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image of Investigation of Novel Etoricoxib Analogues as Potential COX-II Inhibitors through a Bioisosteric Strategy, ADMET Evaluations, Docking Studies, and Molecular Dynamics Simulations

Abstract

Background

Inflammation is a natural process; however, chronic inflammation may result in numerous health issues. Etoricoxib (ETX), a selective cyclooxygenase-2 (COX-2) inhibitor, serves as an anti-inflammatory agent for various types of arthritis. However, prolonged use of ETX is associated with several adverse effects, including cardiovascular toxicity.

Objective

The current research aims to design analogues of ETX having superior pharmacokinetic properties and safer toxicological profiles employing the bioisosteric approach.

Methods

The bioisosteres of various groups in ETX were produced utilizing the MolOpt online tool, resulting in the generation of novel ETX analogues. The pharmacokinetics (ADME) and toxicological profiles of the generated analogues were calculated by ADMETLab 3.0 server. The druglikeness (DL) and drugscore (DS) were calculated using OSIRIS property explorer (PEO). The molecular docking analysis of the ETX analogues against the target protein (PDB ID: 5KIR) was carried out using AutoDock Vina, and their results were visualized by Discovery Studio 2021. Molecular dynamics (MD) simulation of the top three complexes was conducted using the Schrödinger suite. Binding free energy for the A098-5KIR, A188-5KIR, and D121-5KIR complexes was conducted using MM-GBSA/PBSA method.

Results

A total of 1200 ETX bioisosteres were produced; among them, 51 were screened on the basis of ADMET profile, DL, and DS scores and selected for the docking study. A docking study revealed that 12 analogues show good interactions and docking scores. Furthermore, the MD simulation of ligands A098, A188, and D121 demonstrated stability throughout the 100 ns simulation period.

Conclusion

The findings of the ADMET study, DL, DS, docking study, MD simulation, and binding free energy calculation indicate that the analogues A098, A188, and D121, which are bioisosteres of ETX, may serve as potential anti-inflammatory agents for inflammation-related disorders.

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2025-06-30
2025-09-04
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References

