不良结果途径(AOPs)总结了对毒理学效应的机理理解,例如,被强调为将新的体外和计算机模拟方法的数据整合到化学风险评估中的有前途的工具。基于AOP的网络被认为是AOP的功能实现,因为它们更能代表复杂的生物学。同时,目前没有统一的方法来生成AOP网络(AOPN)。确定相关AOPs的系统策略,以及从AOP-Wiki中提取和可视化数据的方法,是需要的。这项工作的目的是开发一种结构化的搜索策略,以识别AOP-Wiki中的相关AOP,和自动数据驱动的工作流程来生成AOPN。该方法应用于一个案例研究,以产生一个专注于雌激素的AOPN,雄激素,甲状腺,和类固醇生成(EATS)模式。根据ECHA/EFSA《内分泌干扰物鉴定指导文件》中的效果参数,先验地开发了一种搜索策略,其中包含搜索项。此外,通过筛选AOP-Wiki中每个途径的内容来进行数据的手动管理,排除不相关的AOP。数据是从Wiki下载的,计算工作流被用来自动处理,过滤器,并格式化数据以进行可视化。这项研究提出了一种在AOP-Wiki中对AOP进行结构化搜索的方法,该方法与自动数据驱动的工作流程相结合,以生成AOPN。此外,这里提供的案例研究提供了与EATS模式相关的AOP-Wiki内容的地图,也是进一步研究的基础,例如,整合来自新方法的机械数据,探索基于机制的方法来识别内分泌干扰物(ED)。计算方法可以作为R脚本免费获得,并且当前允许基于来自AOP-Wiki的数据和用于过滤的相关AOP列表来(重新)生成和过滤新的AOP网络。
Adverse Outcome Pathways (AOPs) summarize mechanistic understanding of toxicological effects and have, for example, been highlighted as a promising tool to integrate data from novel in vitro and in silico methods into chemical risk assessments. Networks based on AOPs are considered the functional implementation of AOPs, as they are more representative of complex biology. At the same time, there are currently no harmonized approaches to generate AOP networks (AOPNs). Systematic strategies to identify relevant AOPs, and methods to extract and visualize data from the AOP-Wiki, are needed. The aim of this work was to develop a structured search strategy to identify relevant AOPs in the AOP-Wiki, and an automated data-driven workflow to generate AOPNs. The approach was applied on a
case study to generate an AOPN focused on the Estrogen, Androgen, Thyroid, and Steroidogenesis (EATS) modalities. A search strategy was developed a priori with search terms based on effect parameters in the ECHA/EFSA Guidance Document on Identification of Endocrine Disruptors. Furthermore, manual curation of the data was performed by screening the contents of each pathway in the AOP-Wiki, excluding irrelevant AOPs. Data were downloaded from the Wiki, and a
computational workflow was utilized to automatically process, filter, and format the data for visualization. This study presents an approach to structured searches of AOPs in the AOP-Wiki coupled to an automated data-driven workflow for generating AOPNs. In addition, the
case study presented here provides a map of the contents of the AOP-Wiki related to the EATS-modalities, and a basis for further research, for example, on integrating mechanistic data from novel methods and exploring mechanism-based approaches to identify endocrine disruptors (EDs). The
computational approach is freely available as an R-script, and currently allows for the (re)-generation and filtering of new AOP networks based on data from the AOP-Wiki and a list of relevant AOPs used for filtering.