Toxicity Prediction using 2D Pharmacophores and Support Vector Machines
Abstract
In silico methods have been largely used in drug development process to predict the toxicity of molecules. Predicting the toxicity is one of the most important stage in developing new pharmaceuticals and computational methods are being used in order to make this process less time-consuming and decrease its high cost. Here we report a new approach, using two-dimensional pharmacophore fingerprint to encode pharmacophoric features of molecules in string sets, which are then processed by support vector machines (SVM) to predict the toxicity endpoint of a carcinogenic data set with 1547 compounds. Previous studies have shown the use of machine learning approaches in predicting the toxicity of molecules, however, in those cases it was required to calculate a large number of molecular descriptors to be able to make such prediction. Using SVM and only one molecular descriptor it was possible to achieve a satisfactory accuracy rate compared to other machine learning approaches.References
Amini, A., Muggleton, S., Lodhi, H., and Sternberg, M. (2007). A novel logic-based approach for quantitative toxicology prediction. J. Chem. Inf. Model., 47(3):998–1006.
Benfenati, E. (2007). Predicting toxicity through computers: A changing world. Chemical Central Journal, 32(1).
Bonachera, F., Parent, B., Barbosa, F., Froloff, N., and Horvath, D. (2006). Fuzzy tricentric pharmacophore fingerprints. Journal of Chemical Information and Modeling, 6(46):2457–2477.
Cronin, M. (2001). Prediction of drug toxicity. Il Farmaco, pages 149–151.
Dearden, J. (2003). In silico prediction of drug toxicity. Journal of computer-aided molecular design, 17(2-4):119–127.
Egan, W., Zlokarnik, G., and Grootenhuis, P. (2004). In silico prediction of drug safety: despite progress there is abundant room for improvement. Drug Discovery Today: Technologies, 1(4):381–387.
Ekins, S. (2003). In silico approaches to predicting drug metabolism, toxicology and beyond. Biochemical Society Transactions, 31(3):611–614.
Ekins, S. (2007). Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals (Wiley Series on Technologies for the Pharmaceutical Industry). Wiley-Interscience.
Enslein, K., Gombar, V., and Blake, B. (1994). Use of sar in computer-assisted prediction of carcinogenicity and mutagenicity of chemicals by the topkat program. Mutat. Res., (305):47–61.
Gold, L., Manley, N., Slone, T., and Ward, J. (2001). Compendium of chemical carcinogens by target organ: Results of chronic bioassays in rats, mice, hamsters, dogs, and monkeys. Toxicologic Pathology, 29(6):639–652(14).
Hansch, C., Maloney, P., Fujita, T., and Muir, R. (1962). Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature, (194):178–180.
Jiang, Z., Yamauchi, K., Yoshioka, K., Aoki, K., Kuroyanagi, S., Iwata, A., Yang, J., and Wang, K. (2006). Support vector machine-based feature selection for classification of liver fibrosis grande in chronic hepatitis c. J. Med. Syst., (30):389–394.
Kavlock, R., Ankley, G., Blancato, J., Breen, M., Conolly, R., Dix, D., Houck, K., Hubal, E., Judson, R., Rabinowitz, J., Richard, A., Setzer, R., Shah, I., Villeneuve, D., and Weber, E. (2008). Computational toxicology - a state of the science mini review. Toxicological Sciences, 103(1):14–27.
Kazius, J., Mcguire, R., and Bursi, R. (2005). Derivation and validation of toxicophores for mutagenicity prediction. J. Med. Chem., 48(1):312–320.
Kruhlak, N., Contrera, J., Benz, R., and Matthews, E. (2007). Progress in qsar toxicity screening of pharmaceutical impurities and other fda regulated products. Advanced Drug Delivery Reviews, 59:43–55.
McGregor, M. and Muskal, S. (1999). Pharmacophore fingerprinting: Application to qsar and focused library design. J. Chem. Inf. Comput. Sci., 39(3):569–574.
Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Müller, L., and Pähler, A. (2008). Computational toxicology in drug development. Drug Discovery Today, 8(7).
