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Predictive QSAR models for the anti-cancer activity of topoisomerase IIα catalytic inhibitors against breast cancer cell line HCT15: GA-MLR and LS-SVM modeling

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Abstract

Structural analysis of topoisomerase IIα catalytic inhibitors exhibited anti-tumor properties to use them in cancer therapeutic procedures. In this study, a quantitative structure-activity relationship (QSAR) modeling including multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) analysis was applied on a series of 46 synthesized xanthone derivatives as topoisomerase IIα inhibitors. It was aimed to predict half-maximal inhibitory activity (IC50) of the compounds against breast cancer cell line HCT15 using the best computational method with the least error prediction. Genetic algorithm multiple linear regression (GA-MLR) explored the functional parameters of the final models. The achieved QSAR models presented a reliable relationship between chemical structure properties as descriptors and the inhibitory activities of compounds. The models were confirmed using Leave one out-cross-validation (LOO-CV) method (for the best linear model including whole dataset R2 = 0.955, Q2 = 0.930, RMSE = 0.151, F = 115.593 and for the best non-linear model R2 = 0.981, Q2 = 0.971, RMSE = 0.099, F = 258.902). So, it was demonstrated that these models especially non-linear models are predictive models of high quality that can produce appropriate inhibitory activity prediction of the compounds to use them in pharmaceutical industries.

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References

  1. Fadaka A, Ajiboye B, Ojo O, Adewale O, Olayide I, Emuowhochere R (2017) Biology of glucose metabolization in cancer cells. J Oncol Sci 3(2):45–51. https://doi.org/10.1016/j.jons.2017.06.002

    Article  Google Scholar 

  2. McDonald LA, Eldredge GS, Barrows LR, Ireland CM Inhibition of topoisomerase II catalytic activity by pyridoacridine alkaloids from a Cystodytes sp. ascidian: a mechanism for the apparent intercalator-induced inhibition of topoisomerase II. J Med Chem 37(22):3819–3827. https://doi.org/10.1021/jm00048a017

  3. Park S, Hong E, Kwak SY, Jun KY, Lee ES, Kwon Y, Na Y (2016) Synthesis and biological evaluation of C1-O-substituted-3-(3-butylamino-2-hydroxy-propoxy)-xanthen-9-one as topoisomerase IIα catalytic inhibitors. Eur J Med Chem 123:211–225. https://doi.org/10.1016/j.ejmech.2016.07.046

    Article  CAS  PubMed  Google Scholar 

  4. Larsen AK, Escargueil AE, Skladanowski A (2003) Catalytic topoisomerase II inhibitors in cancer therapy. Pharmacol Ther 99(2):167–181. https://doi.org/10.1016/S0163-7258(03)00058-5

    Article  CAS  PubMed  Google Scholar 

  5. Khaheshi S, Riahi S, Mohammadi-Khanaposhtani M, Shokrollahzadeh H (2019) Prediction of amines capacity for carbon dioxide absorption based on structural characteristics. Ind Eng Chem Res 58(20):8763–8771. https://doi.org/10.1021/acs.iecr.9b00567

    Article  CAS  Google Scholar 

  6. Gorji AE, Gorji ZE, Riahi S (2017) Quantitative structure-property relationship (QSPR) for prediction of CO 2 Henry’s law constant in some physical solvents with consideration of temperature effects. Korean J Chem Eng 34(5):1405–1415. https://doi.org/10.1007/s11814-017-0018-0

    Article  CAS  Google Scholar 

  7. Rezaei B, Riahi S (2016) Prediction of CO2 loading of amines in carbon capture process using membrane contactors: a molecular modeling. J Nat Gas Sci Eng 33:388–396. https://doi.org/10.1016/j.jngse.2016.05.003

    Article  CAS  Google Scholar 

  8. Mavaddat M, Riahi S (2016) A molecular structure based model for predicting optimal salinity of anionic surfactants. Fluid Phase Equilib 409:354–360. https://doi.org/10.1016/j.fluid.2015.10.010

    Article  CAS  Google Scholar 

  9. Momeni M, Riahi S (2015) An investigation into the relationship between molecular structure and rich/lean loading of linear amine-based CO2 absorbents. Int J Greenh Gas Control 42:157–164. https://doi.org/10.1016/j.ijggc.2015.07.037

    Article  CAS  Google Scholar 

  10. Momeni M, Riahi S (2014) Prediction of amines capacity for carbon dioxide absorption in gas sweetening processes. J Nat Gas Sci Eng 21:442–450. https://doi.org/10.1016/j.jngse.2014.09.002

    Article  CAS  Google Scholar 

  11. He G, Feng L, Chen H (2012) A QSAR study of the acute toxicity of halogenated phenols. Procedia Eng 43:204–209. https://doi.org/10.1016/j.proeng.2012.08.035

    Article  CAS  Google Scholar 

  12. Rezaei B, Riahi S, Gorji AE (2020) Molecular investigation of amine performance in the carbon capture process: least squares support vector machine approach. Korean J Chem Eng 37(1):72–79. https://doi.org/10.1007/s11814-019-0408-6

