Abstract
Background: In past few decades, computational chemistry has seen significant advancements in design and development of novel therapeutics. Benzimidazole derivatives showed promising anti-inflammatory activity through the inhibition of COX-2 enzyme.
Objective: The structural features necessary for COX-2 inhibitory activity for a series of oxadiazole substituted benzimidazoles were explored through 3D-QSAR, combinatorial library generation (Combi Lab) and molecular docking.
Methods: 3D-QSAR (using kNN-MFA (SW-FB) and PLSR (GA) methods) and Combi Lab studies were performed by using VLife MDS Molecular Design Suite. The molecular docking study was performed by using AutoDockVina.
Results: Significant QSAR models generated by PLSR exhibited r2 = 0.79, q2 = 0.68 and pred_r2 = 0. 84 values whereas kNN showed q2 = 0.71 and pred_r2 = 0.84. External validation of developed models by various parameters assures their reliability and predictive efficacy. A library of 72 compounds was generated by combinatorial technique in which 11 compounds (A1-A5 and B1-B6) showed better predicted biological activity than the most active compound 27 (pIC50 = 7.22) from the dataset. These compounds showed proximal interaction with amino acid residues like TYR355 and/or ARG120 on COX-2(PDB ID: 4RS0).
Conclusion: The present work resulted in the design of more potent benzimidazoles as COX-2 inhibitors with good interaction as compared to reference ligand. The results of the study may be helpful in the development of novel COX-2 inhibitors for inflammatory disorders.
Keywords: 3D-QSAR, k-nearest neighbor, Lipinski’s rule of five, virtual screening, COX-2, benzimidazole derivatives.
Graphical Abstract
[1]
Lawrence, T.; Willoughby, D.A.; Gilroy, D.W. Anti-inflammatory lipid mediators and insights into the resolution of inflammation. Nat. Rev. Immunol., 2002, 2, 787-795.
[2]
Kurumbail, R.G.; Stevens, A.M.; Gierse, J.K.; McDonald, J.J.; Stegeman, R.A.; Pak, J.Y.; Gildehaus, D.; Miyashiro, J.M.; Penning, T.D.; Seibert, K.; Isakson, P.C.; Stallings, W.C. Structural basis for selective inhibition of cyclooxygenase-2 by anti-inflammatory agents. Nature, 1996, 384, 644-648.
[3]
Smith, W.L.; DeWitt, D.L. Prostaglandin endoperoxide H synthases- 1 and 2. Adv. Immunol., 1996, 62, 167-215.
[5]
Fiorucci, S.; Meli, R.; Bucci, M.; Cirino, G. Dual inhibitors of cyclooxygenase and 5-lipoxygenase: A new avenue in anti-inflammatory therapy. Biochem. Pharmacol., 2001, 62, 1433-1438.
[6]
Amer, M.; Bead, V.R.; Bathon, J.; Blumenthal, R.S.; Edwards, D.N. Use of nonsteroidal anti-inflammatory drugs in patients with cardiovascular disease. Cardiol. Rev., 2010, 18, 204-212.
[8]
Jain, S.V.; Bhadoriya, K.S.; Bari, S.B.; Sahu, N.K.; Ghate, M. Discovery of potent anticonvulsant ligands as dual NMDA and AMPA receptors antagonists by molecular modelling studies. Med. Chem. Res., 2012, 21, 3465-3484.
[9]
Bhadoriya, K.S.; Sharma, M.C.; Jain, S.V.; Kad, S.A.; Raghuvanshi, D. QSAR studies of fused 5,6-bicyclic heterocycles as c-secretase modulators. J. Pharm. Res., 2012, 5(8), 4127-4132.
[10]
Gaba, M.; Singh, D.; Singh, S.; Sharma, V.; Gaba, P. Synthesis and pharmacological evaluation of novel 5-substituted-1-(phenylsulfonyl)- 2-methylbenzimidazole derivatives as anti-inflammatory and analgesic agents. Eur. J. Med. Chem., 2010, 45, 2245-2249.
[11]
Jesudason, E.P.; Sridhar, S.K.; Malar, E.J.P.; Shanmugapandiyan, P.; Inayathullah, M.; Arul, V.; Selvaraj, D.; Jayakumar, R. Synthesis, pharmacological screening, quantum chemical and in vitro permeability studies of N-Mannich bases of benzimidazoles through bovine cornea. Eur. J. Med. Chem., 2009, 44, 2307-2312.
[12]
Refaat, H.M. Synthesis and anticancer activity of some novel 2- substituted benzimidazole derivatives. Eur. J. Med. Chem., 2010, 45, 2949-2956.
[13]
Romero-Castro, A.; Leon-Rivera, I.; Avila-Rojas, L.C.; Navarrete-Vazquez, G.; Nieto-Rodriguez, A. Synthesis and preliminary evaluation of selected 2-aryl-5(6)-nitro-1H-benzimidazole derivatives as potential anticancer agents. Arch. Pharm. Res., 2011, 34, 181-189.
[14]
Abonia, R.; Cortes, E.; Insuasty, B.; Quiroga, J.; Nogueras, M.; Cobo, J. Synthesis of novel 1,2,5-trisubstituted benzimidazoles as potential antitumor agents. Eur. J. Med. Chem., 2011, 46, 4062-4070.
