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Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Research Article

3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity

Author(s): Chunqiang Wang, Yuzhu Fan, Minfan Pei, Chaoqun Yan and Taigang Liang*

Volume 21, Issue 15, 2024

Published on: 08 January, 2024

Page: [3247 - 3259] Pages: 13

DOI: 10.2174/0115701808179188231205064327

Price: $65

Abstract

Background: Nitric oxide (NO), an important second messenger molecule, regulates numerous physiological responses, while excessive NO generates negative effects on the circulatory, nervous and immune systems. Recently, some natural flavonoids were reported to possess the capability of inhibiting LPS-induced NO production. To fully understand the nature of their own NO inhibitory activity, it is necessary to address the structural requirements of flavonoids as NO inhibitors.

Objective: The objective of this work was to develop efficient QSAR models for predicting the NOinhibitory activity of new flavonoids and improving insights into the critical properties of the chemical structures that were required for the ideal NO production inhibitory activities.

Methods: To provide insights into the structural basis of flavonoids as NO inhibitors, 3D quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR models were developed on a dataset of 55 flavonoids using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) approaches.

Results: The statistically significant models for CoMFA, CoMSIA and HQSAR resulted in crossvalidated coefficient (q2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (r2) values of 0.793, 0.828 and 0.852, respectively. The robustness of these models was further affirmed using a test set of 18 compounds, which resulted in predictive correlation coefficients (r2 pred) of 0.968, 0.954 and 0.906. Furthermore, the models-derived contour maps were appraised for activity trends for the molecules analyzed.

Conclusion: The 3D and 2D-QSAR models constructed in this paper were efficient in estimating the NO inhibitory activities of flavonoids and facilitating the design of flavonoid-derived NO production inhibitors.

