The connection between Bayesian networks and adverse outcome pathway (AOP) networks and how to use it for predicting drug toxicity

• The adverse outcome pathway (AOP) framework is useful for drug toxicity prediction. • Bayesian networks (BN) is a natural fit for predictive models using AOPs. • Empirical results for hepatotoxicity confirmed the congruence between the mathematical properties of BN and AOPs. • Mathematical properties of BN can be utilized to significantly simplify modeling tasks using AOPs. There is significant interest in combining adverse outcome pathways (AOPs) with Bayesian networks (BNs) because of their shared representation using directed acyclic graphs (DAGs). However, it has not been verified empirically whether AOP networks are mathematically congruent with BNs. Furthermore, important properties for BNs, such as Markov blankets, have not been emphasized, which is a missed opportunity for simplifying and optimizing the model. Here, we summarize the connection between AOP networks and BNs and explore the implications for toxicity modeling. We also present a case study in drug-related liver toxicity. Our results confirm that AOP networks are congruent mathematically with BNs, with incorporation of the mathematical properties of BN leading to significantly simplified and more efficient models.