Molecular Nutrition Branch, National Engineering Research Center of Rice and By-Product Deep Processing, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China.
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, PR China.
Food Chem Toxicol. 2022 Oct;168:113325. doi: 10.1016/j.fct.2022.113325. Epub 2022 Aug 10.
No observed adverse effect level (NOAEL) is an identified dose level which used as a point of departure to infer a safe exposure limit of chemicals, especially in food additives and cosmetics. Recently, in silico approaches have been employed as effective alternatives to determine the toxicity endpoints of chemicals instead of animal experiments. Several acceptable models have been reported, yet assessing the risk of repeated-dose toxicity remains inadequate. This study established robust machine learning predictive models for NOAEL at different exposure durations by constructing high-quality datasets and comparing different kinds of molecular representations and algorithms. The features of molecular structures affecting NOAEL were explored using advanced cheminformatics methods, and predictive models also communicated the NOAEL between different species and exposure durations. In addition, a NOAEL prediction tool for chemical risk assessment is provided (available at: https://github.com/ifyoungnet/NOAEL). We hope this study will help researchers easily screen and evaluate the subacute and sub-chronic toxicity of disparate compounds in the development of food additives in the future.
未观察到不良作用水平 (NOAEL) 是一种已确定的剂量水平，可用于推断化学品的安全暴露限值，特别是在食品添加剂和化妆品中。最近，计算方法已被用作确定化学品毒性终点的有效替代方法，而不是动物实验。已经报道了几种可接受的模型，但评估重复剂量毒性的风险仍然不足。本研究通过构建高质量数据集并比较不同类型的分子表示和算法，为不同暴露持续时间的 NOAEL 建立了稳健的机器学习预测模型。使用先进的化学信息学方法探索了影响 NOAEL 的分子结构特征，预测模型还沟通了不同物种和暴露持续时间之间的 NOAEL。此外，还提供了用于化学风险评估的 NOAEL 预测工具（可在：https://github.com/ifyoungnet/NOAEL）。我们希望本研究将有助于研究人员在未来开发食品添加剂时轻松筛选和评估不同化合物的亚急性和亚慢性毒性。
