Fitzpatrick J M, Patlewicz G
a National Center for Computational Toxicology (NCCT), US Environmental Protection Agency (US EPA) , Durham , USA.
SAR QSAR Environ Res. 2017 Apr;28(4):297-310. doi: 10.1080/1062936X.2017.1311941. Epub 2017 Apr 20.
The information characterizing key events in an Adverse Outcome Pathway (AOP) can be generated from in silico, in chemico, in vitro and in vivo approaches. Integration of this information and interpretation for decision making are known as integrated approaches to testing and assessment (IATA). One such IATA was published by Jaworska et al., which describes a Bayesian network model known as ITS-2. The current work evaluated the performance of ITS-2 using a stratified cross-validation approach. We also characterized the impact of replacing the most significant component of the network, output from the expert system TIMES-SS, with structural alert information from the OECD Toolbox and Toxtree. Lack of structural alerts or TIMES-SS predictions yielded a sensitization potential prediction of 79%. If the TIMES-SS prediction was replaced by a structural alert indicator, the network predictivity increased up to 87%. The original network's predictivity was 89%. The local applicability domain of the original ITS-2 network was also evaluated using reaction mechanistic domains to understand what types of chemicals ITS-2 was able to make the best predictions for. We found that the original network was successful at predicting which chemicals would be sensitizers, but not at predicting their potency.
不良结局途径（AOP）中关键事件的特征信息可通过计算机模拟、化学实验、体外实验和体内实验等方法生成。整合这些信息并进行决策解读被称为综合测试与评估方法（IATA）。Jaworska等人发表了一种这样的IATA，它描述了一种名为ITS-2的贝叶斯网络模型。当前的工作使用分层交叉验证方法评估了ITS-2的性能。我们还研究了用经合组织工具箱和Toxtree的结构警示信息取代网络中最重要的组成部分（专家系统TIMES-SS的输出）所产生的影响。缺乏结构警示或TIMES-SS预测时，致敏潜力预测为79%。如果用结构警示指标取代TIMES-SS预测，网络预测能力可提高到87%。原始网络的预测能力为89%。还使用反应机理域评估了原始ITS-2网络的局部适用范围，以了解ITS-2能够对哪些类型的化学品做出最佳预测。我们发现，原始网络在预测哪些化学品会成为致敏原方面很成功，但在预测其效力方面则不然。
