Login Register Cart 0
Generic placeholder image

Current Computer-Aided Drug Design

Editor-in-Chief

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

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

3D-QSAR and Molecular Docking Studies on Design Anti-Prostate Cancer Curcumin Analogues

Author(s): Xi Meng, Lianhua Cui, Fucheng Song, Mingyuan Luan, Junjie Ji, Hongzong Si*, Yunbo Duan and Honglin Zhai

Volume 16, Issue 3, 2020

Page: [245 - 256] Pages: 12

DOI: 10.2174/1573409914666181029123746

Price: $65

Abstract

Background: Prostate cancer is one of the most common tumors in the world and the fifth leading cause of male cancer death. Although the treatment of localized androgen-dependent prostate cancer has been successful, the efficacy of androgen-independent metastatic disease is limited. Curcumin, a natural product, has been found to inhibit the proliferation of prostate cancer cells.

Objective: To design curcumin analogs with higher biological activity and lower toxicity and side effects for the treatment of prostate cancer.

Methods: In this study, the three dimensional-quantitative structure activity relationship (3DQSAR) and molecular docking studies were performed on 34 curcumin analogs as anti-prostate cancer compounds. We introduced OSIRIS Property Explorer to predict drug-related properties of newly designed compounds.

Results: The optimum CoMSIA model exhibited statistically significant results: the cross-validated correlation coefficient q2 is 0.540 and non-cross-validated R2 value is 0.984. The external predictive correlation coefficient Rext 2 is 0.792. The information of structure-activity relationship can be obtained from the CoMSIA contour maps. In addition, the molecular docking study of the compounds for 3ZK6 as the protein target revealed important interactions between active compounds and amino acids.

Conclusion: Compound 28i may be a new type of anti-prostate cancer drug with higher biological activity and more promising development.

Keywords: QSAR, CoMSIA, molecular docking, curcumin analogs, drug design, prostate cancer.

