Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: An EFSA funded project

Regul Toxicol Pharmacol. 2020 Jul:114:104658. doi: 10.1016/j.yrtph.2020.104658. Epub 2020 Apr 22.

Abstract

To facilitate the practical implementation of the guidance on the residue definition for dietary risk assessment, EFSA has organized an evaluation of applicability of existing in silico models for predicting the genotoxicity of pesticides and their metabolites, including literature survey, application of QSARs and development of Read Across methodologies. This paper summarizes the main results. For the Ames test, all (Q)SAR models generated statistically significant predictions, comparable with the experimental variability of the test. The reliability of the models for other assays/endpoints appears to be still far from optimality. Two new Read Across approaches were evaluated: Read Across was largely successful for predicting the Ames test results, but less for in vitro Chromosomal Aberrations. The worse results for non-Ames endpoints may be attributable to the several revisions of experimental protocols and evaluation criteria of results, that have made the databases qualitatively non-homogeneous and poorly suitable for modeling. Last, Parent/Metabolite structural differences (besides known Structural Alerts) that may, or may not cause changes in the Ames mutagenicity were identified and catalogued. The findings from this work are suitable for being integrated into Weight-of-Evidence and Tiered evaluation schemes. Areas needing further developments are pointed out.

Keywords: (Q)SAR; Dietary risk assessment; Genotoxicity; In silico; Metabolite; Pesticide active substance; Read across; Structural changes.

MeSH terms

  • Chromosome Aberrations / drug effects*
  • Databases, Factual
  • Humans
  • Models, Molecular
  • Molecular Structure
  • Mutagenicity Tests
  • Pesticides / analysis
  • Pesticides / metabolism
  • Pesticides / toxicity*
  • Quantitative Structure-Activity Relationship*
  • Risk Assessment

Substances

  • Pesticides