Application of a Validated QSTR Model for Repurposing COX-2 Inhibitor Coumarin Derivatives as Potential Antitumor Agents

Author(s): Gulcin Tugcu , Hande Sipahi* , Ahmet Aydin .

Journal Name: Current Topics in Medicinal Chemistry

Volume 19 , Issue 13 , 2019

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Abstract:

Background: The discovery of novel potent molecules for both cancer prevention and treatment has been continuing over the past decade. In recent years, identification of new, potent, and safe anticancer agents through drug repurposing has been regarded as an expeditious alternative to traditional drug development. The cyclooxygenase-2 is known to be over-expressed in several types of human cancer. For this reason cyclooxygenase-2 inhibition may be useful tool for cancer chemotherapy.

Objective: The first aim of the study was to develop a validated linear model to predict antitumor activity. Subsequently, applicability of the model for repurposing these cyclooxygenase-2 inhibitors as antitumor compounds to abridge drug development process.

Methods: We performed a quantitative structure-toxicity relationship (QSTR) study on a set of coumarin derivatives using a large set of molecular descriptors. A linear model predicting growth inhibition on leukemia CCRF cell lines was developed and consequently validated internally and externally. Accordingly, the model was applied on a set of 143 cyclooxygenase-2 inhibitor coumarin derivatives to explore their antitumor activity.

Results: The results indicated that the developed QSAR model would be useful for estimating inhibitory activity of coumarin derivatives on leukemia cell lines. Electronegativity was found to be a prominent property of the molecules in describing antitumor activity. The applicability domain of the developed model highlighted the potential antitumor compounds.

Conclusion: The promising results revealed that applied integrated in silico approach for repurposing by combining both the biological activity similarity and the molecular similarity via the computational method could be efficiently used to screen potential antitumor compounds among cyclooxygenase-2 inhibitors.

Keywords: QSTR, Coumarin, Leukemia, CCRF, Drug repurposing, COX-2.

