Identification of suitable flavonoids as insulin degrading enzyme inhibitors through in-silico approach

Saranya Dharmaraj; Preeya Negi; M. Esakkimuthukumar; Akey Krishna Swaroop; S. Jubie

doi:10.7324/JAPS.2023.137481

Abstract References Article Metrics Similar Articles Request Permission Related Search Citation Alert By Google Scholar Comment On This Article

Abstract

The present work is aimed to identify the inhibitors for insulin-degrading enzyme (IDE) from plant secondary metabolites through in-silico studies. IDE is a protease that cleaves insulin and other bioactive peptides such as amyloid-β. IDE is the important drug target for diabetes because IDE is the principal insulin-degrading protease in vivo, IDE inhibitors should enhance insulin signaling and thus have efficacy in relevant animal models of diabetes and also in therapy. The in-silico absorption distribution metabolism elimination screening was carried out to find out the drug likeness properties of selected flavonoids. In-silico molecular docking simulations have been performed to positional phytoconstituents into the preferred binding site of the protein receptor IDE, to predict the binding modes, the binding affinities and the orientation of all ligands. The docking studies revealed that all compounds showed good docking score. In-silico molecular docking simulations have been performed to positional phytoconstituents into the preferred binding site of the protein receptor IDE, to predict the binding modes, the binding affinities and the orientation of all ligands. The docking studies revealed that all compounds showed good docking score. The prediction of the binding affinity of a new compound to an identified target is a significant parameter in the development of a new drug. It is found that the flavonoids quercetin, genistein, wogonin, isorhamnetin and luteolin had drug like properties rutin and Diosmin are in good bioavailability radar and diosmin, wogonin and flavilium elicit a higher binding affinity with IDE.

Citation:

Dharmaraj S, Negi P, Esakkimuthukumar M, Swaroop AK, Jubie S. Identification of suitable flavonoids as insulin degrading enzyme inhibitors through in-silico approach. J Appl Pharm Sci, 2023; 13(Suppl 1):051–064. https://doi.org/10.7324/JAPS.2023.137481

Copyright: © The Author(s). This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

HTML Full Text

Reference

Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem, 2010; 53:2719-40. https://doi.org/10.1021/jm901137j
Brito Sanchez Y, Marrero-Ponce Y, Barigye SJ, Yaber Goenaga I, Morell Prez C, Le-Thi-Thu H, Cherkasov A. Towards better BBB passage prediction using an extensive and curated data set. Mol Inform, 2015; 34(5):308-30. https://doi.org/10.1002/minf.201400118
Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R, Lai L. Computation of octanol water partition coefficients by guiding an additive model with knowledge. J Chem Inform Model, 2007; 47:2140-8. https://doi.org/10.1021/ci700257y
Daina A, Michielin O, Vincent Z. SwissADME: a free web tool to evaluate pharmacokinetics, drug likeness and medicinal chemistry friendliness of small molecules. Nat Sci Rep, 2017; 7:1-13. https://doi.org/10.1038/srep42717
Darvas F, Keseru G, Papp A, Dorman G, Urge L, Krajcsi P. In silico and ex silico ADME approaches for drug discovery. Top Med Chem, 2002; 2:1287-304. https://doi.org/10.2174/1568026023392841
Di L, Artursson P, Avdeef A, Ecker GF, Faller B, Fischer H, Houston JB, Kansy M, Kerns EH, Krämer SD, Lennernäs H, Sugano K. Evidence-based approach to assess passive diffusion and carrier-mediated drug transport. Drug Discov Today, 2012; 17(15-6):905-12. https://doi.org/10.1016/j.drudis.2012.03.015
Ega W, Mer KM, Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem, 2000; 43(21):3867-77. https://doi.org/10.1021/jm000292e
Gleeson MP, Hersey A, Hannongbua S. In-silico ADME models a general assessment of their utility in drug discovery applications. Curr Topic Med Chem, 2011; 11(4):358-81. https://doi.org/10.2174/156802611794480927
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, 1997; 23:3-5. https://doi.org/10.1016/S0169-409X(96)00423-1
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. https://doi.org/10.1016/S0169-409X(96)00423-1
Lombardo F, Gifford E, Shalaeva MY. In silico ADME prediction: data, models, facts and myths. Mini Rev Med Chem, 2003; 3:861-75. https://doi.org/10.2174/1389557033487629
Mahanthesh MT, Ranjith, Yaligar R, Jyothi R, Narappa G, Ravi MV. Swiss ADME prediction of phytochemicals present in Butea monosperma (Lam.) Taub. J Pharm Phytochem, 2020; 9(3):1799-809.
Moriguchi I, Shuichi H, Nakagome I, Hirano H. Comparison of reliability of log P values for drugs calculated by several methods. Chem Pharm Bull, 1994; 42:976-8. https://doi.org/10.1248/cpb.42.976
Ogu CC, Maxa JL. Drug interactions due to cytochrome P450. Proc (Bayl Univ Med Cent), 2000; 13(4):421-3. https://doi.org/10.1080/08998280.2000.11927719
Wildman SA, Crippen GM. Prediction of physicochemical parameters by atomic contributions. J Chem Inform Model, 1999; 39:868- 73. https://doi.org/10.1021/ci990307l
Yalkowsky SH, Valvani SC. Solubility and partitioning I: solubility of nonelectrolytes in water. J Pharm Sci, 1980; 69:912-22. https://doi.org/10.1002/jps.2600690814
Yi F, Li L, Xu LJ, Meng H, Dong YM, Liu HB, Xiao PG. In silico approach in reveal traditional medicine plants pharmacological material basis. Chinese Med J, 2018; 13(33):1-20. https://doi.org/10.1186/s13020-018-0190-0

