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Current Traditional Medicine

Editor-in-Chief

ISSN (Print): 2215-0838
ISSN (Online): 2215-0846

Research Article

Molecular Docking Studies and Pharmacophore Modeling of Some Insulin Mimetic Agents from Herbal Sources: A Rational Approach towards Designing of Orally Active Insulin Mimetic Agents

Author(s): Joohee Pradhan* and Sunita Panchawat

Volume 6, Issue 2, 2020

Page: [121 - 133] Pages: 13

DOI: 10.2174/2215083805666191001220342

Price: $65

Abstract

Background: Many herbal drugs have been found to possess oral insulin mimetic property as evidenced from the literature. Although, to date there is no efficient, synthetic orally active insulin-mimetic drug available clinically. Computer-Aided Drug Design (CADD) may help in the development of such agents through Pharmacophore modeling.

Objective: The present work is aimed at the In-silico designing of Pharmacophore that defines the structural requirements of a molecule to possess oral insulin-mimetic properties.

Methods: A set of 16 orally active insulin-mimetic natural compounds available through literature was used to develop a structure-based pharmacophore in a “three-step filtration process” comprised of Lipinski’s rule of 5, Minimum binding energy with the receptor and Ghose filter to the Lipinski’s rule for oral bioavailability of the drugs. The selected ligands were docked with phosphorylated insulin receptor tyrosine kinase in complex with peptide substrate and ATP analog (PDB ID: 1IR3) using Autodock 4.2 and their interaction with the receptor was analyzed followed by the generation of shared and merged feature pharmacophore by Ligandscout 4.2.1.

Results: There are three important structural features that contribute to interaction with the active site of the insulin receptor: these are hydrogen bond donor groups, hydrogen bond acceptor groups and hydrophobic interactions. It is important to note that positive or negative ionizable groups or the presence of aromatic rings are not important for the activity.

Conclusion: Taking a clue from the developed pharmacophore, one may design new lead having necessary groups required for the insulin-mimetic activity that can be elaborated synthetically to get a series of compounds with possible oral insulin-mimetic activity.

Keywords: Diabetes mellitus, insulin mimetic, orally active, structure-based pharmacophore modeling, docking, hydrophobic interactions.

Graphical Abstract

[1]
Amos AF, McCarty DJ, Zimmet P. The rising global burden of diabetes and its complications: Estimates and projections to the year 2010. Diabet Med 1997; 14(Suppl. 5): S1-S85.
[http://dx.doi.org/10.1002/(SICI)1096-9136(199712)14:5+<S7:AID-DIA522>3.3.CO;2-I] [PMID: 9450510]
[2]
Balasubramanyam M, Mohan V. Orally active insulin mimics: Where do we stand now? J Biosci 2001; 26(3): 383-90.
[http://dx.doi.org/10.1007/BF02703748] [PMID: 11568484]
[3]
Alan LH. Plant Natural Products in Anti-Diabetic Drug Discovery. Curr Org Chem 2010; 14(16): 1670-7.
[http://dx.doi.org/10.2174/138527210792927681]
[4]
Yang SY. Pharmacophore modeling and applications in drug discovery: Challenges and recent advances. Drug Discov Today 2010; 15(11-12): 444-50.
[http://dx.doi.org/10.1016/j.drudis.2010.03.013] [PMID: 20362693]
[5]
Patel AD, Barot R, Parmar I, et al. Molecular docking, in-silico ADMET study and development of 1,6- dihydropyrimidine derivative as protein tyrosine phosphatase inhibitor: An approach to design and develop antidiabetic agents. Curr Comput Aided Drug Des 2018; 14(4): 349-62.
[http://dx.doi.org/10.2174/1573409914666180426125721] [PMID: 29701158]
[6]
Selvaraj J. Identification of new antidiabetic agents targeting GLUT4 protein using in silico analysis. Int J Green Pharm IJGP 2019; 12(4): 1-2.
[http://dx.doi.org/10.22377/ijgp.v12i04.2269]
[7]
Ahmed D, Khan MI, Kaithwas G, et al. Molecular docking analysis and antidiabetic activity of Rifabutin against STZ-NA induced diabetes in albino wistar rats. Beni-Suef University J Basic Appl Sci 2017; 6(3): 269-84.
[http://dx.doi.org/10.1016/j.bjbas.2017.04.010]
[8]
Mahendran G, Manoj M, Murugesh E, et al. In vivo anti-diabetic, antioxidant and molecular docking studies of 1, 2, 8-trihydroxy-6-methoxy xanthone and 1, 2-dihydroxy-6-methoxyxanthone-8-O-β-D-xylopy-ranosyl isolated from Swertia corymbosa. Phytomedicine 2014; 21(11): 1237-48.
[http://dx.doi.org/10.1016/j.phymed.2014.06.011] [PMID: 25172785]
[9]
Bharathi A, Roopan SM, Vasavi CS, Munusami P, Gayathri GA, Gayathri M. In silico molecular docking and in vitro antidiabetic studies of dihydropyrimido[4,5-a]acridin-2-amines. BioMed Res Int 2014.2014971569
[http://dx.doi.org/10.1155/2014/971569] [PMID: 24991576]
[10]
Bibi S, Sakata K. Current Status of Computer-Aided Drug Design for Type 2 Diabetes. Curr Comput Aided Drug Des 2016.
[http://dx.doi.org/10.2174/1573409912666160426120709]
[11]
Clark DE. What has computer-aided molecular design ever done for drug discovery? Expert Opin Drug Discov 2006; 1(2): 103-10.
[http://dx.doi.org/10.1517/17460441.1.2.103] [PMID: 23495794]
[12]
Talele TT, Khedkar SA, Rigby AC. Successful applications of computer aided drug discovery: Moving drugs from concept to the clinic. Curr Top Med Chem 2010; 10(1): 127-41.
[http://dx.doi.org/10.2174/156802610790232251] [PMID: 19929824]
[13]
Hubbard SR. Crystal structure of the activated insulin receptor tyrosine kinase in complex with peptide substrate and ATP analog. EMBO J 1997; 16(18): 5572-81.
[http://dx.doi.org/10.1093/emboj/16.18.5572] [PMID: 9312016]
[14]
Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009; 30(16): 2785-91.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[15]
Patel DK, Prasad SK, Kumar R, Hemalatha S. An overview on antidiabetic medicinal plants having insulin mimetic property. Asian Pac J Trop Biomed 2012; 2(4): 320-30.
[http://dx.doi.org/10.1016/S2221-1691(12)60032-X] [PMID: 23569923]
[17]
Guex N, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 1997; 18(15): 2714-23.
[http://dx.doi.org/10.1002/elps.1150181505] [PMID: 9504803]
[18]
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001; 46(1-3): 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[19]
O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011; 3: 33.
[http://dx.doi.org/10.1186/1758-2946-3-33] [PMID: 21982300]
[21]
Pettersen EF, Goddard TD, Huang CC, et al. UCSF Chimera- A visualization system for exploratory research and analysis. J Comput Chem 2004; 25(13): 1605-12.
[http://dx.doi.org/10.1002/jcc.20084] [PMID: 15264254]
[22]
Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 2005; 45(1): 160-9.
[http://dx.doi.org/10.1021/ci049885e] [PMID: 15667141]
[23]
Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. A qualitative and quantitative characterization of known drug databases. J Comb Chem 1999; 1(1): 55-68.
[http://dx.doi.org/10.1021/cc9800071] [PMID: 10746014]
[24]
Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002; 45(12): 2615-23.
[http://dx.doi.org/10.1021/jm020017n] [PMID: 12036371]

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