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Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling

机译：贝叶斯回归和贝叶斯网络建模的不利结果途径网络的量化

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The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2020;00:1-18. (c) 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)
机译：不利的结果途径（AOP）框架已经获得了国际认可，作为将机械过程与毒性终点连接的系统方法。然而，成功实施风险评估仍然受到缺乏定量AOP模型（QAOP）和对不确定因素的限制。迄今为止，少数出版的QAOP模型通常基于数据苛刻的系统生物学模型。在这里，我们提出了一种基于回归建模和贝叶斯网络（BNS）的AOP网络的量化较少的数据苛刻方法。我们用所提出的AOP＃245表明这种方法，“光学磷酸化的解耦导致ATP产生相关生长抑制”，使用从lemna次要暴露于农药3,5-二氯苯酚的小实验数据。 AOP-BN反映了包含2个分子启动事件（MIES），3个关键事件（KES）和1个不利结果（AO）的AOP＃245的网络结构。首先，对于每个剂量 - 响应和响应 - 响应（KE）关系，我们量化了贝叶斯回归建模的因果关系。回归模型对应于常用于生态毒理学的剂量 - 反应功能。其次，我们使用相关的不确定性应用拟合的回归模型来沿预测器梯度模拟10 000响应值。第三，我们使用模拟值来参数化BN模型的条件概率表。量化的AOP-BN模型可以在几个方向上运行：1）预后推断，从压力源节点向前运行以预测AO水平; 2）诊断推断，从AO节点向后运行; 3）全向上，从中间MIE和/或KES运行。内部验证表明，当运行来自中间节点时，AOP-BN可以获得高精度率，并且当AO可接受低分辨率时。尽管该AOP-BN的性能受到小型数据集的限制，但我们的研究证明了概念证明：贝叶斯回归建模和贝叶斯网络建模的组合使用量化AOP。积分环境评估管理MANAC 2020; 00：1-18。 （c）2020作者。综合环境评估和管理由Wiley期刊LLC代表环境毒理学和化学（Setac）

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