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Current Computer-Aided Drug Design


ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Research Article

Towards further Understanding the Structural Requirements of Combretastatin- like Chalcones as Inhibitors of Microtubule Polymerization

Author(s): Naveen Dhingra*, Anand Kar and Rajesh Sharma

Volume 16, Issue 2, 2020

Page: [155 - 166] Pages: 12

DOI: 10.2174/1573409915666181221114107

Price: $65


Background: Microtubules are dynamic filamentous cytoskeletal structures which play several key roles in cell proliferation and trafficking. They are supposed to contribute in the development of important therapeutic targeting tumor cells. Chalcones are important group of natural compounds abundantly found in fruits & vegetables that are known to possess anticancer activity. We have used QSAR and docking studies to understand the structural requirement of chalcones for understanding the mechanism of microtubule polymerization inhibition.

Methods: Three dimensional (3D) QSAR (CoMFA and CoMSIA), pharmacophore mapping and molecular docking studies were performed for the generation of structure activity relationship of combretastatin-like chalcones through statistical models and contour maps.

Results: Structure activity relationship revealed that substitution of electrostatic, steric and donor groups may enhance the biological activity of compounds as inhibitors of microtubule polymerization. From the docking study, it was clear that compounds bind at the active site of tubulin protein.

Conclusion: The given strategies of modelling could be an encouraging way for designing more potent compounds as well as for the elucidation of protein-ligand interaction.

Keywords: Chalcones, 3D QSAR, docking, inhibitors, microtubule polymerization, structure activity relationship.

Graphical Abstract
Orlikova, B.; Tasdemir, D.; Golais, F.; Dicato, M.; Diederich, M. Dietary chalcones with chemopreventive and chemotherapeutic potential. Genes Nutr., 2011, 6(2), 125-147.
[] [PMID: 21484163 ]
de Freitas, SM; Pruccoli, L; Morroni, F; Sita, G; Seghetti, F; Viegas, C Tarozzi, A The Keap1/Nrf2-ARE Pathway as a Pharmacological Target for Chalcones Molecules, 2018, 23, 1-22.
Mirossay, L.; Varinská, L.; Mojžiš, J. Antiangiogenic Effect of Flavonoids and Chalcones: An Update. Int. J. Mol. Sci., 2017, 19(1), 1-28.
[] [PMID: 29271940 ]
Patil, C.B.; Mahajan, S.K.; Katti, S.A. Chalcone-A versatile molecule. J Pharm Sci Res, 2009, 1, 11-22.
National Center for Biotechnology Information. PubChem Compound Database; CID=637760. (accessed July 18, 2017)
Singh, H.; Sidhu, S.; Khan, M. Free radical scavenging property of β-aescin and trans-chalcone: in vitro study. Eur. J. Pharm. Med. Res., 2016, 3, 309-312.
Leelananda, S.P.; Lindert, S. Computational methods in drug discovery. Beilstein J. Org. Chem., 2016, 12, 2694-2718.
[] [PMID: 28144341 ]
Cross, S.; Cruciani, G. Molecular fields in drug discovery: getting old or reaching maturity? Drug Discov. Today, 2010, 15(1-2), 23-32.
[] [PMID: 19150413 ]
Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem., 1994, 37(24), 4130-4146.
[] [PMID: 7990113 ]
Ducki, S.; Rennison, D.; Woo, M.; Kendall, A.; Chabert, J.F.; McGown, A.T.; Lawrence, N.J. Combretastatin-like chalcones as inhibitors of microtubule polymerization. Part 1: synthesis and biological evaluation of antivascular activity. Bioorg. Med. Chem., 2009, 17(22), 7698-7710.
[] [PMID: 19837593 ]
Tripos Associates, SYBYL X Molecular Modeling Software, Version 1.2, St. Louis, 582 MO, 2012.
Agrafiotis, D.K.; Gibbs, A.C.; Zhu, F.; Izrailev, S.; Martin, E. Conformational sampling of bioactive molecules: a comparative study. J. Chem. Inf. Model., 2007, 47(3), 1067-1086.
[] [PMID: 17411028 ]
Wei, Y.; Peng, W.; Wang, D.; Hao, S.H.; Li, W.W.; Ding, F. Design, synthesis, antifungal activity, and 3D-QSAR of coumarin derivatives. J. Pestic. Sci., 2018, 43(2), 88-95.
[] [PMID: 30363100 ]
Wang, M.; Wang, Y.; Kong, D.; Jiang, H.; Wang, J.; Cheng, M. In silico exploration of aryl sulfonamide analogs as voltage-gated sodium channel 1.7 inhibitors by using 3D-QSAR, molecular docking study, and molecular dynamics simulations. Comput. Biol. Chem., 2018, 77, 214-225.
[] [PMID: 30359866 ]
Alexander, D.L.; Tropsha, A.; Winkler, D.A. Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J. Chem. Inf. Model., 2015, 55(7), 1316-1322.
[] [PMID: 26099013 ]
Ravichandran, V.; Harish, R.; Abhishek, J.; Shalini, S.; Christapher, P.V.; Ram, K.A. Validation of QSAR Models Strategies and Importance. Int J Drug Des Dis, 2011, 2, 511-519.
Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model., 2002, 20(4), 269-276.
[] [PMID: 11858635 ]
Roy, K.; Kar, S.; Das, R.N. Statistical Methods in QSAR/QSPR. In: A Primer on QSAR/QSPR Modeling. Springer Briefs in Molecular Science, Springer, Cham.Ed. 2015, pp. 37-59.
Zhang, X.; Qiao, L.; Chen, Y.; Zhao, B.; Gu, Y.; Huo, X.; Zhang, Y.; Li, G. In Silico Analysis of the Association Relationship between Neuroprotection and Flavors of Traditional Chinese Medicine Based on the mGluRs. Int. J. Mol. Sci., 2018, 19(1), 1-16.
[] [PMID: 29320397 ]
Hevener, K.E.; Zhao, W.; Ball, D.M.; Babaoglu, K.; Qi, J.; White, S.W.; Lee, R.E. Validation of molecular docking programs for virtual screening against dihydropteroate synthase. J. Chem. Inf. Model., 2009, 49(2), 444-460.
[] [PMID: 19434845 ]
Xie, H.; Li, Y.; Yu, F.; Xie, X.; Qiu, K.; Fu, J. An Investigation of Molecular Docking and Molecular Dynamic Simulation on Imidazopyridines as B-Raf Kinase Inhibitors. Int. J. Mol. Sci., 2015, 16(11), 27350-27361.
[] [PMID: 26580609 ]
Song, Y.Y.; Lu, Y. Decision tree methods: applications for classification and prediction. Shanghai Jingshen Yixue, 2015, 27(2), 130-135.
[PMID: 26120265 ]

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