(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?. Issue 10 (23rd April 2020)

• Record Type: Journal Article
• Authors: Herrmann, Kristin; Holzwarth, Andrea; Rime, Soyub; Fischer, Benjamin C; Kneuer, Carsten
• Is Part Of: Pest management science. Volume 76:Issue 10(2020)
• Page Start: 3316
• Page End: 3325
• Publication Date: 2020-04-23
• Subjects: (4–6) -- pesticides -- in silico -- QSAR -- mutagenicity -- Ames test; Pests -- Control -- Periodicals; Pesticides -- Periodicals; 632.9
• DOI: 10.1002/ps.5828
• Languages: English
• ISSNs: 1526-498X

Abstract

Abstract: The assessment of human health risks resulting from the presence of metabolites in groundwater and food residues has become an important element in pesticide authorisation. In this context, the evaluation of mutagenicity is of particular interest and a paradigm shift from exposure‐triggered testing to in silico ‐based screening has been recommended in the European Food Safety Authority (EFSA) Guidance on the establishment of the residue definition for dietary risk assessment . In addition, it is proposed to apply in silico predictions when experimental mutagenicity testing is not possible due to a lack of sufficient quantities of the pesticide metabolite. This, combined with animal welfare and economic considerations, has led to a situation where an increasing number of in silico studies are submitted to regulatory authorities. Whilst there is extensive experience with in silico predictions for mutagenicity in the chemical and pharmaceutical industry, their suitability in pesticide regulation is still insufficiently considered. Therefore, we herein discuss critical issues that need to be resolved to successfully implement (Quantitative) Structure‐Activity Relationship ((Q)SAR) as an accepted tool in pesticide regulation. For illustration purposes, the results of a pilot study are included. The presented study highlights a need for further improvement regarding the predictivity and applicability domain of (Q)SAR systems for pesticides and their metabolites, but also raises other questions such as model selection, establishment of acceptance criteria, harmonised approaches to the combination of model outputs into overall conclusions, adequate reporting and data sharing. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Abstract : Results from a study on the predictive performance of (Q)SAR systems for the mutagenicity of pesticides, their impurities or metabolites are presented, highlighting subsequent questions for further improvement.