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Volume 67, Issue 3


Prediction of anti-tuberculosis activity of 3-phenyl-2H-1,3-benzoxazine-2,4(3H)-dione derivatives

Peter Nemeček / Ján Mocák / Jozef Lehotay / Karel Waisser
Published Online: 2012-12-27 | DOI: https://doi.org/10.2478/s11696-012-0278-4


Correlation analysis and, in particular, artificial neural networks (ANN) were used to predict the anti-mycobacterial activity of substituted 3-phenyl-2H-1,3-benzoxazine-2,4(3H)-diones (PBODs) by quantitative structure — activity relationship (QSAR) calculations. Initially, sixty-four derivatives were synthesised and biologically tested; ten further derivatives were proposed for future synthesis on the basis of the prediction results. The biological activity was originally expressed by minimum inhibitory concentration (MIC) against Mycobacterium tuberculosis; however, its transformed pMIC form was found to be more informative. Theoretical molecular descriptors of several types were selected to establish a primary drug model of the species which was expected to exhibit a substantial anti-mycobacterial effect. Lipophilicity and solubility indices, several basic molecular properties, quantum chemistry quantities as well as 1H and 13C NMR chemical shifts, were employed as the descriptors, enabling a very successful prediction of the pMIC values. The utilisation of in silico variables and simulated NMR data is highly advantageous in the first phase of the drug design, as they permit prediction of the compounds with a high expected activity, minimising the risk of synthesising less active species. The MIC values predicted at less than 4 μmol L−1 for six of the ten compounds suggested for further synthesis are better than the best value for the original set of compounds.

Keywords: 3-phenyl-2H-1,3-benzoxazine-2,4(3H)-diones; Mycobacterium tuberculosis; minimum inhibitory concentration; QSAR; artificial neural networks

  • [1] ACD/Labs (2003). ACD/PhysChem, Version 7.0 [computer software]. Toronto, Canada: Advanced Chemistry Development. Google Scholar

  • [2] Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford, UK: Oxford University Press. Google Scholar

  • [3] Corbett, E. L., Churchyard, G. J., Hay, M., Herselman, P., Clayton, T., Williams, B., Hayes, R., Mulder, D.,& De Cock, K. M. (1999). The impact of HIV infection on Mycobacterium kansasii disease in South African gold miners. American Journal of Respiratory and Critical Care Medicine, 160, 10–14. Google Scholar

  • [4] Devillers, J. (Ed.) (1996). Neural networks in QSAR and drug design (Series: Principles of QSAR and drug design). London, UK: Academic Press. Google Scholar

  • [5] Dewar, M. J. S., Zoebisch, E.G., Healy, E. F.,& Stewart, J. J. P. (1985). Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. Journal of the American Chemical Society, 107, 3902–3909. DOI: 10.1021/ja00299a024. http://dx.doi.org/10.1021/ja00299a024CrossrefGoogle Scholar

  • [6] Ďurčeková, J., Mocák, J., Lehotay, J., Čižmárik, J., & Boronová, K. (2010). Chemometrical study of the anaesthetical activity of alkoxyphenylcarbamic acid esters. Die Pharmazie — An International Journal of Pharmaceutical Sciences, 65, 169–174. DOI: 10.1691/ph.2010.9691. CrossrefGoogle Scholar

  • [7] Ďurčeková, J., Mocák, J., Boronová, K.,& Balla, J. (2011). Effect of the statin therapy on biochemical laboratory tests—A chemometrics study. Journal of Pharmaceutical and Biomedical Analysis, 54, 141–147. DOI: 10.1016/j.jpba.2010. 07.047. http://dx.doi.org/10.1016/j.jpba.2010.07.047Web of ScienceCrossrefGoogle Scholar

  • [8] Ďurčeková, T., Boronová, K., Mocák, J., Lehotay, J.,& Čižmárik, J. (2012). QQSRR models for potential local anaesthetic drugs using high performance liquid chromatography. Journal of Pharmaceutical and Biomedical Analysis, 59, 209–216. DOI: 10.1016/j.jpba.2011.09.035. http://dx.doi.org/10.1016/j.jpba.2011.09.035CrossrefWeb of ScienceGoogle Scholar

  • [9] El-Sadr, W. M., Burman, W. J., Grant, L. B., Matts, J. P., Hafner, R., Crane, L., Zeh, D., Gallagher, B., Mannheimer, S. B., Martinez, A.,& Gordin, F. (2000). Discontinuation of prophylaxis against Mycobacterium avium complex disease in HIV-infected patients who have a response to antiretroviral therapy. The New England Journal of Medicine, 342, 1085–1092. DOI: 10.1056/nejm200004133421503. http://dx.doi.org/10.1056/NEJM200004133421503CrossrefGoogle Scholar

