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• W2169600586 abstract "Advances in computer sciences and hardware combined with equally significant developments in molecular biology and chemistry are providing toxicology with a powerful new tool box. This tool box of computational models promises to increase the efficiency and the effectiveness by which the hazards and risks of environmental chemicals are determined. Computational toxicology focuses on applying these tools across many scales, including vastly increasing the numbers of chemicals and the types of biological interactions that can be evaluated. In addition, knowledge of toxicity pathways gathered within the tool box will be directly applicable to the study of the biological responses across a range of dose levels, including those more likely to be representative of exposures to the human population. Progress in this field will facilitate the transformative shift called for in the recent report on toxicology in the 21st century by the National Research Council. This review surveys the state of the art in many areas of computational toxicology and points to several hurdles that will be important to overcome as the field moves forward. Proof-of-concept studies need to clearly demonstrate the additional predictive power gained from these tools. More researchers need to become comfortable working with both the data generating tools and the computational modeling capabilities, and regulatory authorities must show a willingness to the embrace new approaches as they gain scientific acceptance. The next few years should witness the early fruits of these efforts, but as the National Research Council indicates, the paradigm shift will take a long term investment and commitment to reach full potential." @default.
• W2169600586 created "2016-06-24" @default.
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• W2169600586 date "2007-12-07" @default.
• W2169600586 modified "2023-10-12" @default.
• W2169600586 title "Computational Toxicology—A State of the Science Mini Review" @default.
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• W2169600586 doi "https://doi.org/10.1093/toxsci/kfm297" @default.
• W2169600586 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18065772" @default.
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