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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

Studies on the pIC50 of 4,5-Diarylisoxazole as HSP90 Inhibitors

Author(s): Jing Ouyang, Xiaoqian Liu, Yutao Zhao, Ying Liu, Hongzong Si* and Honglin Zhai

Volume 17, Issue 4, 2020

Page: [467 - 478] Pages: 12

DOI: 10.2174/1570180816666190329221959

Price: $65

Abstract

Background: Heat Shock Protein 90(HSP90) inhibitors are involved in multiple anticancer pathways, which indicate many important novel molecular targets for cancer therapy. However, the characteristics of poor water solubility, liver toxicity and finite bioavailability of the present inhibitors limit clinical application. Hence, it is crucial to evaluate the characteristics of compounds and develop new drugs with hypotoxicity and high-bioactivity.

Methods: Quantitative Structure-Activity Relationship (QSAR) has been an effective method for screening novel structures and predicting various properties of the synthesized compounds. Heuristic Method (HM) and Gene Expression Programming (GEP) algorithm were used to establish linear and nonlinear models severally.

Results: The results showed that HM has good correlation coefficients of R2 and lower S2 as 0.79 and 0.29 for the training set and GEP has better values of 0.89 and 0.05, respectively.

Conclusion: Both models have the capability of prediction but the nonlinear model developed by GEP has a more excellent predictive ability and indicates further optimization of the HSP90 inhibitors.

Keywords: HSP90 inhibitors, pIC50, quantitative structure-activity relationship, model, gene expression programming, Heuristic Method.