  • [10] Frieden, T. R., Sterling, T. R., Munsiff, S. S., Watt, C. J.,& Dye, C. (2003). Tuberculosis. Lancet, 362, 887–899. DOI: 10.1016/s0140-6736(03)14333-4. http://dx.doi.org/10.1016/S0140-6736(03)14333-4CrossrefGoogle Scholar

  • [11] Lin, L. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45, 255–268. DOI: 10.2307/2532051. http://dx.doi.org/10.2307/2532051CrossrefGoogle Scholar

  • [12] Marras, T. K., Morris, A., Gonzalez, L. C.,& Daley, C. L. (2004). Mortality prediction in pulmonary Mycobacterium kansasii infection and human immunodeficiency virus. American Journal of Respiratory and Critical Care Medicine, 170, 793–798. http://dx.doi.org/10.1164/rccm.200402-162OCGoogle Scholar

  • [13] Mitha, M., Naicker, P.,& Taljaard, J. (2011). Cutaneous Mycobacterium kansasii infection in a patient with AIDS post initiation of antiretroviral therapy. The Journal of Infection in Developing Countries, 5, 553–555. http://dx.doi.org/10.3855/jidc.1733Web of ScienceGoogle Scholar

  • [14] Mohan Raju, M., Srivastava, R. K., Bisht, D. C. S., Sharma, H. C., & Kumar, A. (2011). Development of artificial neuralnetwork-based models for the simulation of spring discharge. Advances in Artificial Intelligence, 2011, 686258. DOI: 10.1155/2011/686258. CrossrefGoogle Scholar

  • [15] Myers, J. L., & Well, A. D. (2003). Research design and statistical analysis (2nd ed.). Mahwah, NJ, USA: Lawrence Erlbaum Associates. Google Scholar

  • [16] Nemeček, P., Ďurčekovák, J., Waisser, K., & Lehotay, J. (2008). Utilization of artificial neural networks for prediction of biological activity in novel QSAR approach. In P. Glavič, & D. Brodnjak-Vončina (Eds.), Proceedings of the Slovenian Chemical Days, September 25–26, 2008. Maribor, Slovenia: Slovensko Kemijsko Družstvo. Google Scholar

  • [17] Nemeček, P., Ďurčeková, T., Mocák, J.,& Waisser, K. (2009). Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction. Chemical Papers, 63, 84–91. DOI: 10.2478/s11696-008-0089-9. http://dx.doi.org/10.2478/s11696-008-0089-9Web of ScienceCrossrefGoogle Scholar

  • [18] Niculescu, S. P. (2003). Artificial neural networks and genetic algorithms in QSAR. Journal of Molecular Structure (THEOCHEM), 622, 71–83. DOI: 10.1016/s0166-1280(02) 00619-x. http://dx.doi.org/10.1016/S0166-1280(02)00619-XCrossrefGoogle Scholar

  • [19] Patterson, D.W. (1996). Artificial neural networks: Theory and applications (Prentice Hall series in advanced communications). Singapore: Prentice Hall. Google Scholar

  • [20] Petrlíková, E., Waisser, K., DoleŽal, R., Holy, P., Gregor, J., Kuneš, J.,& Kaustová, J. (2011). Antimycobacterial 3-phenyl-4-thioxo-2H-1,3-benzoxazine-2(3H)-ones and 3-phenyl-2H-1,3-benzoxazine-2,4(3H)-dithiones substituted on phenyl and benzoxazine moiety in position 6. Chemical Papers, 65, 352–366. DOI: 10.2478/s11696-011-0020-7. http://dx.doi.org/10.2478/s11696-011-0020-7CrossrefWeb of ScienceGoogle Scholar

  • [21] Shepherd, A. J. (1997). Second-order methods for neural networks: Fast and reliable training methods for multi-layer perceptrons (Series: Perspectives in neural computing). New York, NY, USA: Springer. Google Scholar

  • [22] StatSoft (1984). STATISTICA, Version 7.0 [computer software]. Tulsa, OK, USA: StatSoft. Google Scholar

  • [23] Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P., Palyulin, V. A., Radchenko, E.V., Zefirov, N. S., Makarenko, A. S., Tanchuk, V. Y., & Prokopenko, V. V. (2005). Virtual computational chemistry laboratory — design and description. Journal of Computer-Aided Molecular Design, 19, 453–463. DOI: 10.1007/s10822-005-8694-y. http://dx.doi.org/10.1007/s10822-005-8694-yCrossrefGoogle Scholar