Neagu, D., Craciun, M., Stroia, S., and Bumbaru, S. (2005). Hybrid intelligent systems for predictive toxicology - a distributed approach. Intelligent Systems Design and Applications, International Conference on, pages 26–31.
Rabinowitz, J., Goldsmith, M., Little, S., and Pasquinelli, M. (2008). Computational molecular modeling for evaluating the toxicity of environmental chemicals: Prioritizing bioassay requirements. Environmental Health Perspectives, 116(5):573–577.
Richard, A. and Williams, C. (2002). Distributed structure-searchable toxicity (dsstox) public database network: a proposal. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 499:27–52(26).
Schachter, A. and Ramoni, M. (2007). Clinical forecasting in drug development. Nature Reviews, 6:107–108.
Suresh, P. and Basu, P. (2008). Improving pharmaceutical product development and manufacturing: Impact on cost of drug development and cost of goods sold of pharmaceuticals. Journal of Pharmaceutical Innovation, 3(3):175–187.
Tiwari, A., Knowles, J., Avineri, E., Dahal, K., and Roy, R., editors (2006). Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development, Advances in Soft Computing. Springer.
Todeschini, R., Consonni, V., Mannhold, R., Kubinyi, H., and Timmerman, H. (2000). Handbook of Molecular Descriptors. Wiley-VCH.
Vapnik, V. and Cortes, C. (1995). Support-vectors networks. Machine Learning, 20:273–297.
Varin, T., Saettel, N., Villain, J., Lesnard, A., Dauphin, F., Bureau, R., and Rault, S. (2008). 3d pharmacophore, hierarchical methods, and 5-ht4 receptor binding data. Journal of Enzyme Inhibtion and Medicinal Chemistry, 23(5):593–603.
White, A., Mueller, R., Gallavan, R., Aaron, S., and Wilson, A. (2003). A multiple in silico program approach for the prediction of mutagenicity from chemical structure. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 539:77–89(13).
Yap, C., Xue, Y., Li, Z., and Chen, Y. (2006). Application of support vector machines to in Silico prediction of cytochrome p450 enzyme substrates and inhibitors. Current Topics in Medicinal Chemistry, (6):1593–1607.
Zhao, C., Zhang, H., Zhang, X., Liu, M., Hu, Z., and Fan, B. (2006). Application of support vector machine (svm) for prediction toxic activity of different data sets. Toxicology, (217):105–119.
Benfenati, E. (2007). Predicting toxicity through computers: A changing world. Chemical Central Journal, 32(1).
Bonachera, F., Parent, B., Barbosa, F., Froloff, N., and Horvath, D. (2006). Fuzzy tricentric pharmacophore fingerprints. Journal of Chemical Information and Modeling, 6(46):2457–2477.
Cronin, M. (2001). Prediction of drug toxicity. Il Farmaco, pages 149–151.
Dearden, J. (2003). In silico prediction of drug toxicity. Journal of computer-aided molecular design, 17(2-4):119–127.
Egan, W., Zlokarnik, G., and Grootenhuis, P. (2004). In silico prediction of drug safety: despite progress there is abundant room for improvement. Drug Discovery Today: Technologies, 1(4):381–387.
Ekins, S. (2003). In silico approaches to predicting drug metabolism, toxicology and beyond. Biochemical Society Transactions, 31(3):611–614.
Ekins, S. (2007). Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals (Wiley Series on Technologies for the Pharmaceutical Industry). Wiley-Interscience.
Enslein, K., Gombar, V., and Blake, B. (1994). Use of sar in computer-assisted prediction of carcinogenicity and mutagenicity of chemicals by the topkat program. Mutat. Res., (305):47–61.
Gold, L., Manley, N., Slone, T., and Ward, J. (2001). Compendium of chemical carcinogens by target organ: Results of chronic bioassays in rats, mice, hamsters, dogs, and monkeys. Toxicologic Pathology, 29(6):639–652(14).
Hansch, C., Maloney, P., Fujita, T., and Muir, R. (1962). Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature, (194):178–180.
