Review of the state of science and evaluation of currently available in silico prediction models for reproductive and developmental toxicity: A case study on pesticides. Issue 14 (24th June 2022)

• Record Type: Journal Article
• Authors: Weyrich, Anastasia; Joel, Madeleine; Lewin, Geertje; Hofmann, Thomas; Frericks, Markus
• Is Part Of: Birth defects research. Volume 114:Issue 14(2022)
• Page Start: 812
• Page End: 842
• Publication Date: 2022-06-24
• Subjects: in silico predictions -- in silico protocols -- pesticide -- QSAR -- reproductive toxicology; Teratology -- Periodicals; Abnormalities, Human -- Periodicals; Congenital Abnormalities; Embryo, Mammalian -- abnormalities; Teratology; Abnormalities, Human; Teratology; Periodicals; Periodicals; 616.043
• DOI: 10.1002/bdr2.2062
• Languages: English
• ISSNs: 2472-1727

Abstract

Abstract: Background: In silico methods for toxicity prediction have increased significantly in recent years due to the 3Rs principle. This also applies to predicting reproductive toxicology, which is one of the most critical factors in pesticide approval. The widely used quantitative structure–activity relationship (QSAR) models use experimental toxicity data to create a model that relates experimentally observed toxicity to molecular structures to predict toxicity. Aim of the study was to evaluate the available prediction models for developmental and reproductive toxicity regarding their strengths and weaknesses in a pesticide database. Methods: The reproductive toxicity of 315 pesticides, which have a GHS classification by ECHA, was compared with the prediction of different in silico models: VEGA, OECD (Q)SAR Toolbox, Leadscope Model Applier, and CASE Ultra by MultiCASE. Results: In all models, a large proportion (up to 77%) of all pesticides were outside the chemical space of the model. Analysis of the prediction of remaining pesticides revealed a balanced accuracy of the models between 0.48 and 0.66. Conclusion: Overall, predictions were only meaningful in rare cases and therefore always require evaluation by an expert. The critical factors were the underlying data and determination of molecular similarity, which offer great potential for improvement.