  • [24] Tomioka, H.,& Namba, K. (2006). Development of antituberculous drugs: Current status and future prospects. Kekkaku, 81, 753–774. Google Scholar

  • [25] VCCLAB (2005). Virtual computational chemistry laboratory. Retrieved from http://www.vcclab.org Google Scholar

  • [26] Waisser, K., Kubicová, L., Klimešová, Z. (1993). New groups of potential antituberculotics: 3-aryl-2H,4H-benz[e][1,3]oxazine-2,4-diones. Comparison of the Topliss approach with regression analysis. Collection of Czechoslovak Chemical Communications, 58, 2977–2982. DOI: 10.1135/cccc19932977. http://dx.doi.org/10.1135/cccc19932977CrossrefGoogle Scholar

  • [27] Waisser, K., Hladuvková, J., Gregor, J., Rada, T., Kubicová, L., Klimešová, V., & Kaustová, J. (1998). Relationships between the chemical structure of antimycobacterial substances and their activity against atypical strains. Part 14: 3-aryl-6,8-dihalogeno-2H-1,3-benzoxazine-2,4(3H)-diones. Archiv der Pharmazie, 331, 3–6. DOI: 10.1002/(SICI)1521-4184(199801) 331:1〈3::AID-ARDP3〉3.0.CO;2-2. http://dx.doi.org/10.1002/(SICI)1521-4184(199801)331:1<3::AID-ARDP3>3.0.CO;2-2CrossrefGoogle Scholar

  • [28] Waisser, K., Macháček, M., Dostál, H., Gregor, J., Kubicová, L., Klimešovš, J., Palát, K., Jr., Hladůvkov, J., Kaustová, J.,& Möllmann, U. (1999). Relationships between the chemical structure of substances and their antimycobacterial activity against atypical strains. Part 18. 3-Phenyl-2H-1,3-benzoxazine-2,4(3H)-diones and isosteric 3-phenylquinazoline-2,4(lH,3H)-diones. Collection of Czechoslovak Chemical Communications, 64, 1902–1924. DOI: 10.1135/cccc19991902. http://dx.doi.org/10.1135/cccc19991902CrossrefGoogle Scholar

  • [29] Waisser, K., Hladůvková, J., Holy, P., Macháčmk, M., Karajannis, P., Kubicová, L., Klimešová, V., Kuneš, J., & Kaustová, J. (2001). 2H-1,3-Benzoxazine-2,4(3H)-diones substituted in position 6 as antimycobacterial agents. Chemical Papers, 55, 323–334. Google Scholar

  • [30] Waisser, K., Bureš, O., Holy, P., Kuneš, J., Oswald, R., Jirásková, L., Pour, M., Klimešovát, K., Jr., Kaustová, J., Dahse, H. M.,& Möllmann, U. (2003). Antimycobacterial 3-aryl-2H-1,3-benzoxazine-2,4(3H)-diones. Pharmazie, 58, 83–94. DOI: 10.1002/chin.200324133. CrossrefGoogle Scholar

  • [31] Waisser, K., Matyk, J., Divišováková, P., Kuneš, J., Klimešová, J., Möllmann, U., Dahse, H. M.,& Miko, M. (2006). The oriented development of antituberculotics: Salicylanilides. Archiv der Pharmazie, 339, 616–620. DOI: 10.1002/ardp.200600093. http://dx.doi.org/10.1002/ardp.200600093Web of ScienceCrossrefGoogle Scholar

  • [32] Waisser, K., Matyk, J., Kuneš, J., Doležal, R., Kaustová, J.,& Dahse, H. M. (2008). Highly active potential antituberculotics: 3-(4-Alkylphenyl)-4-thioxo-2H-1,3-benzoxazine-2(3H)-ones and 3-(4-alkylphenyl)-2H-1,3-benzoxazine-2,4(3H)-dihiones substituted in ring-B by halogen. Archiv der Pharmazie, 341, 800–803. DOI: 10.1002/ardp.200800004. http://dx.doi.org/10.1002/ardp.200800004Web of ScienceCrossrefGoogle Scholar

  • [33] Zupan, J., & Gasteiger, J. (1999). Neural networks in chemistry and drug design (2nd ed.). Weinheim, Germany: Wiley. Google Scholar

About the article

Published Online: 2012-12-27

Published in Print: 2013-03-01

Citation Information: Chemical Papers, Volume 67, Issue 3, Pages 305–312, ISSN (Online) 1336-9075, DOI: https://doi.org/10.2478/s11696-012-0278-4.

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