[1]
World Health Organization. Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. (Accessed: January 28,2018).. (Available at: https://www.who.int/cancer/PRGlobocanFinal.pdf).
[2]
Pereira, T.M.; Franco, D.P.; Vitorio, F.; Kummerle, A.E. Coumarin compounds in medicinal chemistry: Some important examples from the last years. Curr. Top. Med. Chem., 2018, 18(2), 124-148.
[http://dx.doi.org/10.2174/1568026618666180329115523]. ] [PMID: 29595110]
[3]
Pinto, D.C.G.A.; Silva, A.M.S. Anticancer natural coumarins as lead compounds for the discovery of new drugs. Curr. Top. Med. Chem., 2017, 17(29), 3190-3198.
[PMID: 29243581]
[4]
Kaur, M.; Kohli, S.; Sandhu, S.; Bansal, Y.; Bansal, G. Coumarin: A promising scaffold for anticancer agents. Anticancer. Agents Med. Chem., 2015, 15(8), 1032-1048.
[http://dx.doi.org/10.2174/1871520615666150101125503] [PMID: 25553437]
[5]
Venkata Sairam, K.; Gurupadayya, B.M.; Chandan, R.S.; Nagesha, D.K.; Vishwanathan, B. A review on chemical profile of coumarins and their therapeutic role in the treatment of cancer. Curr. Drug Deliv., 2016, 13(2), 186-201.
[http://dx.doi.org/10.2174/1567201812666150702102800]. ] [PMID: 26135671]
[6]
An, R.; Hou, Z.; Li, J-T.; Yu, H-N.; Mou, Y-H.; Guo, C. Design, synthesis and biological evaluation of novel 4-substituted coumarin derivatives as antitumor agents. Molecules, 2018, 23(9), 2281.
[http://dx.doi.org/10.3390/molecules23092281] [PMID: 30200625]
[7]
Miri, R.; Nejati, M.; Saso, L.; Khakdan, F.; Parshad, B.; Mathur, D.; Parmar, V.S.; Bracke, M.E.; Prasad, A.K.; Sharma, S.K.; Firuzi, O. Structure-activity relationship studies of 4-methylcoumarin derivatives as anticancer agents. Pharm. Biol., 2016, 54(1), 105-110.
[http://dx.doi.org/10.3109/13880209.2015.1016183]. ] [PMID: 26017566]
[8]
Xu, X-C. COX-2 inhibitors in cancer treatment and prevention, a recent development. Anticancer Drugs, 2002, 13(2), 127-137.
[http://dx.doi.org/10.1097/00001813-200202000-00003] [PMID: 11901304]
[9]
Chen, P.C.; Liu, X.; Lin, Y. Drug repurposing in anticancer reagent development. Comb. Chem. High Throughput Screen., 2017, 20(5), 395-402.
[http://dx.doi.org/10.2174/1386207319666161226143424] [PMID: 28025934]
[10]
Bhattarai, D.; Singh, S.; Jang, Y.; Hyeon Han, S.; Lee, K.; Choi, Y. An insight into drug repositioning for the development of novel anti-cancer drugs. Curr. Top. Med. Chem., 2016, 16(19), 2156-2168.
[http://dx.doi.org/10.2174/1568026616666160216153618] [PMID: 26881715]
[11]
Devinyak, O.; Zimenkovsky, B.; Lesyk, R. Biologically active 4-thiazolidinones: A review of QSAR studies and QSAR modeling of antitumor activity. Curr. Top. Med. Chem., 2012, 12(24), 2763-2784.
[http://dx.doi.org/10.2174/1568026611212240006] [PMID: 23368102]
[12]
Sabt, A.; Abdelhafez, O.M.; El-Haggar, R.S.; Madkour, H.M.F.; Eldehna, W.M.; El-Khrisy, E.E.A.M.; Abdel-Rahman, M.A.; Rashed, L.A. Novel coumarin-6-sulfonamides as apoptotic anti-proliferative agents: synthesis, in vitro biological evaluation, and QSAR studies. J. Enzyme Inhib. Med. Chem., 2018, 33(1), 1095-1107.
[http://dx.doi.org/10.1080/14756366.2018.1477137] [PMID: 29944015]
[13]
Tugcu, G.; Aydın, A. In:Proceedings of the 1st International Conference on Applied Mathematics, Modeling and Life Sciences; ICAMLS’18 , Istanbul, Turkey,. , 2018, p. 79.
[14]
Nasr, T.; Bondock, S.; Rashed, H.M.; Fayad, W.; Youns, M.; Sakr, T.M. Novel hydrazide-hydrazone and amide substituted coumarin derivatives: Synthesis, cytotoxicity screening, microarray, radiolabeling and in vivo pharmacokinetic studies. Eur. J. Med. Chem., 2018, 151, 723-739.
[http://dx.doi.org/10.1016/j.ejmech.2018.04.014] [PMID: 29665526]
[15]
Nasr, T.; Bondock, S.; Youns, M. Anticancer activity of new coumarin substituted hydrazide-hydrazone derivatives. Eur. J. Med. Chem., 2014, 76, 539-548.
