Generic placeholder image

Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Research Article

A Combination of Pharmacophore Generation, Ligand-based Virtual Screening, Atom-based 3D-QSAR, and Molecular Docking Studies on Febuxostat-based Amides Analogues as Anti-inflammatory Agents

Author(s): Trupti S. Chitre*, Aniket L. Bhatambrekar, Purvaj V. Hirode, Shubhangi B. Thorat, Sayli G. Hajare, Dinesh R. Garud, Sakshi M. Jagdale and Kalyani D. Asgaonkar

Volume 22, Issue 1, 2025

Published on: 11 March, 2024

Article ID: e110324227883 Pages: 20

DOI: 10.2174/0115701638281229240226101906

Price: $65

Open Access Journals Promotions 2
Abstract

Background: A defence mechanism of the body includes inflammation. It is a process through which the immune system identifies, rejects, and starts to repair foreign and damaging stimuli. In the world, chronic inflammatory disorders are the leading cause of death.

Materials and Methods: To obtain optimized pharmacophore, previously reported febuxostat- based anti-inflammatory amide derivatives series were subjected to pharmacophore hypothesis, ligand-based virtual screening, and 3D-QSAR studies in the present work using Schrodinger suite 2022-4. QuikProp module of Schrodinger was used for ADMET prediction, and HTVS, SP, and XP protocols of GLIDE modules were used for molecular docking on target protein (PDB ID:3LN1).

Result: Utilising 29 compounds, a five-point model of common pharmacophore hypotheses was created, having pIC50 ranging between 5.34 and 4.871. The top pharmacophore hypothesis AHHRR_ 1 model consists of one hydrogen bond acceptor, two hydrophobic groups and two ring substitution features. The hypothesis model AHHRR_1 underwent ligand-based virtual screening using the molecules from Asinex. Additionally, a 3D-QSAR study based on individual atoms was performed to assess their contributions to model development. The top QSAR model was chosen based on the values of R2 (0.9531) and Q2 (0.9424). Finally, four potential hits were obtained by molecular docking based on virtual screening.

Conclusion: The virtual screen compounds have shown similar docking interaction with amino acid residues as shown by standard diclofenac sodium drugs. Therefore, the findings in the present study can be explored in the development of potent anti-inflammatory agents.

Keywords: Febuxostat, anti-inflammatory, common pharmacophore hypothesis, ligand-based virtual screening, 3D-QSAR, asinex database, schrodinger.