« Previous Next »
Graphical Abstract

[1]
Attard, G.; Reid, A.H.; Olmos, D.; de Bono, J.S. Antitumor activity with CYP17 blockade indicates that castration-resistant prostate cancer frequently remains hormone driven. Cancer Res., 2009, 69(12), 4937-4940.
[http://dx.doi.org/10.1158/0008-5472.CAN-08-4531] [PMID: 19509232]
[2]
Freddie, B.; Jacques, F.; Isabelle, S.; Rebecca, L. Siegel, MPH4.; Lindsey, A.; Ahmedin, J. Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin., 2018, 1-31.
[3]
Torre, L.A.; Bray, F.; Siegel, R.L.; Ferlay, J.; Lortet-Tieulent, J.; Jemal, A. Global cancer statistics, 2012. CA Cancer J. Clin., 2015, 65(2), 87-108.
[http://dx.doi.org/10.3322/caac.21262] [PMID: 25651787]
[4]
Nelson, J.B.; Lepor, H. Prostate cancer: radical prostatectomy. Urol. Clin. North Am., 2003, 30(4), 703-723, viii.,
[http://dx.doi.org/10.1016/S0094-0143(03)00049-1] [PMID: 14680309]
[5]
Michael, A.; Syrigos, K.; Pandha, H. Prostate cancer chemotherapy in the era of targeted therapy. Prostate Cancer Prostatic Dis., 2009, 12(1), 13-16.
[http://dx.doi.org/10.1038/pcan.2008.32] [PMID: 18521103]
[6]
Zaorsky, N.G.; Harrison, A.S.; Trabulsi, E.J.; Gomella, L.G.; Showalter, T.N.; Hurwitz, M.D.; Dicker, A.P.; Den, R.B. Evolution of advanced technologies in prostate cancer radiotherapy. Nat. Rev. Urol., 2013, 10(10), 565-579.
[http://dx.doi.org/10.1038/nrurol.2013.185] [PMID: 24018567]
[7]
Debruyne, F. Hormonal therapy of prostate cancer. Semin. Urol. Oncol., 2002, 20(3)(Suppl. 1), 4-9.
[http://dx.doi.org/10.1053/suro.2002.35051] [PMID: 12198632]
[8]
Teiten, M.H.; Gaascht, F.; Eifes, S.; Dicato, M.; Diederich, M. Chemopreventive potential of curcumin in prostate cancer. Genes Nutr., 2010, 5(1), 61-74.
[http://dx.doi.org/10.1007/s12263-009-0152-3] [PMID: 19806380]
[9]
Kallifatidis, G.; Hoy, J.J.; Lokeshwar, B.L. Bioactive natural products for chemoprevention and treatment of castration-resistant prostate cancer. Semin. Cancer Biol., 2016, 40-41, 160-169.
[http://dx.doi.org/10.1016/j.semcancer.2016.06.003] [PMID: 27370570]
[10]
Mann, J. Natural products in cancer chemotherapy: past, present and future. Nat. Rev. Cancer, 2002, 2(2), 143-148.
[http://dx.doi.org/10.1038/nrc723] [PMID: 12635177]
[11]
Cragg, G.M.; Grothaus, P.G.; Newman, D.J. ChemInform Abstract: Impact of Natural Products on Developing New Anticancer Agents. ChemInform, 2009, 40(47), 3012-3043.
[http://dx.doi.org/10.1002/chin.200947263]
[12]
Dorai, T.; Gehani, N.; Katz, A. Therapeutic potential of curcumin in human prostate cancer-I. curcumin induces apoptosis in both androgen-dependent and androgen-independent prostate cancer cells. Prostate Cancer Prostatic Dis., 2000, 3(2), 84-93.
[http://dx.doi.org/10.1038/sj.pcan.4500399] [PMID: 12497104]
[13]
Killian, P.H.; Kronski, E.; Michalik, K.M.; Barbieri, O.; Astigiano, S.; Sommerhoff, C.P.; Pfeffer, U.; Nerlich, A.G.; Bachmeier, B.E. Curcumin inhibits prostate cancer metastasis in vivo by targeting the inflammatory cytokines CXCL1 and -2. Carcinogenesis, 2012, 33(12), 2507-2519.
[http://dx.doi.org/10.1093/carcin/bgs312] [PMID: 23042094]
[14]
Rivera, M.; Ramos, Y.; Rodríguez-Valentín, M.; López-Acevedo, S.; Cubano, L.A.; Zou, J.; Zhang, Q.; Wang, G.; Boukli, N.M. Targeting multiple pro-apoptotic signaling pathways with curcumin in prostate cancer cells. PLoS One, 2017, 12(6)e0179587
[http://dx.doi.org/10.1371/journal.pone.0179587] [PMID: 28628644]
[15]
Burgos-Morón, E.; Calderón-Montaño, J.M.; Salvador, J.; Robles, A.; López-Lázaro, M. The dark side of curcumin. Int. J. Cancer, 2010, 126(7), 1771-1775.
[PMID: 19830693]
[16]
