Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow - Ineris - Institut national de l'environnement industriel et des risques Accéder directement au contenu
Article Dans Une Revue Regulatory Toxicology and Pharmacology Année : 2021

Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow

Résumé

Regulatory frameworks require information on acute fish toxicity to ensure environmental protection. The experimental assessment of this property relies on a substantial number of fish to be tested and it is in conflict with the current drive to replace in vivo testing. For this reason, alternatives to in vivo testing have been proposed during the past years. Among these alternatives, there are Quantitative Structure-Activity Relationships (QSAR) that require the sole knowledge of chemical structure to yield predictions of toxicities. In this context, the OECD QSAR Toolbox is one of the leading QSAR tools for regulatory purposes that enables the prediction of fish toxicities. The aim of this work is to provide evidence about the predictive reliability of the automated workflow for predicting acute toxicity in fish which is embedded within this toolbox. The results herein presented show that the logic underpinning this automated workflow can predict with a reliability that, in the majority of cases, is comparable to inter-laboratory variability and, in a significant number of cases, is also comparable with intralaboratory variability. Moreover, considerations on the toxic mode of action provided by the OECD tool proved to be helpful in refining predictions and reducing the number of prediction outliers.
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Dates et versions

ineris-03267330 , version 1 (30-06-2021)

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Enrico Mombelli, Pascal Pandard. Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow. Regulatory Toxicology and Pharmacology, 2021, 122, pp.104893. ⟨10.1016/j.yrtph.2021.104893⟩. ⟨ineris-03267330⟩

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