Annotating Adverse Outcome Pathways to Organize Toxicological Information for Risk Assessment

The Adverse Outcome Pathway (AOP) framework connects molecular perturbations with organism and population level endpoints used for regulatory decision-making by providing a conceptual construct of the mechanistic basis for toxicity. Development of an AOP typically begins with the adverse outcome, and intermediate effects connect the outcome with a molecular initiating event amenable to high-throughput toxicity testing (HTT). Publicly available controlled vocabularies were used to provide terminology supporting AOP’s at all levels of biological organization. The resulting data model contains terms from 22 ontologies and controlled vocabularies annotating currently existing AOP’s. The model provides the ability to attach evidence in support of the AOP, supports data aggregation, and promotes the development of AOP networks. Long term, this structured description of the AOP will enable logical reasoning for hazard identification and for dose-response assessment. Case studies showcase how the model informs AOP development in the context of chemical risk assessment.Master of Scienc

Creating a Structured AOP Knowledgebase via Ontology-Based Annotations

The Adverse Outcome Pathway (AOP) framework is increasingly used to integrate data based on traditional and emerging toxicity testing paradigms. As the number of AOP descriptions has increased, so has the need to define the AOP in computable terms. Herein, we present a comprehensive annotation of 172 AOPs housed in the AOPWiki as of December 4, 2016, using terms from existing biological ontologies. AOP Key Events (KEs) were assigned ontology terms using a concept called the Event Component, which consists of a Process, an Object, and an Action term, with each term originating from ontologies and other controlled vocabularies. Annotation of KEs with ontology classes from 14 ontologies and controlled vocabularies resulted in a total of 685 KEs being annotated with a total of 809 Event Components. A set of seven conventions resulted, defining the annotation of KEs via Event Components. This expanded annotation of AOPs allows computational reasoners to aid in both AOP development and applications. In addition, the incorporation of explicit biological objects will reduce the time required for converting a qualitative AOP description into a conceptual model that can support computational modeling. As high-throughput genomics becomes a more important part of the high-throughput toxicity testing landscape, the new approaches described here for annotating KEs will also promote the visualization and analysis of genomics data in an AOP context.JRC.F.3-Chemicals Safety and Alternative Method

Working Title: Smoking cessation, harm reduction, and biomarkers protocols in the PhenX Toolkit: Tools for standardized data collection

The use of standard protocols in studies supports consistent data collection, improves data quality, and facilitates cross-study analyses. Funded by the National Institutes of Health, the PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a catalog of recommended measurement protocols that address a wide range of research topics and are suitable for inclusion in a variety of study designs. In 2020, a PhenX Working Group of smoking cessation experts followed a well-established consensus process to identify and recommend measurement protocols suitable for inclusion in smoking cessation and smoking harm reduction studies. The broader scientific community was invited to review and provide feedback on the preliminary recommendation of the Working Group. Fourteen selected protocols for measuring smoking cessation, harm reduction, and biomarkers research associated with smoking cessation were released in the PhenX Toolkit (https://www.phenxtoolkit.org) in February 2021. These protocols complement existing PhenX Toolkit content related to tobacco regulatory research, substance use and addiction research, and other measures of smoking-related health outcomes. Adopting well-established protocols enables consistent data collection and facilitates comparing and combining data across studies, potentially increasing the scientific impact of individual studies