Login Register Cart 0
Generic placeholder image

Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Rational Design of Colchicine Derivatives as anti-HIV Agents via QSAR and Molecular Docking

Author(s): Apilak Worachartcheewan*, Napat Songtawee, Suphakit Siriwong, Supaluk Prachayasittikul*, Chanin Nantasenamat and Virapong Prachayasittikul

Volume 15, Issue 4, 2019

Page: [328 - 340] Pages: 13

DOI: 10.2174/1573406414666180924163756

Price: $65

Abstract

Background: Human immunodeficiency virus (HIV) is an infective agent that causes an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for preventing the progression of the disease is required.

Objective: This study aims to construct quantitative structure-activity relationship (QSAR) models, molecular docking and newly rational design of colchicine and derivatives with anti-HIV activity.

Methods: A data set of 24 colchicine and derivatives with anti-HIV activity were employed to develop the QSAR models using machine learning methods (e.g. multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular docking.

Results: The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed MLR and SVM methods that displayed LOO−CV 2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV set, and Ext 2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design of colchicine derivatives using informative QSAR and molecular docking was proposed.

Conclusion: These findings serve as a guideline for the rational drug design as well as potential development of novel anti-HIV agents.

Keywords: Colchicines, anti-HIV agent, AIDS, rational design, QSAR, molecular docking.