[1]
Marletta, M.A. Nitric oxide synthase: Aspects concerning structure and catalysis. Cell, 1994, 78(6), 927-930.
[http://dx.doi.org/10.1016/0092-8674(94)90268-2] [PMID: 7522970]
[2]
Förstermann, U.; Closs, E.I.; Pollock, J.S.; Nakane, M.; Schwarz, P.; Gath, I.; Kleinert, H. Nitric oxide synthase isozymes. Characterization, purification, molecular cloning, and functions. Hypertension, 1994, 23(6_pt_2), 1121-1131.
[http://dx.doi.org/10.1161/01.HYP.23.6.1121] [PMID: 7515853]
[3]
Boumezber, S.; Yelekçi, K. Screening of novel and selective inhibitors for neuronal nitric oxide synthase (nNOS) via structure-based drug design techniques. J. Biomol. Struct. Dyn., 2023, 41(8), 3607-3629.
[http://dx.doi.org/10.1080/07391102.2022.2054471] [PMID: 35322764]
[4]
Mariotto, S.; Menegazzi, M.; Suzuki, H. Biochemical aspects of nitric oxide. Curr. Pharm. Des., 2004, 10(14), 1627-1645.
[http://dx.doi.org/10.2174/1381612043384637] [PMID: 15134561]
[5]
Spratt, D.E.; Newman, E.; Mosher, J.; Ghosh, D.K.; Salerno, J.C.; Guillemette, J.G. Binding and activation of nitric oxide synthase isozymes by calmodulin EF hand pairs. FEBS J., 2006, 273(8), 1759-1771.
[http://dx.doi.org/10.1111/j.1742-4658.2006.05193.x] [PMID: 16623711]
[6]
O’Gallagher, K.; Puledda, F.; O’Daly, O.; Ryan, M.; Dancy, L.; Chowienczyk, P.J.; Zelaya, F.; Goadsby, P.J.; Shah, A.M. Neuronal nitric oxide synthase regulates regional brain perfusion in healthy humans. Cardiovasc. Res., 2022, 118(5), 1321-1329.
[http://dx.doi.org/10.1093/cvr/cvab155] [PMID: 34120160]
[7]
Bhatraju, P.; Crawford, J.; Hall, M.; Lang, J.D., Jr Inhaled nitric oxide: Current clinical concepts. Nitric Oxide, 2015, 50, 114-128.
[http://dx.doi.org/10.1016/j.niox.2015.08.007] [PMID: 26335378]
[8]
Arnal, J.F.; Dinh-Xuan, A.T.; Pueyo, M.; Darblade, B.; Rami, J. Endothelium-derived nitric oxide and vascular physiology and pathology. Cell. Mol. Life Sci., 1999, 55(9), 1078-1087.
[http://dx.doi.org/10.1007/s000180050358] [PMID: 10442089]
[9]
Mühl, H.; Bachmann, M.; Pfeilschifter, J. Inducible NO synthase and antibacterial host defence in times of Th17/Th22/T22 immunity. Cell. Microbiol., 2011, 13(3), 340-348.
[http://dx.doi.org/10.1111/j.1462-5822.2010.01559.x] [PMID: 21199257]
[10]
Li, X.A.; Everson, W.; Smart, E.J. Nitric oxide, caveolae, and vascular pathology. Cardiovasc. Toxicol., 2006, 6(1), 1-14.
[http://dx.doi.org/10.1385/CT:6:1:1] [PMID: 16845178]
[11]
Bredt, D.S. Endogenous nitric oxide synthesis: Biological functions and pathophysiology. Free Radic. Res., 1999, 31(6), 577-596.
[http://dx.doi.org/10.1080/10715769900301161] [PMID: 10630682]
[12]
Lirk, P.; Hoffmann, G.; Rieder, J. Inducible nitric oxide synthase--time for reappraisal. Curr. Drug Targets Inflamm. Allergy, 2002, 1(1), 89-108.
[http://dx.doi.org/10.2174/1568010023344913] [PMID: 14561209]
[13]
Grädler, U.; Fuchß, T.; Ulrich, W.R.; Boer, R.; Strub, A.; Hesslinger, C.; Anézo, C.; Diederichs, K.; Zaliani, A. Novel nanomolar imidazo[4,5-b]pyridines as selective nitric oxide synthase (iNOS) inhibitors: SAR and structural insights. Bioorg. Med. Chem. Lett., 2011, 21(14), 4228-4232.
[http://dx.doi.org/10.1016/j.bmcl.2011.05.073] [PMID: 21684157]
[14]
Minhas, R.; Bansal, Y.; Bansal, G. Inducible nitric oxide synthase inhibitors: A comprehensive update. Med. Res. Rev., 2020, 40(3), 823-855.
[http://dx.doi.org/10.1002/med.21636] [PMID: 31502681]
[15]
Minhas, R.; Bansal, Y. iNOS inhibitors: Benzimidazole-coumarin derivatives to combat inflammation. Eur. J. Chem., 2022, 13(3), 307-318.
[http://dx.doi.org/10.5155/eurjchem.13.3.307-318.2282]
[16]
Serreli, G.; Deiana, M. Role of dietary polyphenols in the activity and expression of nitric oxide synthases: A review. Antioxidants, 2023, 12(1), 147.
[http://dx.doi.org/10.3390/antiox12010147] [PMID: 36671009]
[17]
Silva, B.; Biluca, F.C.; Gonzaga, L.V.; Fett, R.; Dalmarco, E.M.; Caon, T.; Costa, A.C.O. In vitro anti-inflammatory properties of honey flavonoids: A review. Food Res. Int., 2021, 141, 110086.
[http://dx.doi.org/10.1016/j.foodres.2020.110086] [PMID: 33641965]
[18]
Matsuda, H.; Morikawa, T.; Ando, S.; Toguchida, I.; Yoshikawa, M. Structural requirements of flavonoids for nitric oxide production inhibitory activity and mechanism of action. Bioorg. Med. Chem., 2003, 11(9), 1995-2000.
[http://dx.doi.org/10.1016/S0968-0896(03)00067-1] [PMID: 12670650]
[19]
Kanakaveti, V.; Anoosha, P.; Sakthivel, R.; Rayala, S.K.; Gromiha, M.M. Quantitative structure-activity relationship in ligand-based drug design: Concepts and applications. In: Protein Interact; World Scientific, 2019; pp. 333-349.
[20]
