Ligand-based Pharmacophore Model for Generation of Active Antidepressant- like Agents from Substituted 1,3,5 Triazine Class

Author(s): Archana Gahtori*, Abhishek Singh.

Journal Name: Current Computer-Aided Drug Design

Volume 16 , Issue 2 , 2020

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Introduction: Although the transition of a lead candidate into a drug is currently structured by well-defined milestone, it is still most challenging and offers no guarantee in success to the end. In fact, ligand-based pharmacophore modeling has become a key motive force for retrieving potential leads across several therapeutic areas.

Methods: An urgent need towards the development of novel antidepressant agents led us to generate a pharmacophore model from an existing 44 compounds dataset. The best model with one hydrophobic, two ring aromatic, and one positive ionization features was chosen on behalf of the correlation coefficient, sensitivity, specificity, yield of actives and accuracy measures using HypoGen module of Discovery Studio. In house library consisting of 10,000 substituted 1,3,5 triazine derivatives were shortlisted to select four insilico hits. All shortlisted compounds were synthesized and characterized by FTIR, 1H-& 13C-NMR spectroscopy and finally tested for antidepressant-like activity using behavioral models on rats viz. Forced Swim Test (FST) and Elevated Plus Maze (EPM).

Results: Two shortlisted compounds with optimal fit values showed a significant decrease in the duration of immobility as compared to standard drug Imipramine in FST while time spent in open arm in enhanced in case of EPM.

Keywords: Ligand-based, EPM, FST, HypoGen module, pharmacophore model, antidepressant-like agents.