  1. Gusev E. Zhuravleva Y. Inflammation: A new look at an old problem. Int. J. Mol. Sci. 2022 23 9 4596 10.3390/ijms23094596 35562986
    [Google Scholar]
  2. Ahmed A.U. An overview of inflammation: Mechanism and consequences. Front. Biol. 2011 6 4 274 281 10.1007/s11515‑011‑1123‑9
    [Google Scholar]
  3. Kemer S. Meti̇n S. Sural G. Demi̇rel Yilmaz E. Autacoids in the inflammation. Turk Hij. Deney. Biyol. Derg. 2022 79 4 784 801 10.5505/TurkHijyen.2022.66742
    [Google Scholar]
  4. Schmid-Schönbein G.W. Analysis of inflammation. Annu. Rev. Biomed. Eng. 2006 8 1 93 151 10.1146/annurev.bioeng.8.061505.095708 16834553
    [Google Scholar]
  5. Sharma S. Kumar D. Singh G. Monga V. Kumar B. Recent advancements in the development of heterocyclic anti-inflammatory agents. Eur. J. Med. Chem. 2020 200 112438 10.1016/j.ejmech.2020.112438 32485533
    [Google Scholar]
  6. Bindu S. Mazumder S. Bandyopadhyay U. Non-steroidal anti-inflammatory drugs (NSAIDs) and organ damage: A current perspective. Biochem. Pharmacol. 2020 180 114147 10.1016/j.bcp.2020.114147 32653589
    [Google Scholar]
  7. Ju Z. Li M. Xu J. Howell D.C. Li Z. Chen F.E. Recent development on COX-2 inhibitors as promising anti-inflammatory agents: The past 10 years. Acta Pharm. Sin. B 2022 12 6 2790 2807 10.1016/j.apsb.2022.01.002 35755295
    [Google Scholar]
  8. Zarghi A. Arfaei S. Selective COX-2 inhibitors: A review of their structure-activity relationships. Iran. J. Pharm. Res. 2011 10 4 655 683 24250402
    [Google Scholar]
  9. Laube M. Kniess T. Pietzsch J. Radiolabeled COX-2 inhibitors for non-invasive visualization of COX-2 expression and activity--a critical update. Molecules 2013 18 6 6311 6355 10.3390/molecules18066311 23760031
    [Google Scholar]
  10. Bacchi S. Palumbo P. Sponta A. Coppolino M.F. Clinical pharmacology of non-steroidal anti-inflammatory drugs: A review. Antiinflamm. Antiall Agents Med. Chem. 2012 11 1 52 64 10.2174/187152312803476255 22934743
    [Google Scholar]
  11. Arfeen M. Srivastava A. Srivastava N. Khan R.A. Almahmoud S.A. Mohammed H.A. Design, classification, and adverse effects of NSAIDs: A review on recent advancements. Bioorg. Med. Chem. 2024 112 117899 10.1016/j.bmc.2024.117899 39217686
    [Google Scholar]
  12. Kaes C. Katz A. Hosseini M.W. Bipyridine: The most widely used ligand. A review of molecules comprising at least two 2,2′-bipyridine units. Chem. Rev. 2000 100 10 3553 3590 10.1021/cr990376z 11749322
    [Google Scholar]
  13. Capone M.L. Tacconelli S. Patrignani P. Clinical pharmacology of etoricoxib. Expert Opin. Drug Metab. Toxicol. 2005 1 2 269 282 10.1517/17425255.1.2.269 16922642
    [Google Scholar]
  14. Day R.O. Graham G.G. Non-steroidal anti-inflammatory drugs (NSAIDs). BMJ 2013 346 f3195 10.1136/bmj.f3195 23757736
    [Google Scholar]
  15. Smith W.L. Prostanoid biosynthesis and mechanisms of action. Am. J. Physiol. Renal Physiol. 1992 263 2 F181 F191 10.1152/ajprenal.1992.263.2.F181 1324603
    [Google Scholar]
  16. Tazawa R. Xu X.M. Wu K.K. Wang L.H. Characterization of the genomic structure, chromosomal location and promoter of human prostaglandin H synthase-2 gene. Biochem. Biophys. Res. Commun. 1994 203 1 190 199 10.1006/bbrc.1994.2167 8074655
    [Google Scholar]
  17. Simmons D.L. Botting R.M. Hla T. Cyclooxygenase isozymes: The biology of prostaglandin synthesis and inhibition. Pharmacol. Rev. 2004 56 3 387 437 10.1124/pr.56.3.3 15317910
    [Google Scholar]
  18. Ahmadi M. Bekeschus S. Weltmann K.D. von Woedtke T. Wende K. Non-steroidal anti-inflammatory drugs: Recent advances in the use of synthetic COX-2 inhibitors. RSC Med. Chem. 2022 13 5 471 496 10.1039/D1MD00280E 35685617
    [Google Scholar]
  19. Martina S.D. Vesta K.S. Ripley T.L. Etoricoxib: A highly selective COX-2 inhibitor. Ann. Pharmacother. 2005 39 5 854 862 10.1345/aph.1E543 15827069
    [Google Scholar]
  20. Hunt R.H. Harper S. Watson D.J. Yu C. Quan H. Lee M. Evans J.K. Oxenius B. The gastrointestinal safety of the COX-2 selective inhibitor etoricoxib assessed by both endoscopy and analysis of upper gastrointestinal events. Am. J. Gastroenterol. 2003 98 8 1725 1733 10.1111/j.1572‑0241.2003.07598.x 12907325
    [Google Scholar]