Bordás, B.; Kőmíves, T.; Lopata, A. Ligand‐based computer‐aided pesticide design. A review of applications of the CoMFA and CoMSIA methodologies. Pest Manag. Sci., 2003, 59(4), 393-400.
[http://dx.doi.org/10.1002/ps.614] [PMID: 12701699]
[21]
Verma, J.; Khedkar, V.; Coutinho, E. 3D-QSAR in drug design--A review. Curr. Top. Med. Chem., 2010, 10(1), 95-115.
[http://dx.doi.org/10.2174/156802610790232260] [PMID: 19929826]
[22]
Myint, K.Z.; Xie, X.Q. Recent advances in fragment-based QSAR and multi-dimensional QSAR methods. Int. J. Mol. Sci., 2010, 11(10), 3846-3866.
[http://dx.doi.org/10.3390/ijms11103846] [PMID: 21152304]
[23]
Srivastava, V.; Selvaraj, C.; Singh, S.K. Chemoinformatics and QSAR. In: Advances in Bioinformatics; Springer: Singapore, 2021; pp. 183-212.
[24]
Aparoy, P.; Suresh, G.K.; Kumar Reddy, K.; Reddanna, P. CoMFA and CoMSIA studies on 5-hydroxyindole-3-carboxylate derivatives as 5-lipoxygenase inhibitors: Generation of homology model and docking studies. Bioorg. Med. Chem. Lett., 2011, 21(1), 456-462.
[http://dx.doi.org/10.1016/j.bmcl.2010.10.119] [PMID: 21084193]
[25]
Patel, B.; Patel, A.; Patel, A.; Bhatt, H. CoMFA, CoMSIA, molecular docking and MOLCAD studies of pyrimidinone derivatives to design novel and selective tankyrase inhibitors. J. Mol. Struct., 2020, 1221, 128783.
[http://dx.doi.org/10.1016/j.molstruc.2020.128783]
[26]
Wang, Y.; Chang, J.; Wang, J.; Zhong, P.; Zhang, Y.; Lai, C.C.; He, Y. 3D-QSAR Studies of S-DABO derivatives as non-nucleoside HIV-1 reverse transcriptase inhibitors. Lett. Drug Des. Discov., 2019, 16(8), 868-881.
[http://dx.doi.org/10.2174/1570180815666180810112321]
[27]
Keretsu, S.; Bhujbal, S.P.; Cho, S.J. Docking and 3D-QSAR studies of hydrazone and triazole derivatives for selective inhibition of GRK2 over ROCK2. Lett. Drug Des. Discov., 2020, 17(5), 618-632.
[http://dx.doi.org/10.2174/1570180816666190618105320]
[28]
El Aissouq, A.; Chedadi, O.; Bouachrine, M.; Ouammou, A.; Khalil, F. Development of novel monoamine oxidase B (MAO-B) inhibitors by combined application of docking-based alignment, 3D-QSAR, ADMET prediction, molecular dynamics simulation, and MM_GBSA binding free energy. J. Biomol. Struct. Dyn., 2023, 41(10), 4667-4680.
[http://dx.doi.org/10.1080/07391102.2022.2071341] [PMID: 35510607]
[29]
Goudzal, A.; El Aissouq, A.; El Hamdani, H.; Hadaji, E.G.; Ouammou, A.; Bouachrine, M. 3D-QSAR modeling and molecular docking studies on a series of 2, 4, 5-trisubstituted imidazole derivatives as CK2 inhibitors. J. Biomol. Struct. Dyn., 2023, 41(1), 234-248.
[http://dx.doi.org/10.1080/07391102.2021.2014360] [PMID: 35068344]
[30]
Raut, V.V.; Bhandari, S.V.; Patil, S.M.; Sarkate, A.P. A rational approach to anticancer drug design: 2D and 3D- QSAR, molecular docking and prediction of adme properties using silico studies of thymidine phosphorylase inhibitors. Lett. Drug Des. Discov., 2023, 20(2), 153-166.
[http://dx.doi.org/10.2174/1570180819666220215115633]
[31]
Tabti, K.; Hajji, H.; Sbai, A.; Maghat, H.; Bouachrine, M.; Lakhlifi, T. Identification of a potential thiazole inhibitor against biofilms by 3D-QSAR, molecular docking, DFT analysis, MM-PBSA binding energy calculations, and molecular dynamics simulation. Phys. Chem. Res, 2023, 11(2), 369-389.
[http://dx.doi.org/10.22036/PCR.2022.335657.2068]
[32]
Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem., 1994, 37(24), 4130-4146.
[http://dx.doi.org/10.1021/jm00050a010] [PMID: 7990113]
[33]
Gupta, N.; Vyas, V.K.; Patel, B.D.; Ghate, M. Design of 2-nitroimidazooxazine derivatives as deazaflavin-dependent nitroreductase (Ddn) activators as anti-mycobacterial agents based on 3D-QSAR, HQSAR, and docking study with in silico prediction of activity and toxicity. Interdiscip. Sci., 2019, 11(2), 191-205.
[http://dx.doi.org/10.1007/s12539-017-0256-1] [PMID: 28895050]
[34]
Ashraf, S.; Ranaghan, K.E.; Woods, C.J.; Mulholland, A.J.; Ul-Haq, Z. Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach. Sci. Rep., 2021, 11(1), 18707.
[http://dx.doi.org/10.1038/s41598-021-97368-3] [PMID: 34548506]
[35]
Chen, Y.; Ma, K.; Xu, P.; Si, H.; Duan, Y.; Zhai, H. Design and screening of new lead compounds for autism based on QSAR model and molecular docking studies. Molecules, 2022, 27(21), 7285.
[http://dx.doi.org/10.3390/molecules27217285] [PMID: 36364109]
[36]
Shirvani, P.; Fassihi, A. In silico design of novel FAK inhibitors using integrated molecular docking, 3D-QSAR and molecular dynamics simulation studies. J. Biomol. Struct. Dyn., 2022, 40(13), 5965-5982.
[http://dx.doi.org/10.1080/07391102.2021.1875880] [PMID: 33475043]

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