AOP-Wiki EMOD 3.0: Data Model Expansions and Content Evaluation Framework for Using Agentic AI to Improve Integration between AOPs and New Approach Methodologies (NAMs)

2026-05-20

AI Summary

This work addresses the limitations of the current AOP-Wiki data model, which hinders the continuous evolution of Adverse Outcome Pathways (AOPs) and impedes their integration with New Approach Methodologies (NAMs) and application in regulatory science. To overcome these challenges, the study introduces agent-based AI into the modernization of AOP-Wiki and develops EMOD 3.0, a prototype evidence model that restructures the underlying data architecture and formalizes evidence representation in alignment with FAIR principles. This framework substantially enhances the AI-readiness and interoperability of AOPs, enabling the advancement of quantitative AOPs (qAOPs) and computationally generated AOPs. It also strengthens the synergy between AOPs and NAMs within multiscale biological models, thereby establishing a scalable, high-quality knowledge infrastructure to support next-generation risk assessment.

Abstract

Adverse Outcome Pathways (AOP) are logic models that causally link biological mechanisms that can be measured in a lab to adverse outcomes, relevant to chemical regulatory endpoints. AOPs contextualize new approach methodologies (NAMs), in vitro and in silico methods used as alternatives to animal testing and the sequential events in an AOP serve as multi-scale models spanning biological scales. The AOP-Wiki serves as the global repository for AOPs. While the AOP-Wiki has played a central role in AOP expansion over the past decade, constraints within the current data model and application infrastructure limit the AOP-Wiki from supporting continued AOP growth and evolution. Yet, the transformative power of agentic AI has re-invigorated AOP-Wiki data modernization efforts at a time when core AOP principles can be harnessed to inform use of AI for aggregating and structuring AOP-relevant information. Seizing upon this momentum, we present AOP-Wiki EMOD 3.0, the third in a series of evidence model prototypes, which concretely demonstrates data model expansions and our vision for how the AOP-Wiki might be transformed to better serve regulatory science and emergent use of AOPs in biomedical and One Health contexts. We aim to lay a foundation to support computationally-generated AOPs and quantitative AOPs (qAOPs) by focussing on solutions for AOP-Wiki internal quality improvement, evidence structuring to enhance AOP FAIRness and AI-readiness, and improved integration between the AOP framework and NAMs to better serve next generation risk assessment.

Problem

• Adverse Outcome Pathways
• New Approach Methodologies
• AOP-Wiki
• data model
• regulatory science

Innovation

• Agentic AI
• AOP-Wiki EMOD 3.0
• Quantitative AOP
• FAIR data
• New Approach Methodologies