Graphical Abstract
[1]
Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med 2019; 25(12): 1822-32.
[http://dx.doi.org/10.1038/s41591-019-0675-0] [PMID: 31806905]
[2]
Barcelos IP, Troxell RM, Graves JS. Mitochondrial dysfunction and multiple sclerosis. Biology 2019; 8(2): 37.
[http://dx.doi.org/10.3390/biology8020037] [PMID: 31083577]
[3]
Bennett JM, Reeves G, Billman GE, Sturmberg JP. Inflammation–Nature’s way to efficiently respond to all types of challenges: Implications for understanding and managing “the epidemic” of chronic diseases. Front Med 2018; 5: 316.
[http://dx.doi.org/10.3389/fmed.2018.00316] [PMID: 30538987]
[4]
Deepak P, Axelrad JE, Ananthakrishnan AN. The role of the radiologist in determining disease severity in inflammatory bowel diseases. Gastrointest Endosc Clin N Am 2019; 29(3): 447-70.
[http://dx.doi.org/10.1016/j.giec.2019.02.006] [PMID: 31078247]
[5]
Kaduševičius E. Novel applications of NSAIDs: Insight and future perspectives in cardiovascular, neurodegenerative, diabetes and cancer disease therapy. Int J Mol Sci 2021; 22(12): 6637.
[http://dx.doi.org/10.3390/ijms22126637] [PMID: 34205719]
[6]
Van Durme CM, Wechalekar MD, Buchbinder R, Schlesinger N, Van Der Heijde D, Landewé RB. Non-steroidal anti-inflammatory drugs for acute gout. Cochrane Database of Systematic Reviews 2012; 2014(9): CD010120.
[http://dx.doi.org/10.1002/14651858.CD010120]
[7]
Wang W, Pang J, Ha EH, et al. Development of novel NLRP3-XOD dual inhibitors for the treatment of gout. Bioorg Med Chem Lett 2020; 30(4): 126944.
[http://dx.doi.org/10.1016/j.bmcl.2019.126944] [PMID: 31924495]
[8]
Coburn BW, Mikuls TR. Treatment options for acute gout. Fed Pract 2016; 33(1): 35-40.
[PMID: 30766136]
[9]
Almeer RS, Hammad SF, Leheta OF, Abdel Moneim AE, Amin HK. Anti-inflammatory and anti-hyperuricemic functions of two synthetic hybrid drugs with dual biological active sites. Int J Mol Sci 2019; 20(22): 5635.
[http://dx.doi.org/10.3390/ijms20225635] [PMID: 31718011]
[10]
Cronstein BN, Terkeltaub R. The inflammatory process of gout and its treatment. Arthritis Res Ther 2006; 8(S1): S3.
[http://dx.doi.org/10.1186/ar1908] [PMID: 16820042]
[11]
Rashad AY, Kassab SE, Daabees HG, Moneim AAE, Rostom SAF. Febuxostat-based amides and some derived heterocycles targeting xanthine oxidase and COX inhibition. Synthesis, in vitro and in vivo biological evaluation, molecular modeling and in silico ADMET studies. Bioorg Chem 2021; 113: 104948.
[http://dx.doi.org/10.1016/j.bioorg.2021.104948] [PMID: 34052736]
[12]
Rajeswari M, Santhi N, Bhuvaneswari V. Pharmacophore and virtual screening of JAK3 inhibitors. Bioinformation 2014; 10(3): 157-63.
[http://dx.doi.org/10.6026/97320630010157] [PMID: 24748756]
[13]
Janardhan S, John L, Prasanthi M, Poroikov V, Narahari Sastry G. A QSAR and molecular modelling study towards new lead finding: Polypharmacological approach to Mycobacterium tuberculosis. SAR QSAR Environ Res 2017; 28(10): 815-32.
[http://dx.doi.org/10.1080/1062936X.2017.1398782] [PMID: 29183232]
[14]
Mohan A, Rendine N, Mohammed MKS, Jeeva A, Ji HF, Talluri VR. Structure-based virtual screening, in silico docking, ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 Mpro. Mol Divers 2022; 26(3): 1645-61.
[http://dx.doi.org/10.1007/s11030-021-10298-0] [PMID: 34480682]
[15]
Shah UA, Deokar HS, Kadam SS, Kulkarni VM. Pharmacophore generation and atom-based 3D-QSAR of novel 2-(4-methylsulfonylphenyl)pyrimidines as COX-2 inhibitors. Mol Divers 2010; 14(3): 559-68.
[http://dx.doi.org/10.1007/s11030-009-9183-3] [PMID: 19669924]
[16]
Release S. 2023-2: Maestro. New York, NY: Schrödinger, LLC 2023.
[17]
Mills N. ChemDraw Ultra 10.0 CambridgeSoft, 100 CambridgePark Drive, Cambridge, MA 02140. www.cambridgesoft.com. Commercial Price: $1910 for download, $2150 for CD-ROM; Academic Price: $710 for download, $800 for CD-ROM. J Am Chem Soc 2006; 128(41): 13649-50.
[http://dx.doi.org/10.1021/ja0697875]
[18]
Release S. 2023-2: LigPrep. New York, NY: Schrödinger, LLC 2021.
[19]
Guo J, Janet JP, Bauer MR, et al. DockStream: A docking wrapper to enhance de novo molecular design. J Cheminform 2021; 13(1): 89.
[http://dx.doi.org/10.1186/s13321-021-00563-7] [PMID: 34789335]
[20]
Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA. PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 2006; 20(10-11): 647-71.
[http://dx.doi.org/10.1007/s10822-006-9087-6] [PMID: 17124629]
[21]
Seidel T, Ibis G, Bendix F, Wolber G. Strategies for 3D pharmacophore-based virtual screening. Drug Discov Today Technol 2010; 7(4): e221-8.
[http://dx.doi.org/10.1016/j.ddtec.2010.11.004] [PMID: 24103798]
[22]
Reddy KK, Singh SK, Dessalew N, Tripathi SK, Selvaraj C. Pharmacophore modelling and atom-based 3D-QSAR studies on N -methyl pyrimidones as HIV-1 integrase inhibitors. J Enzyme Inhib Med Chem 2012; 27(3): 339-47.
[http://dx.doi.org/10.3109/14756366.2011.590803] [PMID: 21699459]
[23]
