A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling
This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multip...
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Veröffentlicht in: | Regulatory toxicology and pharmacology 2021-03, Vol.120, p.104843-104843, Article 104843 |
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Sprache: | eng |
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Zusammenfassung: | This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models’ performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed. Across all compounds which could be assigned to a well-defined experimental category, the average percentage of correct or more conservative predictions was around 80%. These results indicate the potential for reliable and broad application of these models across different industrial sectors. This manuscript describes the evaluation of these models, highlights the importance of an expert review, and provides guidance on the use of AOT models to fulfill testing requirements, GHS classification/labelling, and transportation needs.
•Summarizes the use of an acute oral toxicity test for classification and labelling.•Highlights how an alternative method would support the 3Rs•Presents the performance result for AOT in silico models against over 2000 proprietary and marketed chemicals.•Discusses factors to consider as part of an expert review.•Outlines a workflow to incorporate in silico models to support GHS classification and labelling. |
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ISSN: | 0273-2300 1096-0295 1096-0295 |
DOI: | 10.1016/j.yrtph.2020.104843 |