathic type1diabetes. J Diabetes Its Complicat 2001;15:328-5.
Ashutosh P, Selvam G, Jeyakandan, Ashok M. Lead discovery and lead optimazation: a useful strategy in molecular modification of lead compound in analogue design. Int J Drug Design Discovery 2011;2:458-63.
Athar M, Das AJ. Current trends in drug discovery: target identification to clinical development of the drug. Int Res J Pharm 2012;3:23-7.
Brain K, Irwin D, Dale L. Molecular docking using shape descriptors. J Com Chem 1992;13:380-97.
Christopher A, Lipinski. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technol 2004;1:337-41.
Christopher W, Murray, David E, Timothy R. Pro-select: combining structure based drug design and combinatorial chemistry for rapid lead discovery. J Comput Aided Mol Des 1997;11:193-207.
Chun MS, Shen JL, Joo CT. Recent advances in computer-aided drug design. Brief Bioinformatics 2009;10:579-91.
Dar A, Mir S. Molecular docking: appraches, types, applications and basic challenges. J Anal Bioanal Tech 2017;8:356-61.
Hughes JP, Rees S, Kalindjan SB, Philpott KL. Principles of drug discovery. Br J Pharmacol 2011;162:1239-49.
Ahamed Z, Prashanta CN, Perumal V, Mafibaniasadi Z. Insilico analysis of active constituents of silymarin as alphaglucosidase enzyme inhibitors in type 2 diabetes mellitus. AJPCR 2019;12:225-9.
Gurula H, Loganathan T, Vashum Y, Paneetselvam S, Vetrivel U, Samuel S. Insilico screening of potent PPAR gamma agonists among natural anticancer comounds of Indian orgin. AJPCR 2016;9:320-4.
Wadood A, Ahmed N, Shah L, Ahmad A, Hassan H, Shams S. In silico drug design: An approach which revolution arised the drug discovery process, OA Drug Design Delivery 2013;1:3-10.
Xiang M, Cao Y, Fan W, Chen L, Mo Y. Computer–aided drug design: lead discovery and optimazation. Comb Chem High Throughput Screening 2012;15:328-37.
Lipinski CA. Lead and drug like compounds; the rule of five revolution: drug. Discovery Today 2004;1:337-41.
Tetko IV, Bruneau P, Mewes H, Rohrer D, Poda G. Can we estimate the accuracy of ADMET predictions? Drug Discovery Today 2006;11:700-7.
Prasad G, Mahavir H, Balaji RA. Software based approaches for drug designing and development: a systematic review on commonly used softwares and its applications. Bull Faculty Pharm Cairo University 2017;55:203-10.
Patrick O, Mridula S, Wen Hui T, Hor KC, Kavitha G, Anand G. Invitri antidiabetic activity and in silico studies of binding energies of palmatine with alpha amylase, alpha glucosidase and DPP4 enzymes. Pharmacia 2020;67:363-71.
Abraham G, Yezyn G, Elix AD, Samuel E. Design, synthesis, antihyperglycemic studies and docking simulations of benzimidazole-thiazolidinedione hybrids. J Chem 2019;45:217-22.
Usha T, Shanmugarajan D, Goyal AK, Kumar C. Recent updates on computer aided drug discovery time for a paradigm shift. Curr Topics Med Chem 2017;17:3296-307.
Published
How to Cite
Issue
Section
Copyright (c) 2021 Sreeja S, Anton Smith A, Mathan S, Sunilkumar D, Vani V
This work is licensed under a Creative Commons Attribution 4.0 International License.
