Toxicology predictive

In the area of predictive toxicology the applicability domain is taken to express the scope and limitations of a model, that is, the range of chemical structures for which the model is considered to be applicable [106]. Although this issue has been fundamental to the use of QSAR (and indeed any predictive technique) since its conception, there remain few reliable methods to define and apply an applicability domain in predictive toxicology. The current status of methods to define the applicability domain for use in (Q)SAR has been assessed recently by Netzeva et al. [106]. 

There is currently debate on the best methods to define the applicability domain for a model in predictive toxicology. The ultimate solution is likely to be lacking for a number of years. However, there are some initiatives that are beginning to address the issue of applicability domain, which include the use of statistical measures and also mechanistic appreciation. 

This approach is intuitively appealing to most users, because it promises easy access to toxicological knowledge, and some of the most used predictive toxicology software tools are in fact Expert Systems [e.g., Derek Nexus (https //www. lhasalimited.org/) and Toxtree (http //toxtree.sourceforge.net/)]. 

Helma C (2005) In silico predictive toxicology the state-of-the-art and strategies to predict human health effects. Curr Opin Drug Discov Devel 8(1) 27—31... 

Politzer, P., P. R. Laurence, and K. Jayasuriya. 1985. Structure-Activity Correlation in Mechanism Studies and Prediction Toxicology. Env. Health Persp. 61, 191. 

Lu T et al. Application of cDNA microarray to the study of arsenic-induced liver diseases in the population of Guizhou, China. Toxicol Sci 2001 59 185-192. Bristol DW et al. The NIEHS Predictive Toxicology Evaluation Project. Environ Health Persp 1996 104(Suppl. 5] 1001-1010. 

Fielden, M.R. and Zacharewski, T.R., Challenges and limitations of gene expression profiling in mechanistic and predictive toxicology, Toxicol. Sci., 60, 6-10, 2000. 

Source Predictive Toxicology—Programs, http //predictive-toxicology.com/programs.html... 

Feng, J., Lurati, L., Ouyang, H., Robinson, T., Wang, Y., Yuan, S., and Young, S.S. Predictive toxicology benchmarking molecular descriptors and statistical methods./. Chem. Inf. Comput. Sci. 2003, 43, 1463-1470. 

Suter L, Babiss LE, Wheeldon EB. Toxicogenom-ics in predictive toxicology in drug development. Chem Biol 2004 11 161-71. 

Cytotoxicity Evaluated for confounding interpretation of in vitro efficacy assays, for predicting potential for human toxicity especially in liver but also if warranted by other safety assessments in bone marrow, kidney, neurons, immu-nocytes and so on. Also used for developing understanding of biochemical mechanisms of toxicity. HCA has been repeatedly demonstrated to be an effective tool in predictive toxicology. May also be used for certain translational safety biomarkers of toxicity [37]... 

High Effectiveness of An HCA Cell Model in Predictive Toxicolog/ 337... 

Pohjala, L, Tammela, P., Samanta, S.K, Yli-Kauhaluoma, J. and Vuorela, P. (2007) Assessing the data quality in predictive toxicology using a panel of cell lines and cytotoxicity assays. Analytical Biochemistry, 362, 221-228. 

Key words Predictive toxicology. Predictive models. High-throughput screening, ToxCast... 

Knudsen TB, Kleinstreuer NC (2012) Disruption of embryonic vascular development in predictive toxicology. Birth Defects Res C Embryo Today 93 312-323... 

Some of the techniques described herein were developed more than 60 years ago (e.g., alizarin staining of the fetal skeleton), others are just gaining acceptance (e.g., micro CT), while some have still to reach infancy (e.g., informatics-based predictive toxicology). 

An example of another approach is DEREK, a pnblicly available expert system designed to assist chemists and toxicologists in predicting toxicological hazards based on analysis of chemical strnctnre (see table 9.1). DEREK differs from other compnter methods for toxicity prediction in that it makes qnalitative rather than qnantitative predictions and does not rely on algebraic or statistical relationships. 

A variety of different approaches to the prediction of toxicity have been developed under the sponsorship of the Predictive Toxicology Evalnation project of the National Institnte of Environmental Health Sciences. The widespread application of compnta-tional techniqnes to stndies in biology, chemistry, and environmental sciences has led to a qnest for important, characteristic molecnlar parameters that may be directly derived from these compntational methods. Theoretical linear solvation energy relationships combine compntational molecular orbital parameters with the linear solvation energy relationship of Kamlet and Taft to characterize, nnderstand, and predict biological, chemical, and physical properties of chemical componnds (Eamini and Wilson, 1997). 

Chapter 20. Parish, W. E. Immunological tests to predict toxicological hazards to man. Chapter 21. Venitt, S. Microbial tests in carcinogenesis studies. 

G.C. Gini, A.R. Katritzky (Eds), Predictive Toxicology of Chemicals Experiences and Impact of AI Tools. AAAI 1999 Spring Symposium Series. (AAAI Press, Menlo Park, 1999). 

Yang RSH, Thomas RS, Gustafson DL, Campain J, Benjamin SA, Verhaar HJM, Mumtaz MM. 1998. Approaches to developing alternative and predictive toxicology based on PBPK/PD and (Q)SAR modeling. Environ Hlth Perspect 106 1385-1393. 

Table 1.1 Summary of the Key Historical Events (Scientific and Sociological) That Have Given Rise to the Modern Science of Predictive Toxicology... 

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