Jiang, Z., Yamauchi, K., Yoshioka, K., Aoki, K., Kuroyanagi, S., Iwata, A., Yang, J., and Wang, K. (2006). Support vector machine-based feature selection for classification of liver fibrosis grande in chronic hepatitis c. J. Med. Syst., (30):389–394.
Kavlock, R., Ankley, G., Blancato, J., Breen, M., Conolly, R., Dix, D., Houck, K., Hubal, E., Judson, R., Rabinowitz, J., Richard, A., Setzer, R., Shah, I., Villeneuve, D., and Weber, E. (2008). Computational toxicology - a state of the science mini review. Toxicological Sciences, 103(1):14–27.
Kazius, J., Mcguire, R., and Bursi, R. (2005). Derivation and validation of toxicophores for mutagenicity prediction. J. Med. Chem., 48(1):312–320.
Kruhlak, N., Contrera, J., Benz, R., and Matthews, E. (2007). Progress in qsar toxicity screening of pharmaceutical impurities and other fda regulated products. Advanced Drug Delivery Reviews, 59:43–55.
McGregor, M. and Muskal, S. (1999). Pharmacophore fingerprinting: Application to qsar and focused library design. J. Chem. Inf. Comput. Sci., 39(3):569–574.
Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Müller, L., and Pähler, A. (2008). Computational toxicology in drug development. Drug Discovery Today, 8(7).
Neagu, D., Craciun, M., Stroia, S., and Bumbaru, S. (2005). Hybrid intelligent systems for predictive toxicology - a distributed approach. Intelligent Systems Design and Applications, International Conference on, pages 26–31.
Rabinowitz, J., Goldsmith, M., Little, S., and Pasquinelli, M. (2008). Computational molecular modeling for evaluating the toxicity of environmental chemicals: Prioritizing bioassay requirements. Environmental Health Perspectives, 116(5):573–577.
Richard, A. and Williams, C. (2002). Distributed structure-searchable toxicity (dsstox) public database network: a proposal. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 499:27–52(26).
Schachter, A. and Ramoni, M. (2007). Clinical forecasting in drug development. Nature Reviews, 6:107–108.
Suresh, P. and Basu, P. (2008). Improving pharmaceutical product development and manufacturing: Impact on cost of drug development and cost of goods sold of pharmaceuticals. Journal of Pharmaceutical Innovation, 3(3):175–187.
Tiwari, A., Knowles, J., Avineri, E., Dahal, K., and Roy, R., editors (2006). Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development, Advances in Soft Computing. Springer.
Todeschini, R., Consonni, V., Mannhold, R., Kubinyi, H., and Timmerman, H. (2000). Handbook of Molecular Descriptors. Wiley-VCH.
Vapnik, V. and Cortes, C. (1995). Support-vectors networks. Machine Learning, 20:273–297.
Varin, T., Saettel, N., Villain, J., Lesnard, A., Dauphin, F., Bureau, R., and Rault, S. (2008). 3d pharmacophore, hierarchical methods, and 5-ht4 receptor binding data. Journal of Enzyme Inhibtion and Medicinal Chemistry, 23(5):593–603.
White, A., Mueller, R., Gallavan, R., Aaron, S., and Wilson, A. (2003). A multiple in silico program approach for the prediction of mutagenicity from chemical structure. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 539:77–89(13).
Yap, C., Xue, Y., Li, Z., and Chen, Y. (2006). Application of support vector machines to in Silico prediction of cytochrome p450 enzyme substrates and inhibitors. Current Topics in Medicinal Chemistry, (6):1593–1607.
Zhao, C., Zhang, H., Zhang, X., Liu, M., Hu, Z., and Fan, B. (2006). Application of support vector machine (svm) for prediction toxic activity of different data sets. Toxicology, (217):105–119.
Published
2009-07-20
How to Cite
PEREIRA, Max; SCHMITZ, Ademar.
Toxicity Prediction using 2D Pharmacophores and Support Vector Machines. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 9. , 2009, Bento Gonçalves/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2009
.
p. 1987-1995.
ISSN 2763-8952.