    Article  CAS  Google Scholar 

  13. Mehraein I, Riahi S (2017) The QSPR models to predict the solubility of CO2 in ionic liquids based on least-squares support vector machines and genetic algorithm-multi linear regression. J Mol Liq 225:521–530. https://doi.org/10.1016/j.molliq.2016.10.133

    Article  CAS  Google Scholar 

  14. Abbasi-Radmoghaddam Z, Riahi S, Gharaghani S, Mohammadi-Khanaposhtanai M (2020) Design of potential anti-tumor PARP-1 inhibitors by QSAR and molecular modeling studies. Mol Divers:1–15. https://doi.org/10.1007/s11030-020-10063-9

  15. Bak A, Kozik V, Walczak M, Fraczyk J, Kaminski Z, Kolesinska B, Smolinski A, Jampilek J (2018) Towards intelligent drug design system: application of artificial dipeptide receptor library in QSAR-oriented studies. Molecules 23(8):1964. https://doi.org/10.3390/molecules23081964

    Article  CAS  PubMed Central  Google Scholar 

  16. Maicheen C, Phosrithong N, Jittikoon J, Ungwitayatorn J (2018) Topoisomerase I inhibitory activity and 3D QSAR studies of chromone derivatives. Chiang Mai J Science 45(2):1073–1086

    CAS  Google Scholar 

  17. Luo Y, Zhou Y, Song Y, Chen G, Wang YX, Tian Y, Fan WW, Yang YS, Cheng T, Zhu HL (2018) Optimization of substituted cinnamic acyl sulfonamide derivatives as tubulin polymerization inhibitors with anticancer activity. Bioorg Med Chem Lett 28(23–24):3634–3638. https://doi.org/10.1016/j.bmcl.2018.10.037

    Article  CAS  PubMed  Google Scholar 

  18. Masand VH, El-Sayed NN, Bambole MU, Quazi SA (2018) Multiple QSAR models, pharmacophore pattern and molecular docking analysis for anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues. J Mol Struct 1157:89–96. https://doi.org/10.1016/j.molstruc.2017.12.045

    Article  CAS  Google Scholar 

  19. Yuanita EM, Pranowo HD, Jumina JU, Mustofa MU (2016) Design of hydroxy xanthones derivatives as anticancer using quantitative structure-activity relationship. Asian J Pharm Clin Res 9(2):3–8

    Google Scholar 

  20. Abdelhaleem EF, Abdelhameid MK, Kassab AE, Kandeel MM (2018) Design and synthesis of thienopyrimidine urea derivatives with potential cytotoxic and pro-apoptotic activity against breast cancer cell line MCF-7. Eur J Med Chem 143:1807–1825. https://doi.org/10.1016/j.ejmech.2017.10.075

    Article  CAS  PubMed  Google Scholar 

  21. Karki R, Jun KY, Kadayat TM, Shin S, Magar TB, Bist G, Shrestha A, Na Y, Kwon Y, Lee ES (2016) A new series of 2-phenol-4-aryl-6-chlorophenyl pyridine derivatives as dual topoisomerase I/II inhibitors: synthesis, biological evaluation and 3D-QSAR study. Eur J Med Chem 113:228–245. https://doi.org/10.1016/j.ejmech.2016.02.050

    Article  CAS  PubMed  Google Scholar 

  22. Release H. (2002) 7.5 for Windows. Molecular modeling system, Hypercube Inc http://www hyper com

  23. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H. (2009). Gaussian 09; Gaussian, Inc. Wallingford, 32, 5648–5652

  24. Todeschini R, Consonni V, Mauri A, Pavan M. (2002). DRAGON-software for the calculation of molecular descriptors version 2.1

  25. Kerwin SM (2010). ChemBioOffice ultra 2010 suite. https://doi.org/10.1021/ja1005306

  26. Golbraikh A, Tropsha A (2000) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Mol Divers 5(4):231–243. https://doi.org/10.1023/A:1021372108686

    Article  CAS  Google Scholar 

  27. Mercader AG, Duchowicz PR (2015) Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories. Chemom Intell Lab Syst 149:117–122. https://doi.org/10.1016/j.chemolab.2015.10.007

    Article  CAS  Google Scholar 

  28. Anter AM, Moemen YS, Darwish A, Hassanien AE (2020) Multi-target QSAR modelling of chemo-genomic data analysis based on extreme learning machine. Knowl-Based Syst 188:104977. https://doi.org/10.1016/j.knosys.2019.104977

    Article  Google Scholar 

  29. Ghamali M, Chtita S, Hmamouchi R, Adad A, Bouachrine M, Lakhlifi T (2016) The inhibitory activity of aldose reductase of flavonoid compounds: combining DFT and QSAR calculations. J Taibah Univ Sci 10(4):534–542. https://doi.org/10.1016/j.jtusci.2015.09.006

