Computational toxicology: a tool for all industries

被引：11
作者：
Marchant, Carol A. [1 ]
机构：
[1] Lhasa Ltd, Leeds LS2 9HD, W Yorkshire, England
来源：
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE | 2012年 / 2卷 / 03期
关键词：
D O I：
10.1002/wcms.100
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Statistical, expert system, and machine learning methods among others have been used to develop in silico tools for the prediction of toxicological hazard from chemical structure. The models are being applied to the mammalian and environmental toxicological assessment of chemicals across a range of industries including cosmetics, foods, industrial chemicals, and pharmaceuticals. Their use within a regulatory environment has also been encouraged by recent legislation. Generally, the models address the potential toxicity of low to medium molecular weight organic chemicals but models for other chemical types such as proteins and nanoparticles have also received some attention. (c) 2011 John Wiley & Sons, Ltd.
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收藏
页码：424 / 434
页数：11
相关论文
共 54 条
[1]  
Kavlock R., Dix D., Computational toxicology as implemented by the U.S. EPA: providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk, J Toxicol Environ Health B Crit Rev, 13, pp. 197-217, (2010)
[2]  
Mostrag-Szlichtyng A., Zaldivar Comenges J.M., Worth A.P., Computational toxicology at the European Commission's Joint Research Centre, Expert Opin Drug MetabToxicol, 6, pp. 785-792, (2010)
[3]  
Ohe P.C., Kuhne R., Ebert R.U., Altenburger R., Liess M., Schuurmann G., Structural alerts-a new classification model to discriminate excess toxicity from narcotic effect levels of organic compounds in the acute daphnid assay, Chem Res Toxicol, 18, pp. 536-555, (2005)
[4]  
Verhaar H.J.M., van Leeuwen C.J., Hermens J.L.M., Classifying environmental pollutants, Chemosphere, 25, pp. 471-491, (1992)
[5]  
Patlewicz G.Y., Wright Z.M., Basketter D.A., Pease C.K., Lepoittevin J.P., Arnau E.G., Structure-activity relationships for selected fragrance allergens, Contact Dermatitis, 47, pp. 219-226, (2002)
[6]  
Obiol-Pardo C., Gomis-Tena J., Sanz F., Saiz J., Pastor M., A multiscale simulation system for the prediction of drug-induced cardiotoxicity, J Chem Inf Model, 51, pp. 483-492, (2011)
[7]  
Taboureau O., Jorgensen F.S., In silico predictions of hERG channel blockers in drug discovery: from ligand-based and target-based approaches to systems chemical biology, Comb Chem High Throughput Screen, 14, pp. 375-387, (2011)
[8]  
Stoll F., Goller A.H., Hillisch A., Utility of protein structures in overcoming ADMET-related issues of drug-like compounds, Drug Discov Today, 16, pp. 530-538, (2011)
[9]  
Kortagere S., Krasowski M.D., Reschly E.J., Venkatesh M., Mani S., Ekins S., Evaluation of computational docking to identify pregnane X receptor agonists in the ToxCast database, Environ Health Perspect, 118, pp. 1412-1417, (2010)
[10]  
Judson R.S., Houck K.A., Kavlock R.J., Knudsen T.B., Martin M.T., Mortensen H.M., Reif D.M., Rotroff D.M., Shah I., Richard A.M., Et al., In vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project, Environ Health Perspect, 118, pp. 485-492, (2010)
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