« Previous Next »
Graphical Abstract

[1]
Tavassoli, A. Targeting the protein-protein interactions of the HIV life cycle. Chem. Soc. Rev., 2011, 40, 1337-1346.
[2]
Zhan, P.; Pannecouque, C.; De Clercq, E.; Liu, X. Anti-HIV drug discovery and development: Current innovations and future trends. J. Med. Chem., 2016, 59, 2849-2878.
[3]
Moore, J.P.; Stevenson, M. New targets for inhibitors of HIV-1 replication. Nat. Rev. Mol. Cell Biol., 2000, 1, 40-49.
[4]
Teixeira, C.; Gomes, J.R.; Gomes, P.; Maurel, F.; Barbault, F. Viral surface glycoproteins, gp120 and gp41, as potential drug targets against HIV-1: Brief overview one quarter of a century past the approval of zidovudine, the first anti-retroviral drug. Eur. J. Med. Chem., 2011, 46, 979-992.
[5]
Vermeire, K.; Schols, D. Anti-HIV agents targeting the interaction of gp120 with the cellular CD4 receptor. Expert Opin. Investig. Drugs, 2005, 14, 1199-1212.
[6]
Deftereos, S.; Giannopoulos, G.; Panagopoulou, V.; Bouras, G.; Raisakis, K.; Kossyvakis, C.; Karageorgiou, S.; Papadimitriou, C.; Vastaki, M.; Kaoukis, A.; Angelidis, C.; Pagoni, S.; Pyrgakis, V.; Alexopoulos, D.; Manolis, A.S.; Stefanadis, C.; Cleman, M.W. Anti-inflammatory treatment with colchicine in stable chronic heart failure: a prospective, randomized study. JACC Heart Fail., 2014, 2, 131-137.
[7]
Gasparyan, A.Y.; Ayvazyan, L.; Yessirkepov, M.; Kitas, G.D. Colchicine as an anti-inflammatory and cardioprotective agent. Expert Opin. Drug Metab. Toxicol., 2015, 11, 1781-1794.
[8]
Lin, Z.Y.; Wu, C.C.; Chuang, Y.H.; Chuang, W.L. Anti-cancer mechanisms of clinically acceptable colchicine concentrations on hepatocellular carcinoma. Life Sci., 2013, 93, 323-328.
[9]
Singh, B.; Kumar, A.; Joshi, P.; Guru, S.K.; Kumar, S.; Wani, Z.A.; Mahajan, G.; Hussain, A.; Qazi, A.K.; Kumar, A.; Bharate, S.S.; Gupta, B.D.; Sharma, P.R.; Hamid, A.; Saxena, A.K.; Mondhe, D.M.; Bhushan, S.; Bharate, S.B.; Vishwakarma, R.A. Colchicine derivatives with potent anticancer activity and reduced P-glycoprotein induction liability. Org. Biomol. Chem., 2015, 13, 5674-5689.
[10]
Huczyński, A.; Rutkowski, J.; Popiel, K.; Maj, E.; Wietrzyk, J.; Stefańska, J.; Majcher, U.; Bartl, F. Synthesis, antiproliferative and antibacterial evaluation of C-ring modified colchicine analogues. Eur. J. Med. Chem., 2015, 90, 296-301.
[11]
Schlesinger, N. Reassessing the safety of intravenous and compounded injectable colchicine in acute gout treatment. Expert Opin. Drug Saf., 2007, 6, 625-629.
[12]
Cifuentes, M.; Schilling, B.; Ravindra, R.; Winter, J.; Janik, M.E. Synthesis and biological evaluation of B-ring modified colchicine and isocolchicine analogs. Bioorg. Med. Chem. Lett., 2006, 16, 2761-2764.
[13]
Shen, L.H.; Li, H.Y.; Shang, H.X.; Tian, S.T.; Lai, Y.S.; Liu, L.J. Synthesis and cytotoxic evaluation of new colchicine derivatives bearing 1,3,4-thiadiazole moieties. Chin. J. Chem., 2013, 24, 299-302.
[14]
Thomopoulou, P.; Sachs, J.; Teusch, N.; Mariappan, A.; Gopalakrishnan, J.; Schmalz, H.G. New colchicine-derived triazoles and their influence on cytotoxicity and microtubule morphology. ACS Med. Chem. Lett., 2015, 7, 188-191.
[15]
Zhang, X.; Kong, Y.; Zhang, J.; Su, M.; Zhou, Y.; Zang, Y.; Li, J.; Chen, Y.; Fang, Y.; Zhang, X.; Lu, W. Design, synthesis and biological evaluation of colchicine derivatives as novel tubulin and histone deacetylase dual inhibitors. Eur. J. Med. Chem., 2015, 95, 127-135.
[16]
Tatematsu, H.; Kilkuskie, R.E.; Corrigan, A.J.; Bodner, A.J.; Lee, K.H. Anti-AIDS agents, 3. inhibitory effects of colchicine derivatives on HIV replication in H9 lymphocyte cells. J. Nat. Prod., 1991, 54, 632-637.
[17]
Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Advances in computational methods to predict the biological activity of compounds. Exp Opin. Drug Discov., 2010, 5, 633-654.
[18]
Nantasenamat, C.; Prachayasittikul, V. Maximizing computational tools for successful drug discovery. Expert Opin. Drug Discov., 2015, 10, 321-329.
[19]
Prachayasittikul, V.; Worachartcheewan, A.; Shoombuatong, W.; Songtawee, N.; Simeon, S.; Prachayasittikul, V.; Nantasenamat, C. Computer-aided drug design of bioactive natural products. Curr. Top. Med. Chem., 2015, 15, 1780-1800.
