The FAIR AOP Roadmap: ?Community Efforts Related to AOP Standards

Background   As background, several parallel efforts have emerged at (re-) mapping biomedical information to AOPs. These exist as updates to the original ontology mapping efforts of the AOP-Wiki itself (EMOD data prototype effort, now 2.0;¿https://emod.aopwiki.org/prototypes¿) and early gene mapping efforts like EPA AOP-DB. More recently several FAIR efforts and FAIR contributing AOP resources have further developed FAIR standards through the improved mapping of biomedical and chemical stressor information to AOPs (e.g. AOP-DB RDF; AOP Wiki RDF; AOP-Wiki Explorer; CompToxAI). These efforts also directly affect assay mapping efforts for both human and ecxotox (e.g. Methods2AOP; Malinowski/Saarimaki- OECD AOP OMICs; B. Martens OMICs for Genetox; K.Tollefsen EXPECT). How these types of exercises will feed back to the AOP-Wiki has yet to be determined, but coordination of various approaches, or lack thereof, will directly affect the trustability and acceptance of AOP information on this level and efforts to automate AOP creation (e.g. ChatAOPAI4AOP).  Concurrent to these AOP focused efforts, there are several recently funded as well as ongoing efforts to advance the construction and usage of biomedical knowledge bases that are relevant to environmental health research and policy, such as the Monarch Inititiative, the NCATS Data Translator, the NIEHS Robokop, the NSF-NIH ProtoOKNs, and the NIEHS Comparative Toxicogenomics Database. Efforts to coordinate and advance AOP standards that are not informed by these other efforts risks increased incompatibilities as well as missing out on opportunities for positive collaborations and reuse of methods and tools.  As a venue, we are considering a one-day virtual meeting with short overview talks from speakers to highlight efforts mentioned here with particular emphasis on language standards (ie terminologies, ontologies, knowledge models). This would include time at the end for question and answers as well as discussion. Our aim is to foster awareness of efforts and potentially to frame either solutions or next steps.

Impact/Purpose

Abstract:Background and Purpose: Adverse Outcome Pathways (AOPs) describe the mechanistic interaction of biological entities that result in an adverse response.How we catalog these interactions and associations contributes to our ability to understand mechanistic effects and apply this knowledge to New ApproachMethodologies (NAMs). Related, our ability to connect AOPs to other types of biological and toxicological data defines their utility, reuse, and interoperability and whether AOP data will have the potential to reduce animal testing in chemical and material safety assessments in the future. Wittwehr,et al. (2024) describe why AOPs need to be FAIR and underline the pragmatic necessity of machine-actionability for AOPs to reach their full-impact. The FAIR AOP Cluster Workgroup is an international group of academic, government and industry partners with a shared interest in the use and reliability of AOPinformation. With the recent proliferation of methods and tools that aim to map AOP data and metadata to biomedical entities, the FAIR AOP Cluster Workgroup has formed with the objective to address the coordinated standardization and identification of mechanistic information and data associated with AOPs. The FAIR AOP Cluster Workgroup has reviewed AOP projects and initiatives with related data challenges in an effort to identify synergies and promote coherence. This effort facilitates standardized AOP annotation, and promotes machine actionability and increased trustability of AOP information, while directing community contribution through an open data model.Methods: Making AOP data and supporting metadata FAIR (findable, accessible, interoperable, and re-usable) depends on the coordination and active orchestration of FAIR Enabling Resources and the establishment of consensus formats and description of processes. The current FAIR AOP Cluster Workgroup efforts to document existing AOP projects and tools that implement mechanistic data, metadata, and related biomedical information are described.Results: We discuss existing AOP tools and current approaches for mapping mechanistic data types to AOPs. We review the outcomes of international and collaborative AOP coordination efforts (ELIXIR, The Environmental Health Language Collaborative's 2025 AOP Standards Workshop, the AOP FAIR Implementation Profile) and the progress made in development of FAIR AOP standards. We describe the recent FAIR AOP Cluster Workgroup efforts and progress in defining a FAIR AOP ROADMAP. We also position the FAIR AOP Cluster work among other initiatives, efforts and projects that aim at improvingthe AOP-Wiki and the AOP Framework, and describe how our results and recommendations will impact the planning for AOP-Wiki 3.0.Conclusions: The FAIR AOP Roadmap describes how, through coordinated efforts, AOP mechanistic data and metadata and related biomedical entities can be incorporated to improve the FAIR standards of the AOP framework. These efforts will affect future iterations of AOP FAIR enabling resources including the AOP-Wiki repository, and the trustability of AOP information for inclusion risk assessment and regulatory efforts. This abstract does not reflect EPA Policy

Citation

Mortensen, H. The FAIR AOP Roadmap: ?Community Efforts Related to AOP Standards. EHLC AOP Standards Workshop, Research triangle park, NC, June 04 - 05, 2025.