School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.
School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.
Regul Toxicol Pharmacol. 2021 Mar;120:104855. doi: 10.1016/j.yrtph.2020.104855. Epub 2020 Dec 30.
A group of triazole compounds was selected to investigate the confidence that may be associated with read-across of a complex data gap: repeated dose toxicity. The read-across was evaluated using Assessment Elements (AEs) from the European Chemicals Agency's (ECHA's) Read-Across Assessment Framework (RAAF), alongside appraisal of associated uncertainties. Following an initial read-across based on chemical structure and properties, uncertainties were reduced by the integration of data streams such as those from New Approach Methodologies (NAM) and other existing data. In addition, addressing the findings of the ECHA RAAF framework, complemented with specific questions concerning uncertainties, increased the confidence that can be placed in read-across. Although a data rich group of compounds with a strong mechanistic basis was analysed, it was clearly demonstrated that NAM data available from publicly available resources could be applied to support read-across. It is acknowledged that most read-across studies will not be so data rich or mechanistically robust, therefore some targeted experimentation may be required to fill the data gaps. In this sense, NAMs should constitute new experimental tests performed with the specific goal of reducing the uncertainties and demonstrating the read-across hypothesis.
选择了一组三唑类化合物来研究可能与重复剂量毒性这一复杂数据缺口的关联性推断相关的置信度。使用欧洲化学品管理局(ECHA)的关联性评估框架(RAAF)中的评估要素(AEs),并评估相关不确定性,对关联性推断进行了评估。在基于化学结构和特性的初步关联性推断之后,通过整合新方法策略(NAM)和其他现有数据等数据流,降低了不确定性。此外,为了满足 ECHA RAAF 框架的要求,并针对不确定性的具体问题,增加了关联性推断的可信度。尽管对具有强大机制基础的化合物进行了数据丰富的分析,但显然可以应用来自公共资源的 NAM 数据来支持关联性推断。需要承认的是,大多数关联性推断研究不会有如此丰富的数据或强大的机制,因此可能需要进行有针对性的实验以填补数据缺口。在这种情况下,NAM 应该构成具有特定目标的新的实验测试,旨在降低不确定性并验证关联性推断假设。