Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling
Authors: Moe S Jannicke , Wolf Raoul , Xie Li , Landis Wayne G , Kotamäki Niina , Tollefsen Knut Erik 
Published: September 2020

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 2021;17:147–164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

KEY POINTS

We propose a probabilistic modeling approach for quantification of adverse outcome pathways (AOPs) and AOP networks, based on regression modeling and Bayesian networks. We tested 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 the plant Lemna minor to the pesticide 3,5-dichlorophenol. Each dose–response and response–response (key event) relationship was quantified by Bayesian regression modeling, based on dose–response functions commonly applied in ecotoxicology. This proof-of-concept study demonstrates that the combined use of regression modeling and Bayesian network modeling is a promising approach for quantification of AOPs, even for data-poor cases.