Combinatorial design and virtual screening of potent anti-tubercular fluoroquinolone and isothiazoloquinolone compounds utilizing QSAR and pharmacophore modellingFootnote^$

$ Presented at the 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2017, 27–30 October 2017, Goa, India.

References

• C. Dye, S. Scheele, P. Dolin, V. Pathania, and M.C. Raviglione, Consensus statement. Global burden of tuberculosis estimated incidence, prevalence, and mortality by country, J. Am. Med. Assoc. 282 (1999), pp. 677–686.
   PubMed Web of Science ®Google Scholar
• T.R. Frieden, T.R. Sterling, S.S. Munsiff, C.J. Watt, and C. Dye, Tuberculosis, Lancet 362 (2003), pp. 887–889.
   PubMed Web of Science ®Google Scholar
• S.H. Kaufmann, Tuberculosis deadly combination, Nature 453 (2008), pp. 295–296.
   PubMed Web of Science ®Google Scholar
• A. Konstantinos, Testing for tuberculosis, Aust. Prescr. 33 (2010), pp. 12–18.
   Web of Science ®Google Scholar
• S.D. Lawn and A.I. Zumla, Tuberculosis, Lancet 378, 9785, (2011), pp. 57–72.
   Web of Science ®Google Scholar
• V. ZylSmit, R.N. Pai, M. Yew, W.W. Leung, C.C. Zumla, A. Bateman, and E.D.K. Dheda, Global lung health, the colliding epidemics of tuberculosis, tobacco smoking, HIV and COPD, Eur. Respir. J. 35 (2010), pp. 27–33.
   PubMed Web of Science ®Google Scholar
• T. Parish and N. Stoker, Mycobacteria, bugs and bugbears (two steps forward and one step back), Mol. Biotechnol. 13 (1999), pp. 191–200.
   PubMed Web of Science ®Google Scholar
• R.E. Chaisson and N.A. Martinson, Tuberculosis in Africa—combating an HIV-driven crisis, N. Engl. J. Med. 358 (2008), pp. 1089–1092.
   PubMed Web of Science ®Google Scholar
• World Health Organization, Global Tuberculosis Report, 20th ed. Geneva, Switzerland, World Health Organization, 2015.
   Google Scholar
• K.D. Tripathi, Essentials of Medical Pharmacology, 6th ed. New Delhi, Jaypee Brothers Medical Publishers (P) Ltd, 2006, pp. 745–750.
   Google Scholar
• E.L. Nuermberger, M.K. Spigelman, and W. Yew, Current development and future prospects in chemotherapy of tuberculosis, Respirology 15 (2010), pp. 764–778.
   PubMed Web of Science ®Google Scholar
• Y.L. Janin, Antituberculosis drugs, ten years of research, Bioorg. Med. Chem. 15 (2007), pp. 2479–2513.
   PubMed Web of Science ®Google Scholar
• A. Zumla, P. Nahid, and S.T. Cole, Advances in the development of new tuberculosis drugs and treatment regimen, Nat. Rev. Drug Discov. 12 (2013), pp. 388–404.
   PubMed Web of Science ®Google Scholar
• N.X. Chin, D.C. Britain, and H.C. Neu, In vitro activity of Ro-23-6240, a new fluorinated 4-quinolon, Antimicrob. Agents Chemother. 29 (1986), pp. 675–680.
   PubMed Web of Science ®Google Scholar
• D. Chu, P. Fernandes, A. Claiborne, E. Pihuleac, C. Nordeen, R. Maleczka, and A. Pernet, Synthesis and structure activity relationships of novel arylfluoroquinolone antibacterial agents, J. Med. Chem. 28 (1985), pp. 1558–1564.
   PubMed Web of Science ®Google Scholar
• J.M. Domagala, S.E. Hagen, C.L. Heifetz, M.P. Huff, T.F. Mich, J.P. Sanchez, and A.K. Trehan, 7-Substituted 5-amino-1-cyclopropyl-6,8-difluoro-1,4-dihydro-4-oxo-3-quinolinecarboxylic acid. Synthesis and biological activity of a new class of quinolone antibacterials, J. Med. Chem. 31 (1988), pp. 503–506.
   PubMed Web of Science ®Google Scholar
• P. Ghosh, M. Thanadath, and M.C. Bagchi, On an aspect of calculated molecular descriptors in QSAR studies of quinolone antibacterials, Mol. Divers. 10 (2006), pp. 415–427.
   PubMed Web of Science ®Google Scholar
• M.C. Bagchi, D. Mills, and S.C. Basak, Quantitative structure-activity relationship (QSAR) studies of quinolone antibacterials against M. fortuitum and M. smegmatis using theoretical molecular descriptors, J. Mol. Model. 13 (2007), pp. 111–120.
   PubMed Web of Science ®Google Scholar
• M. Nikola, M. Vracko, and S. Tom, Quantitative structure–activity relationship study of antitubercularfluoroquinolones, Mol. Divers. 15 (2011), pp. 417–426.
   PubMed Web of Science ®Google Scholar
• M. Vracko, M. Nikola, and H. Karoly, Ranking of QSAR models to predict minimal inhibitory concentrations toward Mycobacterium tuberculosis for a set of fluoroquinolones, Acta Chim. Slov. 57 (2010), pp. 586–590.
   PubMed Web of Science ®Google Scholar
• A. Bhattacharjee, B.J. Mylliemngap, and D. Velmurugan, 3D-QSAR studies on fluroquinolones derivatives as inhibitors for tuberculosis, Bioinformation 8 (2012), pp. 381–387.
   PubMedGoogle Scholar
• M.J. Pucci, M. Ackerman, J.A. Thanassi, C.M. Shoen, and M.H. Cynamon, In vitro antituberculosis activities of ACH-702, a novel isothiazoloquinolone, against quinolone-susceptible and quinolone-resistant isolates, Antimicrob. Agents Chem. 54 (2010), pp. 3478–3480.
   PubMed Web of Science ®Google Scholar
• P. Ghosh and M.C. Bagchi, Anti-tubercular drug designing by structure based screening of combinatorial libraries, J. Mol. Model. 17 (2011), pp. 1607–1620.
   PubMed Web of Science ®Google Scholar
• M. Nikola, A. Perdih, and T. Solmajer, Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: A chemometric andmolecular modeling assessment, J. Mol. Model. 18 (2012), pp. 1735–1753.
   PubMed Web of Science ®Google Scholar
• T.E. Reanau, J.P. Sanchiez, J.W. Gage, J.A. Dever, M.A. Shapiro, S.J. Gracheck, and J.M. Domagala, Structure-activity relationships of the quinolone antibacterials against mycobacteria: Effect of structural changes at N-1 and C-7, J. Med. Chem. 39 (1996), pp. 729–735.
   PubMed Web of Science ®Google Scholar
• T.E. Reanau, J.W. Gage, J.A. Dever, G.E. Roland, E.T. Joannides, M.A. Shapiro, J.P. Sanchiez, S.J. Gracheck, J.M. Domagala, M.R. Jacobs, and R.C. Reynolds, Structure-activity relationships of quinolone agents against mycobacteria, effect of structural modification at the 8 position, Antimicrob. Agents Chemother. 40 (1996), pp. 2363–2368.
   PubMed Web of Science ®Google Scholar
• N. Mills, ChemDraw Ultra 10.0, J Am ChemSoc 128 (2006), pp. 13649–13650.
   Google Scholar
• R. Todeschini and V. Consonni, Dragon software (version 5.4), Talete srl., Milano, Italy, 2006; software available at http://www.talete.mi.it/
   Google Scholar
• J.H. Holland, Ridge regression biased estimation for nonorthagonal problems, Technometrics 8 (1975), pp. 27–51.
   Google Scholar
• L.J. De Campos and E.B. De Melo, Modeling structure-activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS, J. Mol. Graph Model. 54 (2014), pp. 19–31.
   PubMed Web of Science ®Google Scholar
• D. Broadhurst, R. Goodacre, J.J. Jone Rowland, and B.D. Kell, Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry, Anal. Chim. Acta 348 (1997), pp. 71–86.
   Web of Science ®Google Scholar
• B.T. Hoffman, T. Kopajtic, J.L. Katz, and A.H. Newman, 2D QSAR modeling and preliminary database searching for dopamine transporter inhibitors using genetic algorithm variable selection of Molconn Z descriptors, J. Med. Chem. 43 (2000), pp. 4151–4159.
   PubMed Web of Science ®Google Scholar
• R. Leardi, R. Boggia, and M. Terrile, Genetic algorithm as a strategy for feature selection, J. Chemom. 6 (1992), pp. 267–281.
   Web of Science ®Google Scholar
• A.K. Saxena and P. Prathipati, Comparison of MLR, PLS and GA-MLR in QSAR analysis, SAR QSAR Environ. Res. 14 (2003), pp. 433–445.
   PubMed Web of Science ®Google Scholar
• S. Nandi and M.C. Bagchi, QSAR modeling of 4-anilinofuro [2, 3-b]quinolines: An approach to anticancer drug design, Med. Chem. Res. 23 (2014), pp. 1672–1682.
   Web of Science ®Google Scholar
• P. Ambure, R.B. Aher, A. Gajewicz, A.T. Puzyn, and K. Roy, “NanoBRIDGES” software, Open access tools to perform QSAR and nano-QSAR modeling, Chemom. Intel. Lab. Sys. 147 (2015), pp. 1–13.
   Web of Science ®Google Scholar
• P.P. Roy and K. Roy, On some aspects of variable selection for partial least squares regression models, QSAR Comb. Sci. 27 (2008), pp. 302–313.
   Google Scholar
• K. Roy, P. Chakraborty, I. Mitra, P.K. Ojha, S. Kar, and R.N. Das, Some case studies on application of “rm 2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data, J. Comput. Chem. 34 (2013), pp. 1071–1082.
   PubMed Web of Science ®Google Scholar
• P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring r m 2 metrics for validation of QSPR models, Chemom. Intel. Lab. Sys. 107 (2011), pp. 194–205.
   Web of Science ®Google Scholar
• G. Lowe, Combinatorial chemistry, Chem. Soc. Rev. 24 (1995), pp. 309–382.
   Web of Science ®Google Scholar
• M.A. Gallop, R.W. Barrett, W.J. Dower, S.P.A. Fodor, and A.M. Gordon, Applications of combinatorial technologies to drug discovery, J. Med. Chem. 37 (1994), pp. 1233–1251.
   PubMed Web of Science ®Google Scholar
• K.D. Janda, Tagged versus untagged libraries: Methods for the generation and screening of combinatorial chemical libraries, Proc. Acad. Sci. USA 91 (1994), pp. 10779–10785.
   PubMed Web of Science ®Google Scholar
• P. Neha and S. Nandi, In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors, SpringerPlus 4 (2015), p. 819.
   PubMed Web of Science ®Google Scholar
• D.T.W. Chu and P.B. Fernandes, Structure-activity relationships of the fluoroquinolones, Antimicrob. Agents Chemother. 33 (1989), pp. 131–135.
   PubMed Web of Science ®Google Scholar
• D.P. Chu, F.A. Claiborne, E. Pihuleac, C. Nordeen, R. Maleczka, and A. Pernet, Synthesis and structure activity relationships of novel arylfluoroquinolone antibacterial agents, J. Med. Chem. 28 (1985), pp. 1558–1564.
   PubMed Web of Science ®Google Scholar
• C.A. Lipinski, F. Lombardo, B.W. Dominy, and P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug. Deliv. Rev. 23 (1997), pp. 3–25.
   Web of Science ®Google Scholar
• D.J. Abraham, Burger’s Medicinal Chemistry and Drug Discovery, Vol. 1, Hoboken, NJ, Wiley-Interscience, 2003.
   Google Scholar
• A.R. Leach, V.J. Gillet, R.A. Lewis, and R. Taylor, Three dimensional pharmacophore methods in drug discovery, J. Med. Chem. 53 (2009), pp. 539–558.
   Web of Science ®Google Scholar
• G. Wolber and T. Langer, LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters, J. Chem. Inf. Model. 45 (2005), pp. 160–169.
   PubMed Web of Science ®Google Scholar
• T. Langer and E.M. Krovat, Chemical feature-based pharmacophores and virtual library screening for discovery of new leads, Curr. Opin. Drug Discov. Dev. 6 (2003), pp. 370–376.
   PubMed Web of Science ®Google Scholar
• D. Schuster and T. Langer, The identification of ligand features essential for PXR activation by pharmacophore modelling, J. Chem. Info. Model. 45 (2005), p. 431–439.
   PubMed Web of Science ®Google Scholar
• A. Batra, S. Nandi, and M.C. Bagchi, QSAR and pharmacophore modeling of indole-based C-3 pyridone compounds as HCV NS5B polymerase inhibitors utilizing computed molecular descriptors, Med. Chem. Res. 24 (2015), pp. 2432–2440.
   Web of Science ®Google Scholar
• K.W. Ronald and A. Larry Stone, Computer aided design of experiments, Technometric 11 (1969), pp. 137–148.
   Web of Science ®Google Scholar
• C.A. Lipinski, F. Lombardo, B.W. Dominy, and P.J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. 23 (1997), pp. 3–25.
   Web of Science ®Google Scholar
• OECD, Guidance document on the validation of (quantitative) structure–activity relationships QSARs Models, ENV/JM/MONO, Paris, OECD. (2007). pp. 15 http://www.oecd.org/
   Google Scholar
• S. Saxena, S.S. Chaudhaery, K. Varshney, and A.K. Saxena, Pharmacophore-based virtual screening and docking studies on Hsp90 inhibitor, SAR QSAR Environ. Res. 21 (2010), pp. 445–462.
   PubMed Web of Science ®Google Scholar
• A.K. Gupta, S.S. Bhunia, V.M. Balaramnavar, and A.K. Saxena, Pharmacophore modelling, molecular docking and virtual screening for EGFR (HER 1) tyrosine kinase inhibitors, SAR QSAR Environ. Res. 22 (2011), pp. 239–263.
   PubMed Web of Science ®Google Scholar
• V.K. Vyas, G. Qureshi, M. Ghate, H. Patel, and S. Dalai, Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity, SAR QSAR Environ. Res. 27 (2016), pp. 427–440.
   PubMed Web of Science ®Google Scholar
• S. Janardhan, L. John, M. Prasanthi, V. Poroikov, and G. Narahari Sastry, A QSAR and molecular modelling study towards new lead finding: Polypharmacological approach to Mycobacterium tuberculosis, SAR QSAR Environ. Res. 28 (2017), pp. 815–832.
   PubMed Web of Science ®Google Scholar
• M.A. Azam and J. Thathan, Pharmacophore generation, atom-based 3D-QSAR and molecular dynamics simulation analyses of pyridine-3-carboxamide-6-yl-urea analogues as potential gyrase B inhibitors, SAR QSAR Environ. Res. 28 (2017), pp. 275–296.
   PubMed Web of Science ®Google Scholar