  21. Takemoto J.K. Reynolds J.K. Remsberg C.M. Vega-Villa K.R. Davies N.M. Clinical pharmacokinetic and pharmacodynamic profile of etoricoxib. Clin. Pharmacokinet. 2008 47 11 703 720 10.2165/00003088‑200847110‑00002 18840026
    [Google Scholar]
  22. Quercia O. Emiliani F. Foschi F.G. Stefanini G.F. Safety of etoricoxib in patients with reactions to NSAIDs. J. Investig. Allergol. Clin. Immunol. 2008 18 3 163 167 18564626
    [Google Scholar]
  23. Borham L. Adverse cardiovascular events by non-steroidal anti-inflammatory drugs. EC Pharmacol. Toxicol. 2020 8 1 10
    [Google Scholar]
  24. Trelle S. Reichenbach S. Wandel S. Hildebrand P. Tschannen B. Villiger P.M. Egger M. Jüni P. Cardiovascular safety of nonsteroidal anti-inflammatory drugs: Network meta-analysis. BMJ 2011 342 jan11 1 c7086 10.1136/bmj.c7086 21224324
    [Google Scholar]
  25. Roubille C. Martel-Pelletier J. Davy J.M. Haraoui B. Pelletier J.P. Cardiovascular adverse effects of anti-inflammatory drugs. Antiinflamm. Antiallergy Agents Med. Chem. 2013 12 1 55 67 10.2174/187152313804998687 23286294
    [Google Scholar]
  26. Arora M. Choudhary S. Singh P.K. Sapra B. Silakari O. Structural investigation on the selective COX-2 inhibitors mediated cardiotoxicity: A review. Life Sci. 2020 251 117631 10.1016/j.lfs.2020.117631 32251635
    [Google Scholar]
  27. Jayashree B.S. Nikhil P.S. Paul S. Bioisosterism in drug discovery and development-an overview. Med. Chem. 2022 18 9 915 925 10.2174/1573406418666220127124228 35086456
    [Google Scholar]
  28. Dick A. Cocklin S. Bioisosteric replacement as a tool in anti-HIV drug design. Pharmaceuticals 2020 13 3 36 10.3390/ph13030036 32121077
    [Google Scholar]
  29. Lima L. Barreiro E. Bioisosterism: A useful strategy for molecular modification and drug design. Curr. Med. Chem. 2005 12 1 23 49 10.2174/0929867053363540 15638729
    [Google Scholar]
  30. Gupta A.K. Vaishnav Y. Jain S.K. Annadurai S. Kumar N. Exploring novel Apalutamide analogues as potential therapeutics for prostate cancer: Design, molecular docking investigations and molecular dynamics simulation. Front Chem. 2024 12 1418975 10.3389/fchem.2024.1418975 39165335
    [Google Scholar]
  31. Shan J. Ji C. MolOpt: A web server for drug design using bioisosteric transformation. Curr. Computeraided Drug Des. 2020 16 4 460 466 10.2174/1573409915666190704093400 31272357
    [Google Scholar]
  32. Fu L. Shi S. Yi J. Wang N. He Y. Wu Z. Peng J. Deng Y. Wang W. Wu C. Lyu A. Zeng X. Zhao W. Hou T. Cao D. ADMETlab 3.0: An updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Res. 2024 52 W1 W422 W431 10.1093/nar/gkae236 38572755
    [Google Scholar]
  33. Sander T. Freyss J. von Korff M. Reich J.R. Rufener C. OSIRIS, an entirely in-house developed drug discovery informatics system. J. Chem. Inf. Model. 2009 49 2 232 246 10.1021/ci800305f 19434825
    [Google Scholar]
  34. Agarwal S. Mehrotra R.J.J.C. An overview of molecular docking. JSM Chem. 2016 4 2 1024 1028
    [Google Scholar]
  35. Leão R.P. Cruz J.V. da Costa G.V. Cruz J.N. Ferreira E.F.B. Silva R.C. de Lima L.R. Borges R.S. dos Santos G.B. Santos C.B.R. Identification of new rofecoxib-based cyclooxygenase-2 inhibitors: A bioinformatics approach. Pharmaceuticals 2020 13 9 209 10.3390/ph13090209 32858871
    [Google Scholar]
  36. O’Boyle N.M. Banck M. James C.A. Morley C. Vandermeersch T. Hutchison G.R. Open Babel: An open chemical toolbox. J. Cheminform. 2011 3 1 33 10.1186/1758‑2946‑3‑33 21982300
    [Google Scholar]
  37. Trott O. Olson A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  38. Studio D. Discovery studio. Accelrys 2008 [2.1] 420
    [Google Scholar]
  39. Alturki N.A. Mashraqi M.M. Alzamami A. Alghamdi Y.S. Alharthi A.A. Asiri S.A. Ahmad S. Alshamrani S. In-silico screening and molecular dynamics simulation of drug bank experimental compounds against SARS-CoV-2. Molecules 2022 27 14 4391 10.3390/molecules27144391 35889265
    [Google Scholar]
  40. Van Der Spoel D. Lindahl E. Hess B. Groenhof G. Mark A.E. Berendsen H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem. 2005 26 16 1701 1718 10.1002/jcc.20291 16211538
    [Google Scholar]
