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How Adverse Outcome Pathways can support computational approaches to predictive toxicology

How Adverse Outcome Pathways can support computational approaches to predictive toxicology How Adverse Outcome Pathways can support computational approaches to predictive toxicology
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Feb 03 2017

Scientists from the JRC and eleven collaborating organisations from the EU, Canada and the USA have shared their collective insights on how adverse outcome pathways (AOP) can aid the development and use of computational prediction models for regulatory toxicology.

The scientists contend that systematically capturing knowledge as AOPs can efficiently inform and help direct the design and development of computational prediction models that can further enhance the utility of data derived from in vitro and in silico methods for toxicological hazard assessment.

The AOP framework provides a systematic approach for managing mechanistic knowledge of toxicological processes associated with potential effects of chemicals on human health and the environment. Computational models of biological systems at various scales provide a means to exploit such understanding to facilitate inference, extrapolation and prediction.

The JRC has also partnered with the OECD and the US Environmental Protection Agency to develop the publically accessible AOP Knowledge Base. This key resource facilitates the scientific crowdsourcing and on-line peer review foreseen in the AOP development process and allows an AOP to continue to evolve as understanding of the underlying science grows. The published article will help the research communities of both predictive toxicology and computational modelling to find common ground, identify synergies and exploit AOP knowledge to advance safety assessment science.

Read more in: C. Wittwehr et al.: "How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology", Toxicol. Sci. (2016) 155 (2), 326-336,  ISSN 1096-6080,  doi:10.1093/toxsci/kfw207