Multi-Omics Integration Into Adverse Outcome Pathway Framework: Principles, Progress, and Prospects for...

The adverse outcome pathway (AOP) framework has emerged as an important tool in mechanistic toxicology, providing chemically agnostic representations of the causal biological sequence from molecular initiating events (MIEs) to apical adverse outcomes (AOs). Yet, traditional AOP construction has relied predominantly on siloed, single-layer biological data, limiting both the mechanistic resolution and quantitative utility of AOPs in regulatory risk assessment. The proliferation of multi-omics technologies, such as transcriptomics, proteomics, metabolomics, and epigenomics, and their single-cell counterparts, now offers new opportunities to populate, validate, and quantify AOP networks with rich, multi-scale molecular data. This review systematically examines how each omics layer contributes uniquely to AOP development and discusses emerging frameworks for their integration. We describe the mechanistic logic underpinning transcriptome-guided key event (KE) identification, proteomic confirmation of KE-to-KE relationships (KERs), metabolomics-based linkage to phenotypic outcomes, epigenomic annotation of persistent and transgenerational effects, and single-cell resolution approaches that dissolve the cell population averaging problem inherent in bulk assays. We further assess quantitative AOP (qAOP) strategies built on benchmark dose (BMD) modeling of omics data, with emerging evidence that transcriptomic points of departure (tPODs) derived from short-term exposures are concordant with chronic apical endpoints. Critical knowledge gaps are identified, including incomplete molecular annotation of KEs in AOP-Wiki, the absence of standardized multiomics bioinformatics pipelines, the underdevelopment of epigenomic and spatial transcriptomic AOP layers, and regulatory hurdles impeding the translation of omics-derived PODs into health-based guidance values (HBGVs). We conclude with a forward-looking framework and research priorities to accelerate the regulatory acceptance of multiomics-informed AOPs as tools for next-generation chemical risk assessment.