IN SILICO STUDY FOR INVESTIGATING AND PREDICTING THE ACTIVITIES OF 1,2,4-TRIAZOLE DERIVATIES AS POTENT ANTI-TUBERCULAR AGENTS
DOI:
https://doi.org/10.18540/jcecvl4iss2pp0246-0254Palabras clave:
Tuberculosis, 1, 2, 4-Triazole, QSAR, Applicability domain, Y-Randomization.Resumen
Abstract
In silico study was carried on a dataset of 1,2,4-Triazole derivatives to investigate their activities behaviour on mycobacterium tuberculosis by utilizing Quantitative Structure-Activity Relationship (QSAR) technique. Genetic Function Algorithm (GFA) and Multiple Linear Regression Analysis (MLRA) were used to select the optimum descriptors and to generate the correlation QSAR model that relate their activities values against mycobacterium tuberculosis with the molecular structures of the inhibitors. The model was validated and was found to have squared correlation coefficient (R2) of 0.9134, adjusted squared correlation coefficient (Radj) of 0.8753 and Leave one out (LOO) cross validation coefficient (Qcv^2) value of 0.8231. The external validation set used for confirming the predictive power of the model has R2pred of 0.7482. Stability and robustness of the model obtained by the validation test indicate that the model can be used to design and synthesis other 1,2,4-Triazole derivatives with improved anti-mycobacterium tuberculosis activities
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YAKAR, A.; YAKAR, F.; YILDIZ, Z. Isoniazid-and rifampicin- induced thrombocytopenia. Multidisciplinary Respiratory Medicine, v.8, 13, 2013
AFANTITIS, A.; MELAGRAKI, G.; SARIMVEIS, H.; KOUTENTIS, P. A.; MARKOPOULOS, J.; IGGLESSI-MARKOPOULOU, O. A novel QSAR model for predicting induction of apoptosis by 4-aryl- 4H-chromenes. Bioorganic and Medicinal Chemistry, v.14, p.6686–6694, 2006.
AZIZ, M. A.; WRIGHT, A.; LASZLO, A.; DE MUYNCK, A.; PORTAELS, F.; VAN DEUN, A.; RAVIGLIONE, M. WHO/International Union Against Tuberculosis And Lung Disease Global Project on Anti- tuberculosis Drug Resistance Surveillance. Epidemiology of antituberculosis drug resistance (the Global Project on Anti-tuberculosis Drug Resistance Surveillance): an upd. Lancet, v.368, p. 2142–2154, 2006.
BALABANOVA, Y.; RUDDY, M., HUBB, J.; YATES, M.; MALOMANOVA, N.; FEDORIN, I.; DROBNIEWSKI, F. Multidrug-resistant tuberculosis in Russia: clinical characteristics, analysis of second- line drug resistance and development of standardized therapy. European Journal of Clinical Microbiology and Infectious Diseases, v. 24, p. 36–139, 2005.
CHAKRABORTI, A. K.; GOPALAKRISHNAN, B.; SOBHIA, M. E.; MALDE, A. 3D-QSAR studies of indole derivatives as phosphodiesterase IV inhibitors. European Journal of Medicinal Chemistry, v.38, p. 975–982, 2003.
CRAMER, R. D., PATTERSON, D. E., & BUNCE, J. D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. Journal of the American Chemical Society, v. 110, p. 5959–5967, 1998.
IBEZIM, E. C.; DUCHOWICZ, P. R.; IBEZIM, N. E.; MULLEN, L. M. A.; ONYISHI, I. V.; BROWN, S. A.; CASTRO, E. A. Computer-aided linear modeling employing QSAR for drug discovery. Scientific Research and Essays, v.4, p.1559–1564, 2009.
JHAMB, S. S.; GOYAL, A.; SINGH, P. P. Determination of the activity of standard anti-tuberculosis drugs against intramacrophage Mycobacterium tuberculosis, in vitro: MGIT 960 as a viable alternative for BACTEC 460. Brazilian Journal of Infectious Diseases, v.18, p. 336–340, 2014.
KENNARD, R. W.; STONE, L. A. Computer aided design of experiments. Technometrics, v. 11, p. 137–148, 1969.
KHALED, K. F. Modeling corrosion inhibition of iron in acid medium by genetic function approximation method: A QSAR model. Corrosion Science, v. 53, p.3457–3465, 2011.
LÖNNROTH, K.; CASTRO, K. G.; CHAKAYA, J. M.; CHAUHAN, L. S.; FLOYD, K.; GLAZIOU, P.; RAVIGLIONE, M. C. Tuberculosis control and elimination 2010–50: cure, care, and social development. The Lancet, v.375, p. 1814–1829, 2010.
MELAGRAKI, G.; AFANTITIS, A.; MAKRIDIMA, K.; SARIMVEIS, H.; IGGLESSI-MARKOPOULOU, O. Prediction of toxicity using a novel RBF neural network training methodology. Journal of Molecular Modeling, v.12, p.297–305, 2006.
MINOVSKI, N.; ŽUPERL, Š.; DRGAN, V.; NOVI?, 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, v. 759, p.28–42, 2013.
ROGERS, D.; & HOPFINGER, A. J. Application of genetic function approximation to quantitative structure- activity relationships and quantitative structure- property relationships. Journal of Chemical Information and Computer Sciences, v.34, p.854–866, 1994.
ROY, K. On some aspects of validation of predictive quantitative structure–activity relationship models. Expert Opinion on Drug Discovery, v.2, p.1567– 1577, 2007.
SARKAR, D.; DESHPANDE, S. R.; MAYBHATE, S. P.; LIKHITE, A. P.; SARKAR, S.; KHAN, A.; CHAVAN, S. R. 1, 2, 4- derivatives and their anti- microbial activity. Google Patents.2016.
TROPSHA, A. Best practices for QSAR model development, validation, and exploitation. Molecular Informatics, v. 29, p. 476–488, 2010.
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. Molecular Informatics, v.22, p.69– 77, 2003.
WU, W.; WALCZAK, B.; MASSART, D. L.; HEUERDING, S.; ERNI, F.; LAST, I. R.; PREBBLE, K. A. Artificial neural networks in classification of NIR spectral data: design of the training set. Chemometrics and Intelligent Laboratory Systems, v. 33, p.35–46, 1996.
YAKAR, A.; YAKAR, F.; YILDIZ, Z. Isoniazid-and rifampicin- induced thrombocytopenia. Multidisciplinary Respiratory Medicine, v.8, 13, 2013
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Publicado
2018-07-04
Cómo citar
ADENIJI, S. E., Uba, S., & Uzairu, A. (2018). IN SILICO STUDY FOR INVESTIGATING AND PREDICTING THE ACTIVITIES OF 1,2,4-TRIAZOLE DERIVATIES AS POTENT ANTI-TUBERCULAR AGENTS. The Journal of Engineering and Exact Sciences, 4(2), 0246–0254. https://doi.org/10.18540/jcecvl4iss2pp0246-0254
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Sección
Physical Chemistry
