The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure–activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency’s National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA’s Chemistry Dashboard and made freely available to the scientific community.

中文翻译：

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大量 LD50 大鼠急性经口毒性数据的 SAR 和 QSAR 建模

啮齿动物口服急性毒性的半数致死剂量 (LD50) 是根据急性接触后对人类健康造成的潜在危害对化学品进行分类所需的标准信息。体内测试的专门使用受到进行实验所需的时间和成本以及需要牺牲大量动物的限制。（定量）构效关系 [(Q)SAR] 被证明是减少和协助评估急性毒理学危害的体内测定的有效替代方法。在一个新的国际合作项目框架内，NTP 替代毒理学方法评估机构间中心和美国环境保护署国家计算毒理学中心编制了一个大鼠急性口服 LD50 数据的大型数据库，旨在支持开发用于预测五个监管相关急性毒性终点的新计算模型。在本文中，通过采用不同的统计和基于知识的方法开发了一系列回归和分类计算模型。进行外部验证是为了证明模型在现实生活中的可预测性。然后应用集成建模来提高单个模型的性能。统计结果证实了所开发模型在监管框架中的相关性，并证实了集成模型的有效性。最佳集成策略的 RMSE 低于 0.50，最佳分类模型的多类别平衡准确度超过 0.70，二元端点平衡准确度超过 0.80。计算预测将托管在 EPA 的化学仪表板上，并免费提供给科学界。