Applicability of QSAR analysis in the evaluation of developmental and neurotoxicity effects for the assessment of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment
Meta data
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Abstract
This is the final report of a project carried out by the European Commission’s Joint Research and sponsored by the European Food Safety Authority with the overall aim of evaluating the potential applicability of computational methods for predicting adverse developmental and neurotoxicity effects in the dietary risk assessment of pesticides. While the toxicological profile of the parent active substance is fully characterised through the experimental studies required by EU legislation, only very limited toxicological data are usually available for their metabolites and degradates. For reasons of efficiency and animal welfare, computational methods based on structure-activity analysis and read-across are being investigated for their applicability in assessing the toxicological relevance of metabolites and degradates of pesticide active substances. The ability to reliably predict the presence and absence of short-term effects of concern, and in particular developmental toxicity and neurotoxicity, would have a positive impact on the way pesticide risk assessments are currently carried out by reducing the need for toxicity testing on metabolites and degradates as well as the need to conduct short-term exposure assessments. In this study, the ability of selected Quantitative Structure-Activity Relationship (QSAR) tools to predict developmental and neurotoxicity was analysed, and a stepwise approach based on the use of QSAR analysis and read-across was proposed as possible way of supporting, alongside other non-testing approaches such as the Threshold of Toxicological Concern (TTC) approach, the assessment of pesticide metabolites and degradates in terms of their toxicological relevance. In this stepwise approach, QSAR tools are used in a preliminary step to identify toxic chemicals, while read across is applied, in cases where a chemical is predicted by QSAR to be non-toxic, as a means of distinguishing between true and false negatives. This approach is shown to improve the overall ability to distinguish between toxic and non-toxic chemicals compared with the use of individual tools.