[1]
Hefti, F.F. Requirements for a lead compound to become a clinical candidate. BMC Neurosci., 2008, 9(Suppl. 3), S7.
[http://dx.doi.org/10.1186/1471-2202-9-S3-S7] [PMID: 19091004 ]
[2]
Katsila, T.; Spyroulias, G.A.; Patrinos, G.P.; Matsoukas, M.T. Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J., 2016, 14, 177-184.
[http://dx.doi.org/10.1016/j.csbj.2016.04.004] [PMID: 27293534 ]
[3]
Kubinyi, H. Sucess stories of computer-aided design: Computer Applications in Pharmaceutical Research and Development; Sean Ekins, Wiley Interscience John Wiley & Sons, 2006, pp. 378-379.
[4]
Drwal, M.N.; Marinello, J.; Manzo, S.G.; Wakelin, L.P.G.; Capranico, G.; Griffith, R. Novel DNA topoisomerase IIα inhibitors from combined ligand- and structure-based virtual screening. PLoS One, 2014, 9(12) e114904
[http://dx.doi.org/10.1371/journal.pone.0114904] [PMID: 25489853 ]
[5]
Kulkarni, O.P.; Sayyed, S.G.; Kantner, C.; Ryu, M.; Schnurr, M.; Sárdy, M.; Leban, J.; Jankowsky, R.; Ammendola, A.; Doblhofer, R.; Anders, H.J. 4SC-101, a novel small molecule dihydroorotate dehydrogenase inhibitor, suppresses systemic lupus erythematosus in MRL-(Fas)lpr mice. Am. J. Pathol., 2010, 176(6), 2840-2847.
[http://dx.doi.org/10.2353/ajpath.2010.091227] [PMID: 20413687 ]
[6]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2013, 66(1), 334-395.
[http://dx.doi.org/10.1124/pr.112.007336] [PMID: 24381236 ]
[7]
Roy, K.; Kar, S.; Das, R.N. SAR and QSAR in drug discovery and chemical design-some examples, Understanding the basics of QSAR for applications in Pharmaceutical Sciences and Risk assessment; Elsevier: Amsterdam, 2015, pp. 449-450.
[8]
Clark, D.E. What has computer-aided molecular design ever done for drug discovery? Expert Opin. Drug Discov., 2006, 1(2), 103-110.
[http://dx.doi.org/10.1517/17460441.1.2.103] [PMID: 23495794 ]
[9]
Clark, D.E. What has virtual screening ever done for drug discovery? Expert Opin. Drug Discov., 2008, 3(8), 841-851.
[http://dx.doi.org/10.1517/17460441.3.8.841] [PMID: 23484962 ]
[10]
Yang, S.Y. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov. Today, 2010, 15(11-12), 444-450.
[http://dx.doi.org/10.1016/j.drudis.2010.03.013] [PMID: 20362693 ]
[11]
Wermuth, C.G.; Ganellin, C.R.; Lindberg, P.; Mitscher, L.A. Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1997). Annu. Rep. Med. Chem., 1998, 33, 385-395 .https://www.iupac.org/publications/pac-2007/1998/pdf/7005x1129 pdf
[http://dx.doi.org/10.1016/S0065-7743(08)61101-X]
[12]
Khedkar, S.A.; Malde, A.K.; Coutinho, E.C.; Srivastava, S. Pharmacophore modeling in drug discovery and development: an overview. Med. Chem., 2007, 3(2), 187-197.
[http://dx.doi.org/10.2174/157340607780059521] [PMID: 17348856 ]
[13]
World Health Organization. http://www.who.int/news-room/fact-sheets/detail/depression (Accessed March 22, 2018)
[14]
Gilligan, P.J.; He, L.; Clarke, T.; Tivitmahaisoon, P.; Lelas, S.; Li, Y.W.; Heman, K.; Fitzgerald, L.; Miller, K.; Zhang, G.; Marshall, A.; Krause, C.; McElroy, J.; Ward, K.; Shen, H.; Wong, H.; Grossman, S.; Nemeth, G.; Zaczek, R.; Arneric, S.P.; Hartig, P.; Robertson, D.W.; Trainor, G. 8-(4-Methoxyphenyl)pyrazolo[1,5-a]-1,3,5-triazines: selective and centrally active corticotropin-releasing factor receptor-1 (CRF1) antagonists. J. Med. Chem., 2009, 52(9), 3073-3083.
[http://dx.doi.org/10.1021/jm9000242] [PMID: 19361210 ]
[15]
Gahtori, A.; Kumar, A.; Kothiyal, P. Gahtori. Facile and efficient preparation of hybrid phenylthiazolyl-1,3,5-triazines and their antidepressant-like effect in mice. Tetrahedron Lett., 2014, 55(36), 4987-4990.
[http://dx.doi.org/10.1016/j.tetlet.2014.04.014]
[16]
Kalliokoski, T.; Kramer, C.; Vulpetti, A.; Gedeck, P. Comparability of mixed IC50 data - a statistical analysis. PLoS One, 2013, 8(4)e61007
[http://dx.doi.org/10.1371/journal.pone.0061007] [PMID: 23613770 ]
[17]
Sakkiah, S.; Lee, K.W. Pharmacophore-based virtual screening and density functional theory approach to identifying novel butyrylcholinesterase inhibitors. Acta Pharmacol. Sin., 2012, 33(7), 964-978.
[http://dx.doi.org/10.1038/aps.2012.21] [PMID: 22684028 ]
[18]
Acharya, C.; Coop, A.; Polli, J.E.; Mackerell, A.D., Jr Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr. Comput. Aided Drug Des., 2011, 7(1), 10-22.
[http://dx.doi.org/10.2174/157340911793743547] [PMID: 20807187 ]
[19]
Gahtori, P.; Pandey, R.; Kumar, V.; Ghosh, S.K.; Das, A.; Kalita, J.M.; Sahu, S.; Prakash, A.; Bhattacharyya, D.R. Toward resistance-compromised DHFR inhibitors part 1: Combined structure/ligand based virtual screenings and ADME-Tox profiling. J. Chemometr., 2016, 30(8), 462-481.
[http://dx.doi.org/10.1002/cem.2814]
[20]
Blotny, G. Recent applications of 2,4,6-trichloro-1,3,5-triazine and its derivatives in organic synthesis. Tetrahedron, 2006, 62, 9507-9522.
[http://dx.doi.org/10.1016/j.tet.2006.07.039]
[21]
Bhat, H.R.; Singh, U.P.; Thakur, A.; Kumar Ghosh, S.; Gogoi, K.; Prakash, A.; Singh, R.K. Synthesis, antimalarial activity and molecular docking of hybrid 4-aminoquinoline-1,3,5-triazine derivatives. Exp. Parasitol., 2015, 157, 59-67.
[http://dx.doi.org/10.1016/j.exppara.2015.06.016] [PMID: 26164360 ]
[22]
Patel, A.B.; Chikhalia, K.H.; Kumari, P. An efficient synthesis of new thiazolidin-4-one fused s-triazines as potential antimicrobial and anticancer agents. J. Saudi Chem. Soc., 2014, 18(5), 646-656.
[http://dx.doi.org/10.1016/j.jscs.2014.02.002]
[23]
Porsolt, R.D.; Anton, G.; Blavet, N.; Jalfre, M. Behavioural despair in rats: a new model sensitive to antidepressant treatments. Eur. J. Pharmacol., 1978, 47(4), 379-391.
[http://dx.doi.org/10.1016/0014-2999(78)90118-8] [PMID: 204499 ]
[24]
Montgomery, K.C. The relation between fear induced by novel stimulation and exploratory behavior. J. Comp. Physiol. Psychol., 1955, 48(4), 254-260.
[http://dx.doi.org/10.1037/h0043788] [PMID: 13252152 ]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 16
ISSUE: 2
Year: 2020
Page: [167 - 175]
Pages: 9
DOI: 10.2174/1573409915666181219125415
Price: $65

Article Metrics

PDF: 12
HTML: 3
EPUB: 1