Ali A, Ali A, Warsi MH, Rahman MA, Ahsan MJ, Azam F. An insight into the structural requirements and pharmacophore identification of carbonic anhydrase inhibitors to combat oxidative stress at high altitudes: An in-silico approach. Curr Issues Mol Biol 2022; 44(3): 1027-45.
[http://dx.doi.org/10.3390/cimb44030068] [PMID: 35723291]
[24]
Pérez-Regidor L, Zarioh M, Ortega L, Martín-Santamaría S. Virtual screening approaches towards the discovery of toll-like receptor modulators. Int J Mol Sci 2016; 17(9): 1508.
[http://dx.doi.org/10.3390/ijms17091508] [PMID: 27618029]
[25]
Ibrahim IM, Elfiky AA, Fathy MM, Mahmoud SH, ElHefnawi M. Targeting SARS-CoV-2 endoribonuclease: A structure-based virtual screening supported by in vitro analysis. Sci Rep 2022; 12(1): 13337.
[http://dx.doi.org/10.1038/s41598-022-17573-6] [PMID: 35922447]
[26]
Tripathi AC, Sonar PK, Rathore R, Saraf SK. Structural insights into the molecular design of HER2 inhibitors. Open Pharm Sci J 2016; 3(1): 164-81.
[http://dx.doi.org/10.2174/1874844901603010164]
[27]
Cappel D, Dixon SL, Sherman W, Duan J. Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling. J Comput Aided Mol Des 2015; 29(2): 165-82.
[http://dx.doi.org/10.1007/s10822-014-9813-4] [PMID: 25408244]
[28]
Bouaziz-Terrachet S, Terrachet R, Taïri-Kellou S. Receptor and ligand-based 3D-QSAR study on a series of nonsteroidal anti-inflammatory drugs. Med Chem Res 2013; 22(4): 1529-37.
[http://dx.doi.org/10.1007/s00044-012-0174-z]
[29]
Prabhu VS, Singh SK. Atom-based 3D-QSAR, induced fit docking, and molecular dynamics simulations study of thieno[2,3-b]pyridines negative allosteric modulators of mGluR5. J Recept Signal Transduct Res 2018; 38(3): 225-39.
[http://dx.doi.org/10.1080/10799893.2018.1476542] [PMID: 29806525]
[30]
Release S. 2023-2: QikProp. New York, NY: Schrödinger, LLC 2023.
[31]
Ntie-Kang F. An in silico evaluation of the ADMET profile of the StreptomeDB database. Springerplus 2013; 2(1): 353.
[http://dx.doi.org/10.1186/2193-1801-2-353] [PMID: 23961417]
[32]
Release S. 2023-2: Protein Preparation Wizard; Epik, Schrödinger, LLC, New York, NY, 2023; Impact, Schrödinger, LLC, New York, NY. New York, NY: Prime, Schrödinger, LLC 2023.
[33]
Madhavi Sastry G, Adzhigirey M, Day T, Annabhimoju R, Sherman W. Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 2013; 27(3): 221-34.
[http://dx.doi.org/10.1007/s10822-013-9644-8] [PMID: 23579614]
[34]
Farid R, Day T, Friesner RA, Pearlstein RA. New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg Med Chem 2006; 14(9): 3160-73.
[http://dx.doi.org/10.1016/j.bmc.2005.12.032] [PMID: 16413785]
[35]
Kouassi ARK, Ganiyou A, Benié A, et al. Identification of potential C-kit protein kinase inhibitors associated with human liver cancer: Atom-based 3D-QSAR modeling, pharmacophores-based virtual screening and molecular docking studies. Am J Pharmacol Sci 2021; 9(1): 1-29.
[http://dx.doi.org/10.12691/ajps-9-1-1]
[36]
Vilar S, Ferino G, Phatak SS, Berk B, Cavasotto CN, Costanzi S. Docking-based virtual screening for ligands of G protein-coupled receptors: Not only crystal structures but also in silico models. J Mol Graph Model 2011; 29(5): 614-23.
[http://dx.doi.org/10.1016/j.jmgm.2010.11.005] [PMID: 21146435]
[37]
Tamilvanan T, Hopper W. High-throughput virtual screening and docking studies of matrix protein Vp40 of ebola virus. Bioinformation 2013; 9(6): 286-92.
[http://dx.doi.org/10.6026/97320630009286] [PMID: 23559747]
[38]
Varpe BD, Jadhav SB, Chatale BC, Mali AS, Jadhav SY, Kulkarni AA. 3D-QSAR and pharmacophore modeling of 3,5-disubstituted indole derivatives as pim kinase inhibitors. Struct Chem 2020; 31(5): 1675-90.
[http://dx.doi.org/10.1007/s11224-020-01503-1]
[39]
Chitre TS, Asgaonkar KD, Patil SM, Kumar S, Khedkar VM, Garud DR. QSAR, docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives for antitubercular activity. Comput Biol Chem 2017; 68: 211-8.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.03.015] [PMID: 28411471]
[40]
Suryanarayanan V, Sudha A, Rajamanikandan S, Vanajothi R, Srinivasan P. Atom-based 3D QSAR studies on novel N-β-d-xylosylindole derivatives as SGLT2 inhibitors. Med Chem Res 2013; 22(2): 615-24.
[http://dx.doi.org/10.1007/s00044-012-0053-7]
[41]
Kakarla P, Inupakutika M, Devireddy AR, et al. 3D-QSar and contour map analysis of tariquidar analogues as multidrug resistance protein-1 (mrp1) inhibitors. Int J Pharm Sci Res 2016; 7(2): 554-72.
[http://dx.doi.org/10.13040/IJPSR.0975-8232.7(2).554-72] [PMID: 26913287]
[42]
Zięba A, Laitinen T, Patel JZ, Poso A, Kaczor AA. Docking-Based 3D-QSAR Studies for 1,3,4-oxadiazol-2-one Derivatives as FAAH Inhibitors. Int J Mol Sci 2021; 22(11): 6108.
[http://dx.doi.org/10.3390/ijms22116108] [PMID: 34204026]
[43]
Divyashri G, Krishna Murthy TP, Sundareshan S, et al. In silico approach towards the identification of potential inhibitors from Curcuma amada Roxb against H. pylori : ADMET screening and molecular docking studies. Bioimpacts 2020; 11(2): 119-27.
[http://dx.doi.org/10.34172/bi.2021.19] [PMID: 33842282]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy