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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

Identifying the Structural Features of Diphenyl Ether Analogues for InhA Inhibition: A 2D and 3D QSAR Based Study

Author(s): Ashutosh Prasad Tiwari, Varadaraj Bhat Giliyar*, Gurypur Gautham Shenoy and Vandana Kalwaja Eshwara

Volume 17, Issue 1, 2020

Page: [31 - 47] Pages: 17

DOI: 10.2174/1570180816666190611153933

Abstract

Background: Enoyl acyl carrier protein reductase (InhA) is a validated target for Mycobacterium. It is an enzyme which is associated with the biosynthesis of mycolic acids in type II fatty acid synthase system. Mycobacterial cell wall majorly comprises mycolic acids, which are responsible for virulence of the microorganism. Several diphenyl ether derivatives have been known to be direct inhibitors of InhA.

Objective: In the present work, a Quantitative Structure Activity Relationship (QSAR) study was performed to identify the structural features of diphenyl ether analogues which contribute to InhA inhibitory activity in a favourable way.

Methods: Both 2D and 3D QSAR models were built and compared. Several fingerprint based 2D QSAR models were generated and their relationship with the structural features was studied. Models which corroborated the inhibitory activity of the molecules with their structural features were selected and studied in detail.

Results: A 2D-QSAR model, with dendritic fingerprints having regression coefficient, for test set molecules Q2 =0.8132 and for the training set molecules, R2 =0.9607 was obtained. Additionally, an atom-based 3D-QSAR model with Q2 =0.7697 and R2 =0.9159 was also constructed.

Conclusion: The data reported by various models provides guidance for the designing of structurally new diphenyl ether inhibitors with potential activity against InhA of M. tuberculosis.

Keywords: InhA, M. tuberculosis, antitubercular, 2D-QSAR, 3D-QSAR, fingerprint.

