Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Letters in Drug Design & Discovery
  4. Volume 21, Issue 15
  5. Article
Volume 21, Issue 15
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

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 NO-inhibitory 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 cross-validated coefficient (2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (2) 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 (2 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.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/lddd/10.2174/0115701808179188231205064327
2024-01-08
2024-12-29

  • From This Site

    /content/journals/lddd/10.2174/0115701808179188231205064327
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. MarlettaM.A. Nitric oxide synthase: Aspects concerning structure and catalysis.Cell199478692793010.1016/0092‑8674(94)90268‑2 7522970
     [Google Scholar]
  2. FörstermannU. ClossE.I. PollockJ.S. NakaneM. SchwarzP. GathI. KleinertH. Nitric oxide synthase isozymes. Characterization, purification, molecular cloning, and functions.Hypertension1994236_pt_21121113110.1161/01.HYP.23.6.1121 7515853
     [Google Scholar]
  3. BoumezberS. YelekçiK. Screening of novel and selective inhibitors for neuronal nitric oxide synthase (nNOS) via structure-based drug design techniques.J. Biomol. Struct. Dyn.20234183607362910.1080/07391102.2022.2054471 35322764
     [Google Scholar]
  4. MariottoS. MenegazziM. SuzukiH. Biochemical aspects of nitric oxide.Curr. Pharm. Des.200410141627164510.2174/1381612043384637 15134561
     [Google Scholar]
  5. SprattD.E. NewmanE. MosherJ. GhoshD.K. SalernoJ.C. GuillemetteJ.G. Binding and activation of nitric oxide synthase isozymes by calmodulin EF hand pairs.FEBS J.200627381759177110.1111/j.1742‑4658.2006.05193.x 16623711
     [Google Scholar]
  6. O’GallagherK. PuleddaF. O’DalyO. RyanM. DancyL. ChowienczykP.J. ZelayaF. GoadsbyP.J. ShahA.M. Neuronal nitric oxide synthase regulates regional brain perfusion in healthy humans.Cardiovasc. Res.202211851321132910.1093/cvr/cvab155 34120160
     [Google Scholar]
  7. BhatrajuP. CrawfordJ. HallM. LangJ.D.Jr Inhaled nitric oxide: Current clinical concepts.Nitric Oxide20155011412810.1016/j.niox.2015.08.007 26335378
     [Google Scholar]
  8. ArnalJ.F. Dinh-XuanA.T. PueyoM. DarbladeB. RamiJ. Endothelium-derived nitric oxide and vascular physiology and pathology.Cell. Mol. Life Sci.19995591078108710.1007/s000180050358 10442089
     [Google Scholar]
  9. MühlH. BachmannM. PfeilschifterJ. Inducible NO synthase and antibacterial host defence in times of Th17/Th22/T22 immunity.Cell. Microbiol.201113334034810.1111/j.1462‑5822.2010.01559.x 21199257
     [Google Scholar]
  10. LiX.A. EversonW. SmartE.J. Nitric oxide, caveolae, and vascular pathology.Cardiovasc. Toxicol.20066111410.1385/CT:6:1:1 16845178
     [Google Scholar]
  11. BredtD.S. Endogenous nitric oxide synthesis: Biological functions and pathophysiology.Free Radic. Res.199931657759610.1080/10715769900301161 10630682
     [Google Scholar]
  12. LirkP. HoffmannG. RiederJ. Inducible nitric oxide synthase--time for reappraisal.Curr. Drug Targets Inflamm. Allergy2002118910810.2174/1568010023344913 14561209
     [Google Scholar]
  13. GrädlerU. FuchßT. UlrichW.R. BoerR. StrubA. HesslingerC. AnézoC. DiederichsK. ZalianiA. Novel nanomolar imidazo[4,5-b]pyridines as selective nitric oxide synthase (iNOS) inhibitors: SAR and structural insights.Bioorg. Med. Chem. Lett.201121144228423210.1016/j.bmcl.2011.05.073 21684157
     [Google Scholar]
  14. MinhasR. BansalY. BansalG. Inducible nitric oxide synthase inhibitors: A comprehensive update.Med. Res. Rev.202040382385510.1002/med.21636 31502681
     [Google Scholar]
  15. MinhasR. BansalY. iNOS inhibitors: Benzimidazole-coumarin derivatives to combat inflammation.Eur. J. Chem.202213330731810.5155/eurjchem.13.3.307‑318.2282
     [Google Scholar]
  16. SerreliG. DeianaM. Role of dietary polyphenols in the activity and expression of nitric oxide synthases: A review.Antioxidants202312114710.3390/antiox12010147 36671009
     [Google Scholar]
  17. SilvaB. BilucaF.C. GonzagaL.V. FettR. DalmarcoE.M. CaonT. CostaA.C.O. In vitro anti-inflammatory properties of honey flavonoids: A review.Food Res. Int.202114111008610.1016/j.foodres.2020.110086 33641965
     [Google Scholar]