[http://dx.doi.org/10.1016/j.ejmech.2014.02.026] [PMID: 24607878]
[16]
Tetko, I.V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V.A.; Radchenko, E.V.; Zefirov, N.S.; Makarenko, A.S.; Tanchuk, V.Y.; Prokopenko, V.V. Virtual computational chemistry laboratory--design and description. J. Comput. Aided Mol. Des., 2005, 19(6), 453-463.
[http://dx.doi.org/10.1007/s10822-005-8694-y] [PMID: 16231203]
[17]
T. E. S. T., (v.4.2.1); Martin., T.; Harten, P. Venkatapathy, R.; Young, D. U.S. EPA/National Risk Management Research Laboratory/ Sustainable Technology Division Cincinnati OH,, 2016.
[18]
Hong, H.; Xie, Q.; Ge, W.; Qian, F.; Fang, H.; Shi, L.; Su, Z.; Perkins, R.; Tong, W. Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J. Chem. Inf. Model., 2008, 48(7), 1337-1344.
[http://dx.doi.org/10.1021/ci800038f] [PMID: 18564836]
[19]
Yap, C.W. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 2011, 32(7), 1466-1474.
[http://dx.doi.org/10.1002/jcc.21707] [PMID: 21425294]
[20]
González, M.P.; Terán, C.; Saíz-Urra, L.; Teijeira, M. Variable selection methods in QSAR: An overview. Curr. Top. Med. Chem., 2008, 8(18), 1606-1627.
[http://dx.doi.org/10.2174/156802608786786552] [PMID: 19075770]
[21]
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. Comput. Chem., 2013, 34(24), 2121-2132.
[http://dx.doi.org/10.1002/jcc.23361]
[22]
Gramatica, P.; Cassani, S.; Chirico, N. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J. Comput. Chem., 2014, 35(13), 1036-1044.
[http://dx.doi.org/10.1002/jcc.23576] [PMID: 24599647]
[23]
Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics; Wiley-VCH Verlag GmbH & Co. KGaA,. , 2009, Vol. 41., .
[http://dx.doi.org/[DOI:10.1002/9783527628766]
[24]
Guidance document on the validation of (quantitative) structure-activity relationship [(Q) SAR] models, series on testing and assessment, Paris,. 2007, 69, 154.
[25]
Gramatica, P.; Sangion, A. A historical excursus on the statistical validation parameters for QSAR models: a clarification concerning metrics and terminology. J. Chem. Inf. Model., 2016, 56(6), 1127-1131.
[http://dx.doi.org/10.1021/acs.jcim.6b00088] [PMID: 27218604]
[26]
Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model., 2002, 20(4), 269-276.
[http://dx.doi.org/10.1016/S1093-3263(01)00123-1] [PMID: 11858635]
[27]
Gramatica, P. Principles of QSAR models validation: internal and external. WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim, 2007, 26(5), 694-701.
[http://dx.doi.org/[https://doi.org/10.1002/qsar.200610151]
[28]
Sabet, R.; Mohammadpour, M.; Sadeghi, A.; Fassihi, A. QSAR study of isatin analogues as in vitro anti-cancer agents. Eur. J. Med. Chem., 2010, 45(3), 1113-1118.
[http://dx.doi.org/10.1016/j.ejmech.2009.12.010] [PMID: 20056518]
[29]
Lang, K.L.; Silva, I.T.; Machado, V.R.; Zimmermann, L.A.; Caro, M.S.; Simões, C.M.; Schenkel, E.P.; Durán, F.J.; Bernardes, L.S.; de Melo, E.B. Multivariate SAR and QSAR of cucurbitacin derivatives as cytotoxic compounds in a human lung adenocarcinoma cell line. J. Mol. Graph. Model., 2014, 48, 70-79.
[http://dx.doi.org/10.1016/j.jmgm.2013.12.004] [PMID: 24378396]
[30]
Allred, A.L.; Rochow, E.G. A scale of electronegativity based on electrostatic force. J. Inorg. Nucl. Chem., 1958, 5(4), 264-268.
[http://dx.doi.org/10.1016/0022-1902(58)80003-2]
[31]
Sheikhpour, R.; Sarram, M.A.; Gharaghani, S. Constraint score for semi-supervised feature selection in ligand-and receptor-based QSAR on serine/threonine-protein kinase PLK3 inhibitors. Chemom. Intell. Lab. Syst., 2017, 163, 31-40.
[http://dx.doi.org/10.1016/j.chemolab.2017.02.006]


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Article Details

VOLUME: 19
ISSUE: 13
Year: 2019
Page: [1121 - 1128]
Pages: 8
DOI: 10.2174/1568026619666190618143552
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