Chemoinformatics in Drug Design. Artificial Neural Networks for Simultaneous Prediction of Anti-enterococci Activities and Toxicological Profiles

Alejandro Speck-Planche, M. N. D. S. Cordeiro

2013

Abstract

Enterococci are dangerous opportunistic pathogens which are responsible of a huge number of nosocomial infections, displaying intrinsic resistance to many antibiotics. The battle against enterococci by using antimicrobial chemotherapies will depend on the design of new antibacterial agents with high activity and low toxicity. Multi-target methodologies focused on quantitative-structure activity relationships (mt-QSAR), have contributed to rationalize the process of drug discovery, improving the knowledge about the molecular patterns related with antimicrobial activity. Until know, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. Here, we developed a unified mtk-QSBER (multitasking quantitative-structure biological effect relationships) model for simultaneous prediction of anti-enterococci activity and toxicity on laboratory animal and human immune cells. The mtk-QSBER model was created by using artificial neural network (ANN) analysis combined with topological indices, with the aim of classifying compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under multiple experimental conditions. The mtk-QSBER model correctly classified more than 90% of the whole dataset (more than 10900 cases). We used the model to predict multiple biological effects of the investigational drug BC-3781. Results demonstrate that our mtk-QSBER may represent a new horizon for the discovery of desirable anti-enterococci drugs.

References

  1. Balaban, A. T., 1982. Highly discriminating distancebased topological index. Chemical Physics Letters. 89(5):399-404.
  2. Brachman, P. S., Abrutyn, E., 2009. Bacterial Infections of Humans: Epidemiology and Control, Springer Science+Business Media, LLC. New York, NY.
  3. Estrada, E., 1995. Edge adjacency relationship and a novel topological index related to molecular volume. Journal of Chemical Information and Computer Sciences. 35:31-33.
  4. Estrada, E., 2002. Physicochemical Interpretation of Molecular Connectivity Indices. The Journal of Physical Chemistry A. 106(39):9085-9091.
  5. Estrada, E., Gutiérrez, Y., 2002-2004. MODESLAB, v1.5. Santiago de Compostela.
  6. Fisher, K., Phillips, C., 2009. The ecology, epidemiology and virulence of Enterococcus. Microbiology. 155(Pt 6):1749-1757.
  7. Flaherty, D., 2012. Immunology for Pharmacy, Mosby, Elsevier Inc. St. Louis, Missouri.
  8. Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., Overington, J. P., 2012. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research. 40:D1100- 1107.
  9. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I. H., 1999-2013. WEKA 3.6.9. Waikato Environment for Knowledge Analysis. Hamilton.
  10. Hau, J., Schapiro, S. J., 2011. Handbook of Laboratory Animal Science: Essential Principles and Practices, CRC Press, Taylor & Francis Group, LLC. Boca Raton, FL.
  11. Hill, T., Lewicki, P., 2006. STATISTICS Methods and Applications. A Comprehensive Reference for Science, Industry and Data Mining, StatSoft. Tulsa.
  12. Kier, L. B., Hall, L. H., 1986. Molecular connectivity in structure-activity analysis, Research Studies Press, Wiley Letchworth, Hertfordshire, England, New York.
  13. Munteanu, C. R., Magalhaes, A. L., Uriarte, E., GonzalezDiaz, H., 2009. Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. Journal Theoretical Biology. 257(2):303-311.
  14. Oprea, T., 2005. Chemoinformatics in Drug Discovery, WILEY-VCH Verlag GmbH & Co. KGaA. Weinheim.
  15. Prado-Prado, F. J., Borges, F., Perez-Montoto, L. G., Gonzalez-Diaz, H., 2009. Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species. European Journal Medicinal Chemistry. 44(10):4051-4056.
  16. Prado-Prado, F. J., Garcia-Mera, X., Gonzalez-Diaz, H., 2010. Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorganic and Medicinal Chemistry. 18(6):2225-2231.
  17. Ryan, K. J., Ray, C. G., 2004. Sherris Medical Microbiology McGraw Hill. Arizona.
  18. Sader, H. S., Biedenbach, D. J., Paukner, S., IvezicSchoenfeld, Z., Jones, R. N., 2012. Antimicrobial activity of the investigational pleuromutilin compound BC-3781 tested against Gram-positive organisms commonly associated with acute bacterial skin and skin structure infections. Antimicrobial Agents and Chemotherapy. 56(3):1619-1623.
  19. Speck-Planche, A., Kleandrova, V. V., 2012. In silico design of multi-target inhibitors for C-C chemokine receptors using substructural descriptors. Molecular Diversity. 16(1):183-191.
  20. Speck-Planche, A., Kleandrova, V. V., Cordeiro, M. N. D. S., 2013. New insights toward the discovery of antibacterial agents: Multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. European Journal Pharmaceutical Sciences. 48(4-5):812-818.
  21. Speck-Planche, A., Kleandrova, V. V., Luan, F., Cordeiro, M. N. D. S., 2012. Rational drug design for anticancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorganic and Medicinal Chemistry. 20(15):4848- 4855.
  22. StatSoft, 2001. STATISTICA 6.0. Data analysis software system.
  23. Tenorio-Borroto, E., Penuelas Rivas, C. G., Vasquez Chagoyan, J. C., Castanedo, N., Prado-Prado, F. J., Garcia-Mera, X., Gonzalez-Diaz, H., 2012. ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorganic and Medicinal Chemistry. 20(20):6181- 6194.
  24. Todeschini, R., Consonni, V., 2009. Descriptors for Chemoinformatics, Verlag GmbH & Co. KGaA. Weinheim.
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Paper Citation


in Harvard Style

Speck-Planche A. and Cordeiro M. (2013). Chemoinformatics in Drug Design. Artificial Neural Networks for Simultaneous Prediction of Anti-enterococci Activities and Toxicological Profiles . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 458-465. DOI: 10.5220/0004542004580465


in Bibtex Style

@conference{ncta13,
author={Alejandro Speck-Planche and M. N. D. S. Cordeiro},
title={Chemoinformatics in Drug Design. Artificial Neural Networks for Simultaneous Prediction of Anti-enterococci Activities and Toxicological Profiles},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={458-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004542004580465},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Chemoinformatics in Drug Design. Artificial Neural Networks for Simultaneous Prediction of Anti-enterococci Activities and Toxicological Profiles
SN - 978-989-8565-77-8
AU - Speck-Planche A.
AU - Cordeiro M.
PY - 2013
SP - 458
EP - 465
DO - 10.5220/0004542004580465