A METHOD TO CALIBRATE CHEMICAL AGNOSTIC QUANTITATIVE ADVERSE OUTCOME PATHWAYS ON MULTIPLE CHEMICAL DOSE-RESPONSE DATA

Quantitative Adverse Outcome Pathways (qAOPs) may support next-generation risk assessment by integrating New Approach Methodologies (NAMs) for derivation of relevant point of departures for hazard characterisation. To be useful, a qAOP should be chemical agnostic. We use a hierarchical statistical modeling approach to separate chemical-specific from chemical-agnostic patterns in response-response relationships between key events in an AOP. The method implements chemical-specific parameters governed by an overarching hierahical structure. Through a simulation study, we demonstrate that the hierarchical specification can capture between chemical heterogeneity, allowing for a way to evaluate if a calibrated qAOP is chemical agnostic. The model’s practical utility was validated using a case study of non-genotoxic liver tumor development, successfully handling heterogeneous responses while maintaining pathway-level insights. This approach enhances qAOP generalizability while preserving chemical agnosticity, supporting reliable NAMs-based next-generation risk assessments.