Article Metrics

46 Views 59 Downloads 105 Total

Year

Month

Related Search

By author names

Dharmaraj S [PubMed] [Google Scholar ]

Negi P [PubMed] [Google Scholar ]

Esakkimuthukumar M [PubMed] [Google Scholar ]

Swaroop A K [PubMed] [Google Scholar ]

Jubie S [PubMed] [Google Scholar ]

By article title

Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem, 2010; 53:2719-40. https://doi.org/10.1021/jm901137j

Brito Sanchez Y, Marrero-Ponce Y, Barigye SJ, Yaber Goenaga I, Morell Prez C, Le-Thi-Thu H, Cherkasov A. Towards better BBB passage prediction using an extensive and curated data set. Mol Inform, 2015; 34(5):308-30. https://doi.org/10.1002/minf.201400118

Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R, Lai L. Computation of octanol water partition coefficients by guiding an additive model with knowledge. J Chem Inform Model, 2007; 47:2140-8. https://doi.org/10.1021/ci700257y

Daina A, Michielin O, Vincent Z. SwissADME: a free web tool to evaluate pharmacokinetics, drug likeness and medicinal chemistry friendliness of small molecules. Nat Sci Rep, 2017; 7:1-13. https://doi.org/10.1038/srep42717

Darvas F, Keseru G, Papp A, Dorman G, Urge L, Krajcsi P. In silico and ex silico ADME approaches for drug discovery. Top Med Chem, 2002; 2:1287-304. https://doi.org/10.2174/1568026023392841

Di L, Artursson P, Avdeef A, Ecker GF, Faller B, Fischer H, Houston JB, Kansy M, Kerns EH, Krämer SD, Lennernäs H, Sugano K. Evidence-based approach to assess passive diffusion and carrier-mediated drug transport. Drug Discov Today, 2012; 17(15-6):905-12. https://doi.org/10.1016/j.drudis.2012.03.015

Ega W, Mer KM, Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem, 2000; 43(21):3867-77. https://doi.org/10.1021/jm000292e

Gleeson MP, Hersey A, Hannongbua S. In-silico ADME models a general assessment of their utility in drug discovery applications. Curr Topic Med Chem, 2011; 11(4):358-81. https://doi.org/10.2174/156802611794480927

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, 1997; 23:3-5. https://doi.org/10.1016/S0169-409X(96)00423-1

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. https://doi.org/10.1016/S0169-409X(96)00423-1

Lombardo F, Gifford E, Shalaeva MY. In silico ADME prediction: data, models, facts and myths. Mini Rev Med Chem, 2003; 3:861-75. https://doi.org/10.2174/1389557033487629

Mahanthesh MT, Ranjith, Yaligar R, Jyothi R, Narappa G, Ravi MV. Swiss ADME prediction of phytochemicals present in Butea monosperma (Lam.) Taub. J Pharm Phytochem, 2020; 9(3):1799-809.

Moriguchi I, Shuichi H, Nakagome I, Hirano H. Comparison of reliability of log P values for drugs calculated by several methods. Chem Pharm Bull, 1994; 42:976-8. https://doi.org/10.1248/cpb.42.976

Ogu CC, Maxa JL. Drug interactions due to cytochrome P450. Proc (Bayl Univ Med Cent), 2000; 13(4):421-3. https://doi.org/10.1080/08998280.2000.11927719

Wildman SA, Crippen GM. Prediction of physicochemical parameters by atomic contributions. J Chem Inform Model, 1999; 39:868- 73. https://doi.org/10.1021/ci990307l

Yalkowsky SH, Valvani SC. Solubility and partitioning I: solubility of nonelectrolytes in water. J Pharm Sci, 1980; 69:912-22. https://doi.org/10.1002/jps.2600690814

Yi F, Li L, Xu LJ, Meng H, Dong YM, Liu HB, Xiao PG. In silico approach in reveal traditional medicine plants pharmacological material basis. Chinese Med J, 2018; 13(33):1-20. https://doi.org/10.1186/s13020-018-0190-0