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[1]
Diaz-Moralli, S.; Tarrado-Castellarnau, M.; Miranda, A.; Cascante, M. Targeting cell cycle regulation in cancer therapy. Pharmacol. Ther., 2013, 138(2), 255-271.
[http://dx.doi.org/10.1016/j.pharmthera.2013.01.011] [PMID: 23356980]
[2]
Li, J.; Buchner, J. Structure, function and regulation of the hsp90 machinery. Biomed. J., 2013, 36(3), 106-117.
[http://dx.doi.org/10.4103/2319-4170.113230] [PMID: 23806880]
[3]
Saibil, H. Chaperone machines for protein folding, unfolding and disaggregation. Nat. Rev. Mol. Cell Biol., 2013, 14(10), 630-642.
[http://dx.doi.org/10.1038/nrm3658] [PMID: 24026055]
[4]
Binbin, Z. Advances of HSP90 inhibitors treating acute myelogenous leukemia. Basic & Clin. Med., 2014, 34(2), 270-273.
[5]
Wu, J.; Liu, T.; Rios, Z.; Mei, Q.; Lin, X.; Cao, S. Heat shock proteins and cancer. Trends Pharmacol. Sci., 2017, 38(3), 226-256.
[http://dx.doi.org/10.1016/j.tips.2016.11.009] [PMID: 28012700]
[6]
Hong, D.S.; Banerji, U.; Tavana, B.; George, G.C.; Aaron, J.; Kurzrock, R. Targeting the molecular chaperone heat shock protein 90 (HSP90): Lessons learned and future directions. Cancer Treat. Rev., 2013, 39(4), 375-387.
[http://dx.doi.org/10.1016/j.ctrv.2012.10.001] [PMID: 23199899]
[7]
Kim, J.G.; Lee, S.C.; Kim, O.H.; Kim, K.H.; Song, K.Y.; Lee, S.K.; Choi, B.J.; Jeong, W.; Kim, S.J. HSP90 inhibitor 17-DMAG exerts anticancer effects against gastric cancer cells principally by altering oxidant-antioxidant balance. Oncotarget, 2017, 8(34), 56473-56489.
[http://dx.doi.org/10.18632/oncotarget.17007] [PMID: 28915605]
[8]
Cedrés, S.; Felip, E.; Cruz, C.; Martinez de Castro, A.; Pardo, N.; Navarro, A.; Martinez-Marti, A.; Remon, J.; Zeron-Medina, J.; Balmaña, J.; Llop-Guevara, A.; Miquel, J.M.; Sansano, I.; Nuciforo, P.; Mancuso, F.; Serra, V.; Vivancos, A. Activity of HSP90 inhibition in a metastatic lung cancer patient with a germline BRCA1 mutation. J. Natl. Cancer Inst., 2018, 110(8), 914-917.
[http://dx.doi.org/10.1093/jnci/djy012] [PMID: 29529211]
[9]
Gümus, M.; Ozgur, A.; Tutar, L.; Disli, A.; Koca, I.; Tutar, Y. Design, synthesis, and evaluation of heat shock protein 90 inhibitors in human breast cancer and its metastasis. Curr. Pharm. Biotechnol., 2016, 17(14), 1231-1245.
[http://dx.doi.org/10.2174/1389201017666161031105815] [PMID: 27804852]
[10]
Saini, J.; Sharma, P.K. clinical, prognostic and therapeutic significance of heat shock proteins in cancer. Curr. Drug Targets, 2018, 19(13), 1478-1490.
[PMID: 28831912]
[11]
Whitesell, L.; Shifrin, S.D.; Schwab, G.; Neckers, L.M. Benzoquinonoid ansamycins possess selective tumoricidal activity unrelated to SRC kinase inhibition. Cancer Res., 1992, 52(7), 1721-1728.
[PMID: 1551101]
[12]
Mellatyar, H.; Talaei, S.; Pilehvar-Soltanahmadi, Y.; Barzegar, A.; Akbarzadeh, A.; Shahabi, A.; Barekati-Mowahed, M.; Zarghami, N. Targeted cancer therapy through 17-DMAG as an Hsp90 inhibitor: Overview and current state of the art. Biomed. Pharmacother., 2018, 102, 608-617.
[http://dx.doi.org/10.1016/j.biopha.2018.03.102] [PMID: 29602128]
[13]
Jia, J.; Xu, X.; Liu, F.; Guo, X.; Zhang, M.; Lu, M.; Xu, L.; Wei, J.; Zhu, J.; Zhang, S.; Zhang, S.; Sun, H.; You, Q. Identification, design and bio-evaluation of novel Hsp90 inhibitors by ligand-based virtual screening. PLoS One, 2013, 8(4) e59315
[http://dx.doi.org/10.1371/journal.pone.0059315] [PMID: 23565147]
[14]
Saxena, S.; Chaudhaery, S.S.; Varshney, K.; Saxena, A.K. Pharmacophore-based virtual screening and docking studies on Hsp90 inhibitors. SAR QSAR Environ. Res., 2010, 21(5-6), 445-462.
[http://dx.doi.org/10.1080/1062936X.2010.501817] [PMID: 20818581]
[15]
Huang, X.Y.; Shan, Z.J.; Zhai, H.L.; Li, L.N.; Zhang, X.Y. Molecular design of anticancer drug leads based on three-dimensional quantitative structure-activity relationship. J. Chem. Inf. Model., 2011, 51(8), 1999-2006.
[http://dx.doi.org/10.1021/ci2002236] [PMID: 21755987]
[16]
Brough, P.A.; Aherne, W.; Barril, X.; Borgognoni, J.; Boxall, K.; Cansfield, J.E.; Cheung, K.M.; Collins, I.; Davies, N.G.; Drysdale, M.J.; Dymock, B.; Eccles, S.A.; Finch, H.; Fink, A.; Hayes, A.; Howes, R.; Hubbard, R.E.; James, K.; Jordan, A.M.; Lockie, A.; Martins, V.; Massey, A.; Matthews, T.P.; McDonald, E.; Northfield, C.J.; Pearl, L.H.; Prodromou, C.; Ray, S.; Raynaud, F.I.; Roughley, S.D.; Sharp, S.Y.; Surgenor, A.; Walmsley, D.L.; Webb, P.; Wood, M.; Workman, P.; Wright, L. 4,5-diarylisoxazole Hsp90 chaperone inhibitors: Potential therapeutic agents for the treatment of cancer. J. Med. Chem., 2008, 51(2), 196-218.
