Development of qualitative and quantitative AOPs and their integration into risk assessment
Abstract
Chemical hazard assessment can directly use qualitative adverse outcome pathways (AOPs) to integrate data generated by alternative methods or in vivo testing. Risk assessment requires quantitative relationships from exposure to effect timing and magnitude: quantitative AOPs (qAOPs) should be able to provide such dose-time-response predictions. There is also an intermediate level of quantification, in which qAOPs are able to make predictions about the probability of a chemical to belong to a category such as toxic/nontoxic, or low/ medium/high toxicity. Bayesian networks have typically been used in the latter case, and are suitable for refined hazard assessment. We will first briefly review the various methods and their main applications so far. In EU-ToxRisk, we have extended the Bayesian network (BN) approach to encompass continuous dose-time-outcome qAOPs. We compared BN to empirical dose-response modeling and to systems biology (SB) modeling. This was done for an oxidative stress induced chronic kidney disease AOP, using in vitro data obtained on RPTEC/ TERT1 cells exposed to potassium bromate. We showed that, despite the fact that dose-response models give adequate fits to the data they should be accompanied by mechanistic modeling to gain a proper understanding of domain of applicability of the quantification. BNs can be both more precise than dose-response models and simpler than SB models, but more experience with their use is needed. We have since extended our work to qAOPs of mitochondrial disruption induced toxic effects in HepG2 (liver), RPTEC/TERT1 (kidney) and LUHMES (neuronal) cells, after exposure to several chemicals, and present those new results in this session. Comparison of the results across cell types and chemicals will be discussed, together with the assumption of chemical independence of the qAOPs developed. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 681002 as well as from the Innovative Medicines Initiative 2 Joint Undertaking (IMI2/JU) under grant agreement No 777365.