Advancing the quantitative understanding of Adverse Outcome Pathways: current status, methodologies, and future directions

Abstract

An adverse outcome pathway (AOP) framework maps the sequence of events leading to adverse outcomes from chemical exposures, providing a mechanistic understanding often absent in traditional methods. The quantitative AOP (qAOP) advances AOP by integrating quantitative data and mathematical modeling, thereby providing a more precise comprehension of relationships between molecular initiating events, key events, and adverse outcomes. This review critically examines three primary methodologies: systems toxicology, regression modeling, and Bayesian network modeling, highlighting their strengths, limitations, and specific data requirements within toxicology. Through an analysis of current methodologies and challenges, this review emphasizes the integration of experimental and computational approaches to elucidate key event relationships and proposes strategies for overcoming limitations through standardized protocols and advanced computational tools. By outlining future research directions and the potential of qAOPs to transform chemical risk assessment, this review aims to contribute to the advancement of regulatory science and the protection of public health and the environment.