A Method to Calibrate Chemical-agnostic Quantitative Adverse Outcome Pathways on Multiple Chemical Data

Quantitative Adverse Outcome Pathways (qAOPs) may support next-generation risk assessment by integrating New Approach Methodologies (NAMs) for derivation of points of departure. To be useful, a qAOP should be chemical-agnostic. However, existing qAOP studies often pool multi-chemical data without adequately addressing inter-chemical heterogeneity. Consequently, fundamental pathway relationships become obscured by heterogeneity-induced noise, thereby compromising the reliability of chemical-agnostic predictions. We developed a chemical-agnostic calibration approach to addresses this challenge by leveraging hierarchical structures to systematically separate chemical-specific heterogeneity from underlying pathway effects. Through this methodological framework, chemical-specific deviations are explicitly modeled as random effects, enabling the extraction of pathway-level parameters that represent core mechanistic relationships independent of individual chemical properties. Through simulation studies across varying heterogeneity levels, we demonstrate that performance differences between models with and without hierarchical calibration reveal the magnitude of heterogeneity in the data. Moreover, when heterogeneity is substantial, an uncalibrated qAOP should not be considered truly chemical-agnostic in practice, as it confounds pathway-level effects with chemical-specific variation. We demonstrated the application of this calibration approach through a case study of non-mutagenic liver tumor. The framework proposed in this study enhances qAOP generalizability while preserving the chemical-agnostic principle, supporting robust NAMs-based next-generation risk assessments.