Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Brussels, Belgium.
Sciome LLC, Durham, NC, USA.
J Biomed Inform. 2023 Sep;145:104465. doi: 10.1016/j.jbi.2023.104465. Epub 2023 Aug 2.
Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships.
Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship.
This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis.
This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
不良结局途径（AOP）网络是毒理学和风险评估中非常有用的工具，可用于捕获和可视化源自各种数据源的毒性的驱动机制。它们具有由一组分子起始事件和关键事件组成的共同结构，通过关键事件关系连接，导致实际的不良结局。AOP 网络被认为是活的文档，应通过输入新数据频繁更新。这种迭代优化练习通常是手动进行的，这不仅耗时费力，而且还存在忽略关键数据的风险。本研究介绍了一种使用人工智能对先前发表的化学诱导胆汁淤积 AOP 网络进行 AOP 网络优化的新方法，以促进自动化数据收集，随后对分子起始事件、关键事件和关键事件关系进行后续定量置信度评估。
通过免费的网络平台 Sysrev 进行人工智能辅助数据收集。为了对优化的 AOP 网络进行基于证据权重的评估，对定制的布拉德福德-希尔标准的置信度水平进行量化。计算了生物学合理性、经验证据和必要性的分数，并将其整合到关键事件关系置信度的总分数中。使用 Cytoscape 可视化优化的 AOP 网络，节点大小表示关键事件的发生率，边缘大小表示关键事件关系的总置信度。
这导致分别确定了 38 个和 135 个独特的关键事件和关键事件关系。转运体变化是发生率最高的关键事件，与胆汁淤积这一不良结局形成了最有信心的关键事件关系。AOP 网络中存在的其他重要关键事件包括：核受体变化、细胞内胆汁酸积累、胆汁酸合成变化、氧化应激、炎症和细胞凋亡。
这个过程产生了一个广泛的信息丰富的 AOP 网络，重点关注化学诱导的胆汁淤积。这个优化的 AOP 网络可以作为开发一组体外测定的机制指南针，以可靠地预测化学诱导的胆汁淤积性损伤。
