Cronin Mark T D, Enoch Steven J, Mellor Claire L, Przybylak Katarzyna R, Richarz Andrea-Nicole, Madden Judith C
School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England.
Toxicol Res. 2017 Jul;33(3):173-182. doi: 10.5487/TR.2017.33.3.173. Epub 2017 Jul 15.
methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.
预测毒性的方法包括使用（定量）构效关系（（Q）SARs）以及进行分组（类别形成）以便进行类推。建模面临的一个具有挑战性的领域是慢性毒性的预测，尤其是无观察到（不良）效应水平（NO（A）EL）的预测。一种针对慢性毒性预测的建议解决方案是考虑器官水平的效应，而不是对NO（A）EL本身进行建模。本综述重点关注使用结构警报来识别潜在的肝脏毒物。已根据作用机制并结合当前对不良结局途径的了解，开发了特征图谱或结构警报组。这些特征图谱很可靠，可以进行计算编码以实现预测。然而，它们并未涵盖肝脏毒性的所有机制或模式，并给出了改进这些方法的建议。