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[1]
BCG vaccine: WHO position paper, February 2018 - Recommendations. Vaccine, 2018, 36(24), 3408-3410.
[http://dx.doi.org/10.1016/j.vaccine.2018.03.009] [PMID: 29609965]
[2]
Forrellad, M.A.; Klepp, L.I.; Gioffré, A.; Sabio, Y.; García, J.; Morbidoni, H.R.; de la Paz Santangelo, M.; Cataldi, A.A.; Bigi, F. Virulence factors of the Mycobacterium tuberculosis complex. Virulence, 2013, 4(1), 3-66.
[http://dx.doi.org/10.4161/viru.22329] [PMID: 23076359]
[3]
Zhang, Y.; Heym, B.; Allen, B.; Young, D.; Cole, S. The catalase-peroxidase gene and isoniazid resistance of Mycobacterium tuberculosis. Nature, 1992, 358(6387), 591-593.
[http://dx.doi.org/10.1038/358591a0] [PMID: 1501713]
[4]
McMurry, L.M.; Oethinger, M.; Levy, S.B. Triclosan targets lipid synthesis. Nature, 1998, 394(6693), 531-532.
[http://dx.doi.org/10.1038/28970] [PMID: 9707111]
[5]
Parikh, S.L.; Xiao, G.; Tonge, P.J. Inhibition of InhA, the enoyl reductase from Mycobacterium tuberculosis, by triclosan and isoniazid. Biochemistry, 2000, 39(26), 7645-7650.
[http://dx.doi.org/10.1021/bi0008940] [PMID: 10869170]
[6]
Luckner, S.R.; Liu, N. am Ende, C.W.; Tonge, P.J.; Kisker, C. A slow, tight binding inhibitor of InhA, the enoyl-acyl carrier protein reductase from Mycobacterium tuberculosis. J. Biol. Chem., 2010, 285(19), 14330-14337.
[http://dx.doi.org/10.1074/jbc.M109.090373] [PMID: 20200152]
[7]
Pan, P.; Knudson, S.E.; Bommineni, G.R.; Li, H.J.; Lai, C.T.; Liu, N.; Garcia-Diaz, M.; Simmerling, C.; Patil, S.S.; Slayden, R.A.; Tonge, P.J. Time-dependent diaryl ether inhibitors of InhA: structure-activity relationship studies of enzyme inhibition, antibacterial activity, and in vivo efficacy. ChemMedChem, 2014, 9(4), 776-791.
[http://dx.doi.org/10.1002/cmdc.201300429] [PMID: 24616444]
[8]
Ende, C.W.; Knudson, S.E.; Liu, N. Antimycobacterial activity of B-ring modified diaryl ether inhA inhibitors. Bioorg. Med. Chem. Lett., 2008, 18(10), 3029-3033.
[http://dx.doi.org/10.1016/j.bmcl.2008.04.038]
[9]
Cinu, T.A.; Sidhartha, S.K.; Indira, B.; Varadaraj, B.G.; Vishnu, P.S.; Shenoy, G.G. Design, synthesis and evaluation of antitubercular activity of Triclosan analogues. Arab. J. Chem., 2015.
[http://dx.doi.org/10.1016/j.arabjc.2015.09.003]
[10]
Lu, H.; Tonge, P.J. Inhibitors of FabI, an enzyme drug target in the bacterial fatty acid biosynthesis pathway. Acc. Chem. Res., 2008, 41(1), 11-20.
[http://dx.doi.org/10.1021/ar700156e] [PMID: 18193820]
[11]
An, Y.; Sherman, W.; Dixon, S.L. Kernel-based partial least squares: application to fingerprint-based QSAR with model visualization. J. Chem. Inf. Model., 2013, 53(9), 2312-2321.
[http://dx.doi.org/10.1021/ci400250c] [PMID: 23901898]
[12]
Pan, P.; Tonge, P.J. Targeting InhA, the FASII enoyl-ACP reductase: SAR studies on novel inhibitor scaffolds. Curr. Top. Med. Chem., 2012, 12(7), 672-693.
[http://dx.doi.org/10.2174/156802612799984535] [PMID: 22283812]
[13]
Announcing Schrödinger Software Release 2018-4, Maestro; Schrödinger, 2018.
[14]
McGregor, M.J.; Pallai, P.V. Clustering of large databases of compounds: Using the MDL “Keys” as structural descriptors. J. Chem. Inf. Comput. Sci., 1997, 37, 443-448.
[http://dx.doi.org/10.1021/ci960151e]
[15]
Heikamp, K.; Bajorath, J. Large-scale similarity search profiling of ChEMBL compound data sets. J. Chem. Inf. Model., 2011, 51(8), 1831-1839.
[http://dx.doi.org/10.1021/ci200199u] [PMID: 21728295]
[16]
Maestro Graphical Interface, Release, S. 2019-1: Canvas; Schrödinger, 2019.
[17]
Duan, J.; Dixon, S.L.; Lowrie, J.F.; Sherman, W. Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods. J. Mol. Graph. Model., 2010, 29(2), 157-170.
[http://dx.doi.org/10.1016/j.jmgm.2010.05.008] [PMID: 20579912]
[18]
Morgan, H.L. The generation of a unique machine description for chemical structures-A technique developed at chemical abstracts service. J. Chem. Doc., 1965, 5, 107-113.
[http://dx.doi.org/10.1021/c160017a018]
[19]
Log , Prep 3.4 user manual, Software Release, 2018-4: LigPrep; Schrödinger, 2018.
[20]
Rosipal, R. Kernel partial least squares for nonlinear regression and discrimination. Neural Netw. World, 2003.
[21]
Dixon, S.L.; Smondyrev, A.M.; Knoll, E.H.; Rao, S.N.; Shaw, D.E.; Friesner, R.A. 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), 647-671.
[http://dx.doi.org/10.1007/s10822-006-9087-6] [PMID: 17124629]
[22]
Kamsri, P.; Punkvang, A.; Saparpakorn, P.; Hannongbua, S.; Irle, S.; Pungpo, P. Elucidating the structural basis of diphenyl ether derivatives as highly potent enoyl-ACP reductase inhibitors through molecular dynamics simulations and 3D-QSAR study. J. Mol. Model., 2014, 20(7), 2319.
[http://dx.doi.org/10.1007/s00894-014-2319-0] [PMID: 24935113]
[23]
Kamsri, P.; Koohatammakun, N.; Srisupan, A.; Meewong, P.; Punkvang, A.; Saparpakorn, P.; Hannongbua, S.; Wolschann, P.; Prueksaaroon, S.; Leartsakulpanich, U.; Pungpo, P. Rational design of InhA inhibitors in the class of diphenyl ether derivatives as potential anti-tubercular agents using molecular dynamics simulations. SAR QSAR Environ. Res., 2014, 25(6), 473-488.
[http://dx.doi.org/10.1080/1062936X.2014.898690] [PMID: 24785640]
[24]
Kumar, V.; Sobhia, M.E. Insights into the bonding pattern for characterizing the open and closed state of the substrate-binding loop in Mycobacterium tuberculosis InhA. Future Med. Chem., 2014, 6(6), 605-616.
[http://dx.doi.org/10.4155/fmc.14.27] [PMID: 24895891]
[25]
Kumar, V.; Sobhia, M.E. Molecular dynamics-based investigation of InhA substrate binding loop for diverse biological activity of direct InhA inhibitors. J. Biomol. Struct. Dyn., 2016, 34(11), 2434-2452.
[http://dx.doi.org/10.1080/07391102.2015.1118410] [PMID: 27206346]

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