MKTox & Co Ltd, 36 Fairford Crescent, Downhead Park, Milton Keynes, Buckinghamshire, MK15 9AQ, UK.
L'Oreal Research & Innovation, 9 Rue Pierre Dreyfus, 92110, Clichy, France.
Regul Toxicol Pharmacol. 2022 Mar;129:105094. doi: 10.1016/j.yrtph.2021.105094. Epub 2022 Jan 4.
This paper presents a 10-step read-across (RAX) framework for use in cases where a threshold of toxicological concern (TTC) approach to cosmetics safety assessment is not possible. RAX builds on established approaches that have existed for more than two decades using chemical properties and in silico toxicology predictions, by further substantiating hypotheses on toxicological similarity of substances, and integrating new approach methodologies (NAM) in the biological and kinetic domains. NAM include new types of data on biological observations from, for example, in vitro assays, toxicogenomics, metabolomics, receptor binding screens and uses physiologically-based kinetic (PBK) modelling to inform about systemic exposure. NAM data can help to substantiate a mode/mechanism of action (MoA), and if similar chemicals can be shown to work by a similar MoA, a next generation risk assessment (NGRA) may be performed with acceptable confidence for a data-poor target substance with no or inadequate safety data, based on RAX approaches using data-rich analogue(s), and taking account of potency or kinetic/dynamic differences.
本文提出了一个 10 步读框(RAX)框架,用于在无法采用毒理学关注阈值(TTC)方法进行化妆品安全评估的情况下。RAX 建立在已有二十多年历史的既定方法基础上,这些方法利用化学性质和计算毒理学预测,进一步证实物质毒理学相似性的假设,并将新的方法学(NAM)整合到生物学和动力学领域。NAM 包括来自例如体外测定、毒理基因组学、代谢组学、受体结合筛选等方面的生物学观察的新型数据,并利用基于生理学的动力学(PBK)建模来提供关于全身暴露的信息。NAM 数据有助于证实作用模式/机制(MoA),如果可以证明类似的化学物质通过类似的 MoA 起作用,则可以在缺乏或缺乏安全性数据的情况下,对数据不足的目标物质进行下一代风险评估(NGRA),这是基于使用数据丰富的类似物的 RAX 方法,并考虑到效力或动力学/动态差异。