Ji, J.L.; Huang, X.F.; Zhu, H.L. Curcumin and its formulations: potential anti-cancer agents. Anticancer. Agents Med. Chem., 2012, 12(3), 210-218.
[http://dx.doi.org/10.2174/187152012800228733] [PMID: 22044005]
[17]
Bansal, S.S.; Goel, M.; Aqil, F.; Vadhanam, M.V.; Gupta, R.C. Advanced drug delivery systems of curcumin for cancer chemoprevention. Cancer Prev. Res. (Phila.), 2011, 4(8), 1158-1171.
[http://dx.doi.org/10.1158/1940-6207.CAPR-10-0006] [PMID: 21546540]
[18]
Yallapu, M.M.; Othman, S.F.; Curtis, E.T.; Bauer, N.A.; Chauhan, N.; Kumar, D.; Jaggi, M.; Chauhan, S.C. Curcumin-loaded magnetic nanoparticles for breast cancer therapeutics and imaging applications. Int. J. Nanomedicine, 2012, 7(1), 1761-1779.
[PMID: 22619526]
[19]
Doello, K.; Ortiz, R.; Alvarez, P.J.; Melguizo, C.; Cabeza, L.; Prados, J. Latest in vitro and in vivo assay, clinical trials and patents in cancer treatment using curcumin: a literature review. Nutr. Cancer, 2018, 70(4), 569-578.
[http://dx.doi.org/10.1080/01635581.2018.1464347] [PMID: 29708445]
[20]
Pastorelli, D.; Fabricio, A.S.C.; Giovanis, P.; D’Ippolito, S.; Fiduccia, P.; Soldà, C.; Buda, A.; Sperti, C.; Bardini, R.; Da Dalt, G.; Rainato, G.; Gion, M.; Ursini, F. Phytosome complex of curcumin as complementary therapy of advanced pancreatic cancer improves safety and efficacy of gemcitabine: Results of a prospective phase II trial. Pharmacol. Res., 2018, 132, 72-79.
[http://dx.doi.org/10.1016/j.phrs.2018.03.013] [PMID: 29614381]
[21]
Huang, H.; Chen, X.; Li, D.; He, Y.; Li, Y.; Du, Z.; Zhang, K.; DiPaola, R.; Goodin, S.; Zheng, X. Combination of α-Tomatine and Curcumin Inhibits Growth and Induces Apoptosis in Human Prostate Cancer Cells. PLoS One, 2015, 10(12)e0144293
[http://dx.doi.org/10.1371/journal.pone.0144293] [PMID: 26630272]
[22]
Mukhopadhyay, A.; Bueso-Ramos, C.; Chatterjee, D.; Pantazis, P.; Aggarwal, B.B. Curcumin downregulates cell survival mechanisms in human prostate cancer cell lines. Oncogene, 2001, 20(52), 7597-7609.
[http://dx.doi.org/10.1038/sj.onc.1204997] [PMID: 11753638]
[23]
Shankar, S.; Srivastava, R.K. Involvement of Bcl-2 family members, phosphatidylinositol 3′-kinase/AKT and mitochondrial p53 in curcumin (diferulolylmethane)-induced apoptosis in prostate cancer. Int. J. Oncol., 2007, 30(4), 905-918.
[http://dx.doi.org/10.3892/ijo.30.4.905] [PMID: 17332930]
[24]
Duch, W.; Swaminathan, K.; Meller, J. Artificial intelligence approaches for rational drug design and discovery. Curr. Pharm. Des., 2007, 13(14), 1497-1508.
[http://dx.doi.org/10.2174/138161207780765954] [PMID: 17504169]
[25]
Zheng, M.; Liu, X.; Xu, Y.; Li, H.; Luo, C.; Jiang, H. Computational methods for drug design and discovery: focus on China. Trends Pharmacol. Sci., 2013, 34(10), 549-559.
[http://dx.doi.org/10.1016/j.tips.2013.08.004] [PMID: 24035675]
[26]
Lill, M.A. Multi-dimensional QSAR in drug discovery. Drug Discov. Today, 2007, 12(23-24), 1013-1017.
[http://dx.doi.org/10.1016/j.drudis.2007.08.004] [PMID: 18061879]
[27]
Zhang, X.; Guo, S.; Chen, C.; Perez, G.R.; Zhang, C.; Patanapongpibul, M.; Subrahmanyam, N.; Wang, R.; Keith, J.; Chen, G.; Dong, Y.; Zhang, Q.; Zhong, Q.; Zheng, S.; Wang, G.; Chen, Q.H. Asymmetric 1,5-diarylpenta-1,4-dien-3-ones: Antiproliferative activity in prostate epithelial cell models and pharmacokinetic studies. Eur. J. Med. Chem., 2017, 137, 263-279.
[http://dx.doi.org/10.1016/j.ejmech.2017.05.062] [PMID: 28601720]
[28]
Clark, M.; Iii, R.D.C.; Opdenbosch, N.V. Validation of the general purpose tripos 5.2 force field. J. Comput. Chem., 1989, 10(8), 982-1012.
[http://dx.doi.org/10.1002/jcc.540100804]
