Evaluation of the predictivity of Acute Oral Toxicity (AOT) structure-activity relationship models

Several public efforts are aimed at discovering patterns or classifiers in the high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. The current study sought to assess and compare the predictions of the Globally Harmonized System (GHS) categories and Dangerous Goods (DG) classifications based on Lethal Dose (LD50) from several available tools (ACD/Labs, Leadscope, T.E.S.T., CATMoS, CaseUltra). External validation was done using dataset of 375 substances to demonstrate their predictive capacity. All models showed very good performance for identifying non-toxic compounds, which would be useful for DG classification, developing or triaging new chemicals, prioritizing existing chemicals for more detailed and rigorous toxicity assessments, and assessing non-active pharmaceutical intermediates. This would ultimately reduce animal use and improve risk assessments. Category-to-category prediction was not optimal, mainly due to the tendency to overpredict the outcome and the general limitations of acute oral toxicity (AOT) in vivo studies. Overprediction does not specifically pose a risk to human health, it can impact transport and material packaging requirements. Performance for compounds with LD50 ≤ 300 mg/kg (approx. 5% of the dataset) was the poorest among all groups and could be potentially improved by including expert review and read-across to similar substances.