  18. MatsudaH. MorikawaT. AndoS. ToguchidaI. YoshikawaM. Structural requirements of flavonoids for nitric oxide production inhibitory activity and mechanism of action.Bioorg. Med. Chem.20031191995200010.1016/S0968‑0896(03)00067‑1 12670650
     [Google Scholar]
  19. KanakavetiV. AnooshaP. SakthivelR. RayalaS.K. GromihaM.M. Quantitative structure-activity relationship in ligand-based drug design: Concepts and applications. Protein Interact.World Scientific2019333349
     [Google Scholar]
  20. BordásB. KőmívesT. LopataA. Ligand‐based computer‐aided pesticide design. A review of applications of the CoMFA and CoMSIA methodologies.Pest Manag. Sci.200359439340010.1002/ps.614 12701699
     [Google Scholar]
  21. VermaJ. KhedkarV. CoutinhoE. 3D-QSAR in drug design--A review.Curr. Top. Med. Chem.20101019511510.2174/156802610790232260 19929826
     [Google Scholar]
  22. MyintK.Z. XieX.Q. Recent advances in fragment-based QSAR and multi-dimensional QSAR methods.Int. J. Mol. Sci.201011103846386610.3390/ijms11103846 21152304
     [Google Scholar]
  23. SrivastavaV. SelvarajC. SinghS.K. Chemoinformatics and QSAR. Advances in Bioinformatics.SingaporeSpringer2021183212
     [Google Scholar]
  24. AparoyP. SureshG.K. Kumar ReddyK. ReddannaP. 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.201121145646210.1016/j.bmcl.2010.10.119 21084193
     [Google Scholar]
  25. PatelB. PatelA. PatelA. BhattH. CoMFA, CoMSIA, molecular docking and MOLCAD studies of pyrimidinone derivatives to design novel and selective tankyrase inhibitors.J. Mol. Struct.2020122112878310.1016/j.molstruc.2020.128783
     [Google Scholar]
  26. WangY. ChangJ. WangJ. ZhongP. ZhangY. LaiC.C. HeY. 3D-QSAR Studies of S-DABO derivatives as non-nucleoside HIV-1 reverse transcriptase inhibitors.Lett. Drug Des. Discov.201916886888110.2174/1570180815666180810112321
     [Google Scholar]
  27. KeretsuS. BhujbalS.P. ChoS.J. Docking and 3D-QSAR studies of hydrazone and triazole derivatives for selective inhibition of GRK2 over ROCK2.Lett. Drug Des. Discov.202017561863210.2174/1570180816666190618105320
     [Google Scholar]
  28. El AissouqA. ChedadiO. BouachrineM. OuammouA. KhalilF. 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.202341104667468010.1080/07391102.2022.2071341 35510607
     [Google Scholar]
  29. GoudzalA. El AissouqA. El HamdaniH. HadajiE.G. OuammouA. BouachrineM. 3D-QSAR modeling and molecular docking studies on a series of 2, 4, 5-trisubstituted imidazole derivatives as CK2 inhibitors.J. Biomol. Struct. Dyn.202341123424810.1080/07391102.2021.2014360 35068344
     [Google Scholar]
  30. RautV.V. BhandariS.V. PatilS.M. SarkateA.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.202320215316610.2174/1570180819666220215115633
     [Google Scholar]
  31. TabtiK. HajjiH. SbaiA. MaghatH. BouachrineM. LakhlifiT. 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. Res202311236938910.22036/PCR.2022.335657.2068
     [Google Scholar]
  32. KlebeG. AbrahamU. MietznerT. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity.J. Med. Chem.199437244130414610.1021/jm00050a010 7990113
     [Google Scholar]
  33. GuptaN. VyasV.K. PatelB.D. GhateM. 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.201911219120510.1007/s12539‑017‑0256‑1 28895050
     [Google Scholar]
  34. AshrafS. RanaghanK.E. WoodsC.J. MulhollandA.J. Ul-HaqZ. Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach.Sci. Rep.20211111870710.1038/s41598‑021‑97368‑3 34548506
     [Google Scholar]
  35. ChenY. MaK. XuP. SiH. DuanY. ZhaiH. Design and screening of new lead compounds for autism based on QSAR model and molecular docking studies.Molecules20222721728510.3390/molecules27217285 36364109
     [Google Scholar]
  36. ShirvaniP. FassihiA. In silico design of novel FAK inhibitors using integrated molecular docking, 3D-QSAR and molecular dynamics simulation studies.J. Biomol. Struct. Dyn.202240135965598210.1080/07391102.2021.1875880 33475043
     [Google Scholar]
/content/journals/lddd/10.2174/0115701808179188231205064327
Loading
3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity
Letters in Drug Design & Discovery 21, 3247 (2024); https://doi.org/10.2174/0115701808179188231205064327
/content/journals/lddd/10.2174/0115701808179188231205064327
/content/journals/lddd/10.2174/0115701808179188231205064327
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): CoMFA; CoMSIA; flavonoids; HQSAR; no inhibition; QSAR

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test