    Article  Google Scholar 

  30. Sharifi M, Ghadamyari M, Gholivand K, Valmoozi AA, Sajedi RH (2017) Characterization of acetylcholinesterase from elm left beetle, Xanthogaleruca luteola and QSAR of temephos derivatives against its activity. Pestic Biochem Physiol 136:12–22. https://doi.org/10.1016/j.pestbp.2016.08.010

    Article  CAS  PubMed  Google Scholar 

  31. Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MT, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA, Myatt G (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships: the report and recommendations of ecvam workshop 52. Altern Lab Anim 33(2):155–173. https://doi.org/10.1177/026119290503300209

    Article  CAS  PubMed  Google Scholar 

  32. Kupcewicz B, Balcerowska-Czerniak G, Małecka M, Paneth P, Krajewska U, Rozalski M (2013) Structure–cytotoxic activity relationship of 3-arylideneflavanone and chromanone (E, Z isomers) and 3-arylflavones. Bioorg Med Chem Lett 23(14):4102–4106. https://doi.org/10.1016/j.bmcl.2013.05.044

    Article  CAS  PubMed  Google Scholar 

  33. Katritzky AR, Pacureanu LM, Slavov SH, Dobchev DA, Karelson M (2008) QSPR study of critical micelle concentrations of nonionic surfactants. Ind Eng Chem Res 47(23):9687–9695. https://doi.org/10.1021/ie800954k

    Article  CAS  Google Scholar 

  34. Ghaslani D, Gorji ZE, Gorji AE, Riahi S (2017) Chemical engineering research and design descriptive and predictive models for Henry ’ s law constant of CO 2 in ionic liquids : a QSPR study. Chem Eng Res Des 120:15–25. https://doi.org/10.1016/j.cherd.2016.12.020

    Article  CAS  Google Scholar 

  35. Yap CW, Chen YZ (2005) Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network. J Pharm Sci 94(1):153–168. https://doi.org/10.1002/jps.20232

    Article  CAS  PubMed  Google Scholar 

  36. Ma S, Lv M, Zhang X, Zhai H, Lv W (2015) Computational study of the effects of cations and anions to the cytotoxicity of diverse ionic liquids by supervised machine learning. Chemom Intell Lab Syst 144:138–147. https://doi.org/10.1016/j.chemolab.2015.03.014

    Article  CAS  Google Scholar 

  37. Daghir-Wojtkowiak E, Wiczling P, Bocian S, Kubik Ł, Kośliński P, Buszewski B, Kaliszan R, Markuszewski MJ (2015) Least absolute shrinkage and selection operator and dimensionality reduction techniques in quantitative structure retention relationship modeling of retention in hydrophilic interaction liquid chromatography. J Chromatogr A 1403:54–62. https://doi.org/10.1016/j.chroma.2015.05.025

    Article  CAS  PubMed  Google Scholar 

  38. Zheng F, Zheng G, Deaciuc AG, Zhan CG, Dwoskin LP, Crooks PA (2007) Computational neural network analysis of the affinity of lobeline and tetrabenazine analogs for the vesicular monoamine transporter-2. Bioorg Med Chem 15(8):2975–2992. https://doi.org/10.1016/j.bmc.2007.02.013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhang J, Nan X, Yu HT, Cheng PL, Zhang Y, Liu YQ, Zhang SY, Hu GF, Liu H, Chen AL (2016) Synthesis, biological activities and structure− activity relationships for new avermectin analogues. Eur J Med Chem 121:422–432. https://doi.org/10.1016/j.ejmech.2016.05.056

    Article  CAS  PubMed  Google Scholar 

  40. Kupcewicz B, Małecka M, Zapadka M, Krajewska U, Rozalski M, Budzisz E (2016) Quantitative relationships between structure and cytotoxic activity of flavonoid derivatives. An application of Hirshfeld surface derived descriptors. Bioorg Med Chem Lett 26(14):3336–3341. https://doi.org/10.1016/j.bmcl.2016.05.038

    Article  CAS  PubMed  Google Scholar 

  41. Fahmy T (2013) XLSTAT, Version 2013. Addinsoft, Paris

    Google Scholar 

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Acknowledgments

The authors like to acknowledge the Institute of Petroleum Engineering (IPE), the University of Tehran for their support.

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Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Caspian, College of Engineering, University of Tehran, Tehran, Iran

    Niloufar Rahmani

  2. Institute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Zeinab Abbasi-Radmoghaddam & Siavash Riahi

  3. Faculty of Fouman, College of Engineering, University of Tehran, Tehran, Iran

    Mohammad Mohammadi-Khanaposhtanai

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Correspondence to Siavash Riahi.

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Rahmani, N., Abbasi-Radmoghaddam, Z., Riahi, S. et al. Predictive QSAR models for the anti-cancer activity of topoisomerase IIα catalytic inhibitors against breast cancer cell line HCT15: GA-MLR and LS-SVM modeling. Struct Chem 31, 2129–2145 (2020). https://doi.org/10.1007/s11224-020-01543-7

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