[15]
Shaharyar, M.; Abdullah, M.M.; Bakht, M.A.; Majeed, J. Pyrazoline bearing benzimidazoles: Search for anticancer agents. Eur. J. Med. Chem., 2010, 45, 114-119.
[16]
Rathore, A.; Rahman, M.U.; Siddiqui, A.A.; Ali, A.; Shaharyar, M. Design and synthesis of benzimidazole analogs endowed with oxadiazole as selective COX-2 inhibitor. Arch. Pharm. Chem. Life Sci., 2014, 1, 347.
[17]
Rathore, A.; Rahman, M.U.; Siddiqui, A.A.; Ali, A.; Shaharyar, M. Synthesis and evaluation of benzimidazole derivatives as selective COX-2 inhibitors. Med. Chem., 2015, 11, 188-199.
[18]
VLife M.D.S. 4.4 Molecular design suite. Vlife Sciences Technologies Pvt. Ltd., Pune. Available at; www.vlifesciences.com2014
[19]
Ajmani, S.; Jadhav, K.; Kulkarni, S.A. Three-dimensional QSAR using the k-nearest neighbor method and its interpretation. J. Chem. Inf. Model., 2006, 46, 24-31.
[20]
Clark, M.; Cramer, R.D.; Van, O.N. Validation of the general purpose Tripose 5.2 force field. J. Comput. Chem., 1989, 10, 982-1012.
[21]
Guyon, I.; Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res., 2003, 3, 1157-1182.
[24]
Ghosh, P.; Bagchi, M.C. QSAR modeling for quinoxaline derivatives using genetic algorithm and simulated annealing based feature selection. Curr. Med. Chem., 2009, 16(30), 4032-4048.
[25]
Tropsha, A.; Gramatica, P.; Gombar, V. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. Quant. Str. Act. Rel., 2003, 22, 1-9.
[26]
Shi, L.M.; Fang, H.; Tong, W.D.; Wu, J.; Perkins, R.; Blair, R.M.; Branham, W.S.; Dial, S.L.; Moland, C.I.; Sheehan, D.M. QSAR models using a large diverse set of estrogens. J. Chem. Inf. Comput. Sci., 2001, 41, 186-195.
[27]
Schüurmann, G.; Ebert, R.U.; Chen, J.; Wang, B.; Kuehne, R. External validation and prediction employing the predictive squared correlation coefficient - test set activity mean vs training set activity mean. J. Chem. Inf. Model., 2008, 48, 2140-2145.
[28]
Gramatica, P.; Chirico, N.; Papa, E.; Cassani, S.; Kovarich, S. QSARINS: A new software for the development, analysis, and validation of QSAR MLR models. J. Comp. Chem. Software News Update, 2013, 34, 2121-2132.
[29]
Tropsha, A.; Golbraikh, A. Predictive QSAR modeling work- flow, model applicability domains, and virtual screening. Curr. Pharm. Des., 2007, 13, 3494-3504.
[30]
Eriksson, L.; Jaworska, J.; Worth, A.P.; Cronin, M.T.D.; McDowell, R.M.; Gramatica, P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Perspect., 2003, 111, 1361-1375.
[31]
Roy, P.P.; Roy, K. On some aspects of validation of predictive quantitative structure-activity relationship models. Expert Opin. Drug Discov., 2007, 2, 1567-1577.
[33]
Jaworska, J.; Nikolova, J.N.; Aldenberg, T. QSAR applicabilty domain estimation by projection of the training set descriptor space: A review. Altern. Lab. Anim., 2005, 33, 445-459.
[34]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 1997, 23, 3-25.
[35]
Trott, O.; Olson, A.J. AutoDockVina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comput. Chem., 2010, 31, 455-461.
[36]
Seeliger, D.; Groot, B.L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des., 2010, 24, 417-422.
[37]
Chang, M.W.; Ayeni, C.; Breuer, S.; Torbett, B.E. Virtual Screening for HIV protease inhibitors: A comparison of AutoDock 4 and Vina. PLoS One, 2010, 5(8), e11955.
[38]
Osterberg, F.; Morris, G.M.; Sanner, M.F.; Olson, A.J.; Goodsell, D.S. Automated docking to multiple target structures: Incorporation of protein mobility and structural water heterogeneity in autodock. Proteins: StructFunct Genet., 2002, 46, 34-40.
[39]
Stigliani, J.L.; Bernardes-Génisson, V.; Bernadoua, J.; Pratviela, G. Cross-docking study on InhA inhibitors: A combination of AutodockVina and PM6-DH2 simulations to retrieve bio-active conformations. Org. Biomol. Chem., 2012, 10, 6341-6345.
[40]
Sun, H.; Li, Y.; Shen, M.; Tian, S.; Xu, L.; Pan, P.; Guan, Y.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phy. Chem., 2014, 16(40), 22035-22045.
[41]
Sierra, C.; Ordóñez, C.; Saavedra, A.; Gallego, J.R. Element enrichment factor calculation using grain-size distribution and functional data regression. Chemosphere, 2015, 119, 1192-1199.
[42]
Hamza, A.; Wei, N.N.; Zhan, C.G. Ligand-based virtual screening approach using a new scoring function. J. Chem. Inf. Model., 2012, 52(4), 963-974.
[44]
Becke, A.D. A new mixing of Hartree-Fock and local densityfunctional theories. J. Chem. Phys., 1993, 98(2), 1372.
Article Metrics
67
8