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B0417
Title: Application of dynamic Bayesian network approaches for quantitative adverse outcome pathway modelling Authors:  Wang Gao - University of Technology of Compiegne (France) [presenting]
Ghislaine Gayraud - University of Technology of Compiegne (France)
Frederic Bois - Certara UK Ltd (United Kingdom)
Abstract: In toxicology, an Adverse Outcome Pathway (AOP) is a conceptual framework that qualitatively describes the existing knowledge on the links between the two anchor points: Molecular Initiating Event (MIE) and Adverse Outcome (AO) at a level of biological organization relevant for risk assessment. The transformation of an AOP to its quantitative version, qAOP allows building a powerful risk assessment tool, thanks to its ability to quantitatively predict the AO. Given that an AOP is by definition a directed chain describing toxicological causality, we propose new methods based on dynamic Bayesian networks for qAOP modelling. The linear and non-linear qAOP models based on different assumptions (stochastic transition without observational error or deterministic transition with Gaussian observational errors) will be introduced in the presentation. We will demonstrate and compare the numerical results of our models applied to simulated data and real data from the toxicological studies of chronic kidney disease and Parkinsonian motor deficits.