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Article Dans Une Revue Environmental Science and Pollution Research Année : 2024

Editorial trend: adverse outcome pathway (AOP) and computational strategy — towards new perspectives in ecotoxicology

Damien Baudiffier

Résumé

The adverse outcome pathway (AOP) has been conceptualized in 2010 as an analytical construct to describe a sequential chain of causal links between key events, from a molecular initiating event leading to an adverse outcome (AO), considering several levels of biological organization. An AOP aims to identify and organize available knowledge about toxic effects of chemicals and drugs, either in ecotoxicology or toxicology, and it can be helpful in both basic and applied research and serve as a decision-making tool in support of regulatory risk assessment. The AOP concept has evolved since its introduction, and recent research in toxicology, based on integrative systems biology and artificial intelligence, gave it a new dimension. This innovative in silico strategy can help to decipher mechanisms of action and AOP and offers new perspectives in AOP development. However, to date, this strategy has not yet been applied to ecotoxicology. In this context, the main objective of this short article is to discuss the relevance and feasibility of transferring this strategy to ecotoxicology. One of the challenges to be discussed is the level of organisation that is relevant to address for the AO (population/community). This strategy also offers many advantages that could be fruitful in ecotoxicology and overcome the lack of time, such as the rapid identification of data available at a time t, or the identification of “data gaps”. Finally, this article proposes a step forward with suggested priority topics in ecotoxicology that could benefit from this strategy.
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hal-04296774 , version 1 (20-12-2023)

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Damien Baudiffier, Karine Audouze, Olivier Armant, Sandrine Frelon, Sandrine Charles, et al.. Editorial trend: adverse outcome pathway (AOP) and computational strategy — towards new perspectives in ecotoxicology. Environmental Science and Pollution Research, 2024, ⟨10.1007/s11356-023-30647-w⟩. ⟨hal-04296774⟩
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