[http://dx.doi.org/10.1021/jm701018h] [PMID: 18020435]
[17]
Guven, A.; Aytek, A. New approach for stageA discharge relationship: Gene-expression programming. Hydrol. Eng, 2009, 14, 812-820.
[http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000044]
[18]
Froimowitz, M. HyperChem: A software package for computational chemistry and molecular modeling. Biotechniques, 1993, 14(6), 1010-1013.
[PMID: 8333944]
[19]
Boyd, D.B. Quantum chemistry program exchange. J. Mol. Graph. Model., 1999, 17(1), 62-63.
[PMID: 10660912]
[20]
Katritzky, A.R.; Lobanov, V.; Karelson, M. Comprehensive descriptors for structural and statistical analysis. 1: Correlations between structure and physical properties of substituted pyridines. Rev. Roum. Chim., 1996, 41(11), 851-867.
[21]
Wright, J.S.; Carpenter, D.J.; Mckay, D.J. Theoretical calculation of substituent effects on the O-H bond strength of phenolic antioxidants related to vitamin E. J. Am. Chem. Soc., 1997, 119(18), 4245-4252.
[http://dx.doi.org/10.1021/ja963378z]
[22]
Si, H.; Zhao, J.; Cui, L.; Lian, N.; Feng, H.; Duan, Y.B.; Hu, Z. Study of human dopamine sulfotransferases based on gene expression programming. Chem. Biol. Drug Des., 2011, 78(3), 370-377.
[http://dx.doi.org/10.1111/j.1747-0285.2011.01155.x] [PMID: 21668651]
[23]
Ferreira, C. Genetic representation and genetic neutrality in gene expression programming. Adv. Complex Syst., 2002, 5(4), 389-408.
[http://dx.doi.org/10.1142/S0219525902000626]
[24]
Yang, H.Z.; Liu, X.; Ye, Z.L. Studies on the IC50 of trisubstituted thiazoles as Cdc7 kinase inhibitors. Lett. Drug Des. Discov., 2016, 13(1), 33-42.
[http://dx.doi.org/10.2174/1570180812999150713144215]
[25]
Si, H.Z.; Yuan, S.P.; Zhang, K.J. Quantitative structure activity relationship study on EC50 of anti-HIV drugs. Chemom. Intell. Lab. Syst., 2008, 90(1), 15-24.
[http://dx.doi.org/10.1016/j.chemolab.2007.06.011]
[26]
Si, H.; Lian, N.; Yuan, S.; Fu, A.; Duan, Y.B.; Zhang, K.; Yao, X. Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds. Eur. J. Med. Chem., 2009, 44(10), 4044-4050.
[http://dx.doi.org/10.1016/j.ejmech.2009.04.039] [PMID: 19482386]
[27]
Li, Y.; You, G.; Jia, B.; Si, H.; Yao, X. Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming. BioMed Res. Int., 2014. 2014210672
[http://dx.doi.org/10.1155/2014/210672] [PMID: 24971318]
[28]
Si, H.Z.; Wang, T.; Zhang, K.J.; Hu, Z.D.; Fan, B.T. QSAR study of 1,4-dihydropyridine calcium channel antagonists based on gene expression programming. Bioorg. Med. Chem., 2006, 14(14), 4834-4841.
[http://dx.doi.org/10.1016/j.bmc.2006.03.019] [PMID: 16580211]
[29]
Fatemi, M.H.; Baher, E. A novel quantitative structure-activity relationship model for prediction of biomagnification factor of some organochlorine pollutants. Mol. Divers., 2009, 13(3), 343-352.
[http://dx.doi.org/10.1007/s11030-009-9121-4] [PMID: 19219557]
[30]
Katritzky, A R; Lobanov, V; Karelson, M Comprehensive descriptors for structural and statistical analysis reference manual, version 2.0. 1995.
[31]
Golmohammadi, H.; Safdari, M. Quantitative structure-property relationship prediction of gas-to-chloroform partition coefficient using artificial neural network. Microchem. J., 2010, 95(2), 140-151.
[http://dx.doi.org/10.1016/j.microc.2009.10.019]
[32]
Liu, X.Q.; Zhao, Y.T.; Tian, D.D. Studies on the IC50 of metabolically stable dibenzo[b’e]oxepin-11(6H)-ones as highly selective p38 MAP kinase inhibitors. J. Comput. Sci. Eng., 2017, 29, 829-840.
[33]
Song, F.; Cui, L.; Piao, J.; Liang, H.; Si, H.; Duan, Y.; Zhai, H. Quantitative structure-activity relationship and molecular docking studies on designing inhibitors of the perforin. Chem. Biol. Drug Des., 2017, 90(4), 535-544.
[http://dx.doi.org/10.1111/cbdd.12975] [PMID: 28296049]
[34]
Gepsoft. Automatic Problem Solver 3.0. http://www.gepsoft.com 2004.
[35]
Karelson, M.; Dobchev, D.A.; Kulshyn, O.V.; Katritzky, A.R. Neural networks convergence using physicochemical data. J. Chem. Inf. Model., 2006, 46(5), 1891-1897.
[http://dx.doi.org/10.1021/ci0600206] [PMID: 16995718]

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