Abstract
As the pace of pharmaceutical drug discovery quickens and greater numbers of preclinical candidates are identified using combinatorial and other high throughput methods, the demand on safety assessment assays increases. As most in vitro toxicology assays are, at best, medium throughput, it is readily apparent that rapid in silico assessment protocols must be developed and validated for their use in the early discovery phase. No strangers to the increased demand for accurate safety assessments of candidate compounds and the additional constraints imposed by limited resources, regulatory agencies have long been at the forefront of utilizing and championing computational methods. As regulatory databases of safety information are populated and legacy data incorporated, methods to utilize this data to extract meaningful information must be developed and validated. As this is not intended to be an exhaustive review of all in silico tools for toxicology assessment, the reader is referred to a number of recent articles which do an outstanding job of summarizing the algorithms, benefits and shortcomings of many of the commercial packages available (Pearl, Livingston-Carr et al. 2001; Greene 2002; Snyder, Pearl et al. 2004).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agresti, A. (1996). An Introduction to Categorical Data Analysis. New York, Chichester, Brisbane, Toronto and Singapore, John Wiley & Sons, Inc.: 246–249.
Benigni, R., L. Passerini, et al. (1998). “QSAR models for discriminating between mutagenic and nonmutagenic aromatic and heteroaromatic amines.” Environ Mol Mutagen 32(1): 75–83.
Colvin, M. E., F. T. Hatch, et al. (1998). “Chemical and biological factors affecting mutagen potency.” Mutat Res 400(1–2): 479–92.
Corey, E. J., A. K. Long, et al. (1985). “Computer-assisted analysis in organic synthesis.” Science 228(4698): 408–18.
Greene, N. (2002). “Computer systems for the prediction of toxicity: an update.” Adv Drug Deliv Rev 54(3): 417–31.
Greene, N., P. N. Judson, et al. (1999). “Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR.” SAR QSAR Environ Res 10(2–3): 299–314.
He, L., P. C. Jurs, et al. (2003). “Predicting the genotoxicity of polycyclic aromatic compounds from molecular structure with different classifiers.” Chem Res Toxicol 16(12): 1567–80.
Hofnung, M. and P. Quillardet (1988). “The SOS Chromotest, a colorimetric assay based on the primary cellular responses to genotoxic agents.” Ann N Y Acad Sci 534: 817–25.
Judson, P. N., C. A. Marchant, et al. (2003). “Using argumentation for absolute reasoning about the potential toxicity of chemicals.” J Chem Inf Comput Sci 43(5): 1364–70.
Jurs, P. C., M. N. Hasan, et al. (1983). “Computer-assisted studies of molecular structure and carcinogenic activity.” Fundam Appl Toxicol 3(5): 343–9.
Kier, L. B. and L. H. Hall (1990). “An electrotopological-state index for atoms in molecules.” Pharm Res 7(8): 801–7.
Klopman, G. and O. T. Macina (1985). “Use of the Computer Automated Structure Evaluation program in determining quantitative structure-activity relationships within hallucinogenic phenylalkylamines.” J Theor Biol 113(4): 637–48.
Lai, D. Y., Y.-t. Woo, et al. (1996). “Carcinogenic Potential of Organic Peroxides: Prediction Based on Structure-Activity Relationships (SAR) and Mechanism-Based Short Term Tests.” Journal of Environmental Science and Health, Part C—Environmental Carcinogenesis & Ecotoxicology Reviews.
Lipinski, C. A., F. Lombardo, et al. (2001). “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.” Adv Drug Deliv Rev 46(1–3): 3–26.
Mattioni, B. E., G. W. Kauffman, et al. (2003). “Predicting the genotoxicity of secondary and aromatic amines using data subsetting to generate a model ensemble.” J Chem Inf Comput Sci 43(3): 949–63.
Mosier, P. D., P. C. Jurs, et al. (2003). “Predicting the genotoxicity of thiophene derivatives from molecular structure.” Chem Res Toxicol 16(6): 721–32.
Pearl, G. M., S. Livingston-Carr, et al. (2001). “Integration of computational analysis as a sentinel tool in toxicological assessments.” Curr Top Med Chem 1(4): 247–55.
Quillardet, P. and M. Hofnung (1993). “The SOS chromotest: a review.” Mutat Res 297(3): 235–79.
Snyder, R. D., G. S. Pearl, et al. (2004). “Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical molecules.” Environ Mol Mutagen 43(3): 143–58.
Sutton, M. D., B. T. Smith, et al. (2000). “The SOS response: recent insights into umuDC-dependent mutagenesis and DNA damage tolerance.” Annu Rev Genet 34: 479–497.
Trieff, N. M., G. L. Biagi, et al. (1989). “Aromatic amines and acetamides in Salmonella typhimurium TA98 and TA100: a quantitative structure-activity relation study.” Mol Toxicol 2(1): 53–65.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 American Association of Pharmaceutical Scientists
About this chapter
Cite this chapter
Kreatsoulas, C., Durham, S.K., Custer, L.L., Pearl, G.M. (2006). Elementary Predictive Toxicology for Advanced Applications. In: Borchardt, R.T., Kerns, E.H., Hageman, M.J., Thakker, D.R., Stevens, J.L. (eds) Optimizing the “Drug-Like” Properties of Leads in Drug Discovery. Biotechnology: Pharmaceutical Aspects, vol IV. Springer, New York, NY. https://doi.org/10.1007/978-0-387-44961-6_14
Download citation
DOI: https://doi.org/10.1007/978-0-387-44961-6_14
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-34056-2
Online ISBN: 978-0-387-44961-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)
