IN SILICO ELUCIDATION OF SOME QUINOLINE DERIVATIVES WITH POTENT ANTI-BREAST CANCER ACTIVITIES.
DOI:
https://doi.org/10.18540/jcecvl6iss1pp0008-0014Palavras-chave:
Keywords, QSAR model, model validations, Breast cancer, quinoline derivatives.Resumo
Abstract: The toxicity and high resistance to the commercially sold breast-cancer drugs have become more alarming and the demand to produce new and less toxic breast-cancer drugs arises. In silico studies was carried out on some quinoline derivatives to investigate their reported activities against breast cancer and thereby generate a model with a better activity against breast cancer. The chemical structures of the compounds were optimized using Spartan software at Density Functional Theory (DFT) level, utilizing the B3LYP/ 6-31G* basis set. Four QSAR models were generated using Multi-Linear Regression (MLR) and Genetic Function Approximation (GFA) method. Equation one was chosen as the best model based on the validation parameters. The validation parameters was found to be statistically signi?cant with square correlation coefficient (R2) of 0.9853, adjusted square correlation coef?cient ( ) of 0.9816, cross validation coefficient ( ) of 0.9727 and an external correlation coefficient square ( ) of 0.6649 was used to validate the model. The built model was a good and robust one for it passed the minimum requirement for generating a QSAR model.
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COLEMAN, M. P.; QUARESMA, M.; BERRINO, F.; LUTZ, J. M.; DE ANGELIS, R.;
CAPOCACCIA, R.; BAILI, P.; RACHET, B.; GATTA, G.; HAKULINEN, T.; MICHELI, A.; SANT, M.; WEIR, H. K.; ELWOOD, J. M.; TSUKUMA, H.; KOIFMAN, S.; SILVA, G. A.; FRANCISCI, S.; SANTAQUILANI, M.; VERDECCHIA, A.; STORM, H. H.; YOUNG, J. L., Cancer survival in five continents. A worldwide population-based study (CONCORD), Lancet Oncol. 2008, 9:730-756 [PMID: 18639491 DOI: 10.1016/ S1470-2045(08)70179-7].
FERLAY, J.; SHINH. R.; BRAY, F.; FORMAN, D.; MATHERS, C.; PARKIN, D. M.
Estimates of worldwide burden of cancer in 2008. GLOBOCAN 2008, Int J Cancer. 2010, 127: 2893-2917 [PMID: 21351269 DOI: 10.1002/ijc.25516]
FERLIN, M. G.; CHIARELOTTO, G.; GASPAROTTO, V.; DALLA, V. L.; PEZZI, V.;
BARZON, L.; PALU`, G.; CASTAGLIUOLO, I., J. Med. Chem.2005, 48, 3417, DOI: 10.1021/jm049387.
FRANKISH H., 15 Million new cancer cases per year by 2020, says WHO, Lancet
2003, 1278–1287, DOI: 10.1016/S0140-6736(03)13038-3.
FRIEDMAN, J. H., Multivariate adaptive regression splines. Ann. Stat, 1991, 1-67.
GASPAROTTO, V.; CASTAGLIUOLO, I.; CHIARELOTTO, G.; PEZZI, V.;
MONTANARO, D.; BRUN, P.; PALU, G.; VIOLA, G.; FERLIN, M. G., J. Med.Chem. 2006, 49, 1910, DOI: 10.1021/jm0510676.
KENNARD, R. W.; STONE, L. A., Computer aided design of experiments.
Technometrics. J. Sci. Res.1969, 11, 137-48, DOI: 10.1080/
1969.10490666.
LARIF, M.; CHTITA, S.; ADAD, A.; HMAMOUCHI, R.; BOUACHRINE, M.;
LAKHLIFI, T., Predicting biological activity of Anticancer Molecules
-ary l-4-hydroxyquinolin-2-(1H)-one by DFT-QSAR models,
International Journal of Clinical and Experimental Medicine, 2013,
vol.3, pp.32–42, ISSN: 2319-8753.
MINOVSKI, N.; ˇZUPERL, ˇS. DRGAN, V.; NOVIˇC M., Assessment of applicability
domain for multivariate counter-propagation artificial neural network
predictive models by minimum Euclidean distance space analysis: A case study, Analytica Chimica Acta. 2013, vol. 759, pp. 28–42, DOI:10.1016/j.aca.2012.11.002.
SINGH, P., Quantitative Structure-activity relationship study of substituted-[1,2,4]
oxadiazoles as S1P1 agonists, J. Curr. Chem. Pharm. Sci.2013, 3, 64-
, ISSN: 2277-2871.
SHOLA, E. A.; UBA, S.; UZAIRU A., QSAR Modeling and Molecular Docking Analysis of
Some Active Compounds against Mycobacterium
tuberculosis Receptor (Mtb CYP121), J. Pathog. 2018
DOI:10.1155/2018/1018694.
TROPSHA, A.; GRAMATICA, P.; GOMBAR, V. K., the importance of being earnest:
validation is the absolute essential for successful application and
interpretation of QSPR models. Mol. Inform. 2003, 22, 69-77,
DOI:10.1002/qsar.200390007.
VEERASAMY, R.; RAJAK, H.; JAIN, A.; SIVADASAN, S.; VARGHESE, C. P.;
AGRAWAL, R. K., Validation of QSAR models-strategies and
importance, International Journal of Drug Design and Discovery.2011,
vol. 3, pp. 511–519.
WONG, K. Y.; MERCADER, A. G.; SAAVEDRA, L. M.; HONARPARVAR, B.;
ROMANELLI, G. P.; DUCHOWICZ, P. R., QSAR analysis on related
acetyl cholinesterase inhibitors, Journal of Biomedical Science 2014,
vol. 21, no.1, DOI: 10.1186/s12929-014-0084-0.