[20]
Dennington, II, R.; Keith, T.; Millam, J.; Eppinnett, K.; Hovell, W.L.; Gilliland, R. GaussView, Version 3.09; Semichem: Shawnee Mission, KS, USA, 2003.
[21]
Frisch, M.J.; Trucks, G.W.; Schlegel, H.B.; Scuseria, G.E.; Robb, M.A.; Cheeseman, J.R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G.A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H.P.; Izmaylov, A.F.; Bloino, J.; Zheng, G.; Sonnenberg, J.L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J.A., Jr; Peralta, J.E.; Ogliaro, F.; Bearpark, M.; Heyd, J.J.; Brothers, E.; Kudin, K.N.; Staroverov, V.N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J.C.; Iyengar, S.S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, N.J.; Klene, M.; Knox, J.E.; Cross, J.B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R.E.; Yazyev, O.; Austin, A.J.; Cammi, R.; Pomelli, C.; Ochterski, J.W.; Martin, R.L.; Morokuma, K.; Zakrzewski, V.G.; Voth, G.A.; Salvador, P.; Dannenberg, J.J.; Dapprich, S.; Daniels, A.D.; Farkas, O.; Foresman, J.B.; Ortiz, J.V.; Cioslowski, J.; Fox, D.J. Gaussian 09, Revision A.1, Connecticut, Wallingford, , 2009.
[22]
Karelson, M.; Lobanov, V.S.; Katritzky, A.R. Quantum-chemical descriptors in QSAR/QSPR studies. Chem. Rev., 1996, 96, 1027-1044.
[23]
Parr, R.G.; Donnelly, R.A.; Levy, M.; Palke, W.E. Electronegativity: The density functional viewpoint. J. Chem. Phys., 1978, 68, 3801-3807.
[24]
Parr, R.G.; Pearson, R.G. Absolute hardness: Companion parameter to absolute electronegativity. J. Am. Chem. Soc., 1983, 105, 7512-7516.
[25]
Parr, R.G.; Szentpály, L.; Liu, S. Electrophilicity index. J. Am. Chem. Soc., 1999, 121, 1922-1924.
[26]
Thanikaivelan, P.; Subramanian, V.; Raghava Rao, J.; Unni Nair, B. Application of quantum chemical descriptor in quantitative structure activity and structure property relationship. Chem. Phys. Lett., 2000, 323, 59-70.
[27]
Talete srl. DRAGON for windows (Software for molecular descriptor calculations), Version 5.5, Milano, Italy 2007.
[28]
Witten, I.H.; Frank, E.; Hall, M.A. Data mining: Practical machine learning tools and techniques; Morgan Kaufmann: San Francisco, USA, 2011.
[29]
Worachartcheewan, A.; Nantasenamat, C.; Isarankura-Na-Ayudha, C.; Prachayasittikul, V. QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum. Chem. Pap., 2013, 67, 1462-1473.
[30]
Pingaew, R.; Prachayasittikul, V.; Worachartcheewan, A.; Nantasenamat, C.; Prachayasittikul, S.; Ruchirawat, S.; Prachayasittikul, V. Novel 1,4-naphthoquinone-based sulfonamides: synthesis, QSAR, anticancer and antimalarial studies. Eur. J. Med. Chem., 2015, 103, 446-459.
[31]
Nantasenamat, C.; Naenna, T.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. J. Comput. Aided Mol. Des., 2005, 19, 509-524.
[32]
Su, Q.; Xu, X.; Zhou, L. QSAR model of triterpene derivatives as potent anti-HIV agents. Mol. Simul., 2008, 34, 651-659.
[33]
Cortes, C.; Vapnik, V. Support-vector network. Mach. Learn., 1995, 20, 273-297.
[34]
Vapnik, V. Statistical learning theory; Wiley: New York, USA, 1998.
[35]
Nantasenamat, C.; Worachartcheewan, A.; Jamsak, S.; Preeyanon, L.; Shoombuatong, W.; Simeon, S.; Mandi, P.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. AutoWeka: Toward an automated data mining software for QSAR and QSPR studies. Methods Mol. Biol., 2015, 1260, 119-147.
[36]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30, 2785-2791.
[37]
Lan, P.; Chen, W.N.; Huang, Z.J.; Sun, P.H.; Chen, W.M. Understanding the structure-activity relationship of betulinic acid derivatives as anti-HIV-1 agents by using 3D-QSAR and docking. J. Mol. Model., 2011, 17, 1643-1659.
[38]
Chen, Y.F.; Hsu, K.C.; Lin, S.R.; Wang, W.C.; Huang, Y.C.; Yang, J.M. SiMMap: a web server for inferring site-moiety map to recognize interaction preferences between protein pockets and compound moieties. Nucleic Acids Res., 2010, 38, W424-430.
[39]
Dassault Systèmes, B.I.O.V.I.A. Discovery Studio Modeling Environment, Release 2017; San Diego: Dassault Systèmes, 2016.
[40]
Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Tansila, N.; Naenna, T.; Prachayasittikul, V. Prediction of GFP spectral properties using artificial neural network. J. Comput. Chem., 2007, 28, 1275-1289.
[41]
Prachayasittikul, V.; Pingaew, R.; Anuwongcharoen, N.; Worachartcheewan, A.; Nantasenamat, C.; Prachayasittikul, S.; Ruchirawat, S.; Prachayasittikul, V. Discovery of novel 1,2,3-triazole derivatives as anticancer agents using QSAR and in silico structural modification. Springerplus, 2015, 4, 571.
[42]
Srungboonmee, K.; Songtawee, N.; Monnor, T.; Prachayasittikul, V.; Nantasenamat, C. Probing the origins of 17β-hydroxysteroid dehydrogenase type 1 inhibitory activity via QSAR and molecular docking. Eur. J. Med. Chem., 2015, 96, 231-237.
[43]
Worachartcheewan, A.; Suvannang, N.; Prachayasittikul, S.; Prachayasittikul, V.; Nantasenamat, C. Probing the origins of aromatase inhibitory activity of disubstituted coumarins via QSAR and molecular docking. EXCLI J., 2014, 13, 1259-1274.
[44]
Worachartcheewan, A.; Nantasenamat, C.; Owasirikul, W.; Monnor, T.; Naruepantawart, O.; Janyapaisarn, S.; Prachayasittikul, S.; Prachayasittikul, V. Insights into antioxidant activity of 1-adamantylthiopyridine analogs using multiple linear regression. Eur. J. Med. Chem., 2014, 73, 258-264.
[45]
Shoombuatong, W.; Prachayasittikul, V.; Anuwongcharoen, N.; Songtawee, N.; Monnor, T.; Prachayasittikul, S.; Prachayasittikul, V.; Nantasenamat, C. Navigating the chemical space of dipeptidyl peptidase-4 inhibitors. Drug Des. Devel. Ther., 2015, 9, 4515-4549.
[46]
Worachartcheewan, A.; Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Predicting antimicrobial activities of benzimidazole derivatives. Med. Chem. Res., 2013, 22, 5418-5430.
[47]
Mandi, P.; Shoombuatong, W.; Phanus-umporn, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V.; Bülow, L.; Nantasenamat, C. Exploring the origins of structure–oxygen affinity relationship of human haemoglobin allosteric effector. Mol. Simul., 2015, 41, 1283-129.
[48]
Worachartcheewan, A.; Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Probing the origins of anticancer activity of chrysin derivatives. Med. Chem. Res., 2015, 24, 1884-1892.
[49]
Lapins, M.; Worachartcheewan, A.; Spjuth, O.; Georgiev, V.; Prachayasittikul, V.; Nantasenamat, C.; Wikberg, J.E. A unified proteochemometric model for prediction of inhibition of cytochrome P450 isoforms. PLoS One, 2013, 8, e66566.
[50]
Saghaie, L.; Sakhi, H.; Sabzyan, H.; Shahlaei, M.; Shamshirian, D. Stepwise MLR and PCR QSAR study of the pharmaceutical activities of antimalarial 3-hydroxypyridinone agents using B3LYP/6-311++G** descriptors. Med. Chem. Res., 2013, 22, 1679-1688.
[51]
Bucinski, A.; Markuszewski, M.J.; Wiktorowicz, W.; Krysinski, J.; Kaliszan, R. Artificial neural networks for prediction of antibacterial activity in series of imidazole derivatives. Comb. Chem. High Throughput Screen., 2004, 7, 327-336.
[52]
Verma, R.P.; Hansch, C. QSAR modeling of taxane analogues against colon cancer. Eur. J. Med. Chem., 2010, 45, 1470-1477.
[53]
Worachartcheewan, A.; Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, S.; Prachayasittikul, V. Predicting the free radical scavenging activity of curcumin derivatives. Chemometr. Intell. Lab. Syst., 2011, 109, 207-216.
[54]
Sawant, R.L.; Bansode, C.A.; Wadekar, J.B. (2013). In vitro anti-inflammatory potential and QSAR analysis of oxazolo/thiazolo pyrimidine derivatives. Med. Chem. Res., 2013, 22, 1884-1892.
[55]
Kwon, Y.D.; LaLonde, J.M.; Yang, Y.; Elban, M.A.; Sugawara, A.; Courter, J.R.; Jones, D.M.; Smith, A.B.; Debnath, A.K.; Kwong, P.D. Crystal structures of HIV-1 gp120 envelope glycoprotein in complex with NBD analogues that target the CD4-binding site. PLoS One, 2014, 9, e85940.
[56]
Madani, N.; Schon, A.; Princiotto, A.M.; Lalonde, J.M.; Courter, J.R.; Soeta, T.; Ng, D.; Wang, L.; Brower, E.T.; Xiang, S.H.; Kwon, Y.D.; Huang, C.C.; Wyatt, R.; Kwong, P.D.; Freire, E.; Smith, III, A.B.; Sodroski, J. Small-molecule CD4 mimics interact with a highly conserved pocket on HIV-1 gp120. Structure, 2008, 16, 1689-1701.

Rights & Permissions Print Cite
Article Metrics
74
3
1
1
© 2024 Bentham Science Publishers | Privacy Policy