  41. Banks J.L. Beard H.S. Cao Y. Cho A.E. Damm W. Farid R. Felts A.K. Halgren T.A. Mainz D.T. Maple J.R. Murphy R. Philipp D.M. Repasky M.P. Zhang L.Y. Berne B.J. Friesner R.A. Gallicchio E. Levy R.M. Integrated modeling program, applied chemical theory (IMPACT). J. Comput. Chem. 2005 26 16 1752 1780 10.1002/jcc.20292 16211539
    [Google Scholar]
  42. González-Esparragoza D. Carrasco-Carballo A. Rosas-Murrieta N.H. Millán-Pérez Peña L. Luna F. Herrera-Camacho I. In-silico analysis of protein–protein interactions of putative endoplasmic reticulum metallopeptidase 1 in Schizosaccharomyces pombe. Curr. Issues Mol. Biol. 2024 46 5 4609 4629 10.3390/cimb46050280 38785548
    [Google Scholar]
  43. Ziyaul-Haque M. Ayub R. Mohd Siddique M.U. Gangwal A. Ansari A. Shahid M. Agrawal Y.O. Khan T. Machine learning approaches in designing anti-HIV nitroimidazoles: 2D/3D QSAR, kNN-MFA, docking, dynamics, PCA analysis and MMGBSA studies. Arab. J. Chem. 2024 17 11 105995 10.1016/j.arabjc.2024.105995
    [Google Scholar]
  44. Wang E. Sun H. Wang J. Wang Z. Liu H. Zhang J.Z.H. Hou T. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chem. Rev. 2019 119 16 9478 9508 10.1021/acs.chemrev.9b00055 31244000
    [Google Scholar]
  45. Matore B.W. Roy P.P. Singh J. Discovery of novel VEGFR2-TK inhibitors by phthalimide pharmacophore based virtual screening, molecular docking, MD simulation and DFT. J. Biomol. Struct. Dyn. 2023 41 22 13056 13077 10.1080/07391102.2023.2178510 36775656
    [Google Scholar]
  46. Pollastri M.P. Overview on the rule of five. Curr. Protoc Pharmacol. 2010 Chapter 9 Unit 9.12 10.1002/0471141755.ph0912s49
    [Google Scholar]
  47. Lipinski C.A. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today. Technol. 2004 1 4 337 341 10.1016/j.ddtec.2004.11.007 24981612
    [Google Scholar]
  48. Prasanna S. Doerksen R. Topological polar surface area: A useful descriptor in 2D-QSAR. Curr. Med. Chem. 2009 16 1 21 41 10.2174/092986709787002817 19149561
    [Google Scholar]
  49. Ertl P. Rohde B. Selzer P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 2000 43 20 3714 3717 10.1021/jm000942e 11020286
    [Google Scholar]
  50. Bickerton G.R. Paolini G.V. Besnard J. Muresan S. Hopkins A.L. Quantifying the chemical beauty of drugs. Nat. Chem. 2012 4 2 90 98 10.1038/nchem.1243 22270643
    [Google Scholar]
  51. Ertl P. Schuffenhauer A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 2009 1 1 8 10.1186/1758‑2946‑1‑8 20298526
    [Google Scholar]
  52. Ivanenkov Y.A. Zagribelnyy B.A. Aladinskiy V.A. Are we opening the door to a new era of medicinal chemistry or being collapsed to a chemical singularity? J. Med. Chem. 2019 62 22 10026 10043 10.1021/acs.jmedchem.9b00004 31188596
    [Google Scholar]
  53. Wei W. Cherukupalli S. Jing L. Liu X. Zhan P. Fsp3: A new parameter for drug-likeness. Drug Discov. Today 2020 25 10 1839 1845 10.1016/j.drudis.2020.07.017 32712310
    [Google Scholar]
  54. Lovering F. Bikker J. Humblet C. Escape from flatland: Increasing saturation as an approach to improving clinical success. J. Med. Chem. 2009 52 21 6752 6756 10.1021/jm901241e 19827778
    [Google Scholar]
  55. Kaisar M.A. Sajja R.K. Prasad S. Abhyankar V.V. Liles T. Cucullo L. New experimental models of the blood-brain barrier for CNS drug discovery. Expert Opin. Drug Discov. 2017 12 1 89 103 10.1080/17460441.2017.1253676 27782770
    [Google Scholar]
  56. Smith D.A. Beaumont K. Maurer T.S. Di L. Volume of distribution in drug design. J. Med. Chem. 2015 58 15 5691 5698 10.1021/acs.jmedchem.5b00201 25799158
    [Google Scholar]
  57. Fan J. Shi S. Xiang H. Fu L. Duan Y. Cao D. Lu H. Predicting elimination of small-molecule drug half-life in pharmacokinetics using ensemble and consensus machine learning methods. J. Chem. Inf. Model. 2024 64 8 3080 3092 10.1021/acs.jcim.3c02030 38563433
    [Google Scholar]
  58. Ursu O. Rayan A. Goldblum A. Oprea T.I. Understanding drug‐likeness. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2011 1 5 760 781 10.1002/wcms.52
    [Google Scholar]
  59. Gilson M.K. Zhou H.X. Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct. 2007 36 1 21 42 10.1146/annurev.biophys.36.040306.132550 17201676
    [Google Scholar]
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