[29]
Gasteiger, J.; Marsili, M. Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron, 1980, 36(22), 3219-3228.
[http://dx.doi.org/10.1016/0040-4020(80)80168-2]
[30]
Xu, G.; Chu, Y.; Jiang, N.; Yang, J.; Li, F. The three dimensional Quantitative Structure Activity Relationships (3D-QSAR) and docking studies of curcumin derivatives as androgen receptor antagonists. Int. J. Mol. Sci., 2012, 13(5), 6138-6155.
[http://dx.doi.org/10.3390/ijms13056138] [PMID: 22754355]
[31]
Srivastava, V.; Gupta, S.P.; Siddiqi, M.I.; Mishra, B.N. 3D-QSAR studies on quinazoline antifolate thymidylate synthase inhibitors by CoMFA and CoMSIA models. Eur. J. Med. Chem., 2010, 45(4), 1560-1571.
[http://dx.doi.org/10.1016/j.ejmech.2009.12.065] [PMID: 20153089]
[32]
Ghose, A.K.; Pritchett, A.; Crippen, G.M. Atomic physicochemical parameters for three dimensional structure directed quantitative structure‐activity relationships III: Modeling hydrophobic interactions. J. Comput. Chem., 1988, 9(1), 80-90.
[http://dx.doi.org/10.1002/jcc.540090111]
[33]
Frank, I.; Feikema, J.; Constantine, N.; Kowalski, B. Prediction of Product Quality from Spectral Data Using the Partial Least-Squares Method. J. Chem. Inf. Model., 1984, 24(1), 20-24.
[http://dx.doi.org/10.1021/ci00041a602]
[34]
Saxena, A.K.; Prathipati, P. Comparison of MLR, PLS and GA-MLR in QSAR analysis. SAR QSAR Environ. Res., 2003, 14(5-6), 433-445.
[http://dx.doi.org/10.1080/10629360310001624015] [PMID: 14758986]
[35]
Yu, Z.; Li, X.; Ge, C.; Si, H.; Cui, L.; Gao, H.; Duan, Y.; Zhai, H. 3D-QSAR modeling and molecular docking study on Mer kinase inhibitors of pyridine-substituted pyrimidines. Mol. Divers., 2015, 19(1), 135-147.
[http://dx.doi.org/10.1007/s11030-014-9556-0] [PMID: 25355276]
[36]
Iii, R.D.C.; Bunce, J.D.; Patterson, D.E.; Frank, I.E. Crossvalidation, Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies. Mol. Inform., 1988, 7(1), 18-25.
[37]
Rücker, C.; Rücker, G.; Meringer, M. y-Randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model., 2007, 47(6), 2345-2357.
[http://dx.doi.org/10.1021/ci700157b] [PMID: 17880194]
[38]
Pratim Roy, P.; Paul, S.; Mitra, I.; Roy, K. On two novel parameters for validation of predictive QSAR models. Molecules, 2009, 14(5), 1660-1701.
[http://dx.doi.org/10.3390/molecules14051660] [PMID: 19471190]
[39]
Gramatica, P. On the development and validation of QSAR models. Humana Press, , 2013, 930, 499-526.
[http://dx.doi.org/10.1007/978-1-62703-059-5_21]
[40]
Shankar, S.; Chen, Q.; Sarva, K.; Siddiqui, I.; Srivastava, R.K. Curcumin enhances the apoptosis-inducing potential of TRAIL in prostate cancer cells: molecular mechanisms of apoptosis, migration and angiogenesis. J. Mol. Signal., 2007, 2(1), 10.
[http://dx.doi.org/10.1186/1750-2187-2-10] [PMID: 17916240]
[41]
Koska, J.; Spassov, V.Z.; Maynard, A.J.; Yan, L.; Austin, N.; Flook, P.K.; Venkatachalam, C.M. Fully automated molecular mechanics based induced fit protein-ligand docking method. J. Chem. Inf. Model., 2008, 48(10), 1965-1973.
[http://dx.doi.org/10.1021/ci800081s] [PMID: 18816046]
[42]
Jain, A.N. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem., 2003, 46(4), 499-511.
[http://dx.doi.org/10.1021/jm020406h] [PMID: 12570372]
[43]
Ghaleb, A.; Aouidate, A.; Ghamali, M.; Sbai, A.; Bouachrine, M.; Lakhlifi, T. 3D-QSAR Modeling and Molecular Docking Studies on a series of 2,5 disubstituted 1,3,4-oxadiazoles. J. Mol. Struct., 2017, 1145, 278-284.
[http://dx.doi.org/10.1016/j.molstruc.2017.05.065]

Rights & Permissions Print Cite
Article Metrics
29
1
© 2024 Bentham Science Publishers | Privacy Policy