Abstract：
Although ecotoxicological and toxicological risk assessments are performed separately from each other, recent efforts have been made in both disciplines to reduce animal testing and develop predictive approaches instead, for example, via conserved molecular markers, and in vitro and in silico approaches. Among them, adverse outcome pathways (AOPs) have been proposed to facilitate the prediction of molecular toxic effects at larger biological scales. Thus, more toxicological data are used to inform on ecotoxicological risks and vice versa. An AOP has been previously developed to predict reproductive toxicity of silver nanoparticles via oxidative stress on the nematode Caenorhabditis elegans (AOPwiki ID 207). Following this previous study, our present study aims to extend the biologically plausible taxonomic domain of applicability (tDOA) of AOP 207. Various types of data, including in vitro human cells, in vivo, and molecular to individual, from previous studies have been collected and structured into a cross-species AOP network that can inform both human toxicology and ecotoxicology risk assessments. The first step was the collection and analysis of literature data to fit the AOP criteria and build a first AOP network. Then, key event relationships were assessed using a Bayesian network modeling approach, which gave more confidence in our overall AOP network. Finally, the biologically plausible tDOA was extended using in silico approaches (Genes-to-Pathways Species Conservation Analysis and Sequence Alignment to Predict Across Species Susceptibility), which led to the extrapolation of our AOP network across over 100 taxonomic groups. Our approach shows that various types of data can be integrated into an AOP framework, and thus facilitates access to knowledge and prediction of toxic mechanisms without the need for further animal testing. Environ Toxicol Chem 2024;00:1-14. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
摘要：
尽管生态毒理学和毒理学风险评估是彼此分开进行的，最近在这两个学科中都做出了努力，以减少动物试验并开发预测方法，例如,通过保守的分子标记，以及体外和计算机模拟方法。其中,已经提出了不良结果途径(AOPs),以促进在更大的生物学尺度上预测分子毒性作用.因此，更多的毒理学数据被用来告知生态毒理学风险，反之亦然。先前已经开发了AOP来预测银纳米颗粒通过氧化应激对线虫秀丽隐杆线虫的生殖毒性(AOPwikiID207)。根据先前的研究，我们本研究旨在扩展AOP207的生物学上合理的适用性分类域（tDOA）。各种类型的数据，包括体外人类细胞，在体内，从分子到个体，从以前的研究中收集并构建为跨物种AOP网络，该网络可以为人类毒理学和生态毒理学风险评估提供信息。第一步是收集和分析文献数据以符合AOP标准并构建第一个AOP网络。然后，使用贝叶斯网络建模方法评估关键事件关系，这给了我们的整体AOP网络更多的信心。最后,使用计算机模拟方法（基因到路径物种保护分析和序列比对以预测跨物种的易感性）扩展了生物学上合理的tDOA，这导致了我们的AOP网络在100多个分类组中的推断。我们的方法表明，各种类型的数据可以集成到AOP框架中，因此，无需进一步的动物试验，就可以获得有关毒性机制的知识和预测。环境毒物化学2024；00：1-14。©2024作者WileyPeriodicalsLLC代表SETAC出版的环境毒理学和化学。
