Science Inventory

Adverse Outcome Pathway Network Analyses: Techniques and benchmarking the AOPwiki

Citation:

Pollesch, N., J. O'Brien, AND Dan Villeneuve. Adverse Outcome Pathway Network Analyses: Techniques and benchmarking the AOPwiki. SETAC North America, Minneapolis, MN, November 12 - 16, 2017.

Impact/Purpose:

In order to link molecular level perturbations elicited by chemicals or other stressors to adverse outcomes at higher levels of biological organization that are typically of regulatory significance, a mechanistic description is often required. Adverse outcome pathways (AOPs) provide researchers and regulators a formal framework to link molecular initiating events (MIEs) to adverse outcomes (AOs) by defining causal relationships between measureable key events (KEs) and scientifically defensible underpinnings that support extrapolation from one even to the next the pathway. Because stressors will often trigger more than one pathway, and those pathways can interact and influence one another, it is important to consider AOPs in a systems context. AOP networks, which are single AOPs joined by one or more key events, can be developed and analyzed to provide a systems-level understanding. This paper highlights graph theoretic and network analytic techniques that are relevant for studying properties of AOP networks. These techniques may be applied by program office partners to use AOPs in the evaluation of more complex, “real world”, exposure scenarios. Additionally, this paper looks at the repository of AOP knowledge within the AOPwiki (AOPwiki.org) applies a set of network analysis techniques to benchmark the current state of the AOPwiki. This will be useful for guiding development of new AOPs within the CSS 17.01 Adverse Outcome Pathway Discovery and Development Project and providing baseline metrics to help assess the overall growth and impact of the AOP knowledgebase in upcoming years.

Description:

Abstract: As the community of toxicological researchers, risk assessors, and risk managers adopt the adverse outcome pathway (AOP) paradigm for organizing toxicological knowledge, the number and diversity of adverse outcome pathways and AOP networks are continuing to grow. This growth includes the description of new AOPs as well as refinement and linking of existing AOPs. This paper introduces a suite of network analytic and graph theoretic techniques that are relevant for analyzing AOP networks; these techniques are described and displayed through an analysis of the Collaborative Adverse Outcome Pathway Wiki (AOPwiki) knowledge base (AOPwiki.org). The AOPwiki is a repository for storing and sharing AOPs within the expanding AOP community. As a result of using the AOPwiki to illustrate techniques, this paper also serves as a benchmarking effort for the on-going development AOP knowledge base. This benchmarking will be useful for understanding the current state of AOP knowledge within the AOPwiki, identifying major points of connectivity among current AOP descriptions, and identifying parts of the overall network that would benefit from further elaboration. The analyses highlighted provide an important baseline that will be important for understanding how AOP knowledge is expanding and changing in coming years and how effectively the crowd-sourced model of AOP development is working in practice. The contents of this abstract neither constitute, nor necessarily reflect, US EPA policy. Product Description: Linking molecular level interactions with chemicals or other stressors to adverse outcomes at higher levels of biological organization that are of regulatory significance is a challenge. Adverse outcome pathways (AOPs) provide a structured methodology to address this challenge. In addition to structuring toxicological knowledge, AOPs have found use in a variety of applications, from serving to facilitate weight of evidence approaches for risk assessment to providing a structure for quantitative modeling efforts (qAOPs). This research product is two-fold, first, it develops and introduces a set of mathematical and statistical techniques for analyzing AOP networks that allow researchers to extract additional information and value from their investment in constructing an AOP network. Second, the benchmarking aspect of this product will provide an assessment of progress that is useful to track how AOP development is continuing and to highlight areas of interest to EPA researchers and beyond.

Record Details:

Record Type: DOCUMENT (PRESENTATION/SLIDE)
Product Published Date: 11/16/2017
Record Last Revised: 11/13/2017
OMB Category: Other
Record ID: 338274

Organization:

U.S. ENVIRONMENTAL PROTECTION AGENCY

OFFICE OF RESEARCH AND DEVELOPMENT

NATIONAL HEALTH AND ENVIRONMENTAL EFFECTS RESEARCH LABORATORY

MID-CONTINENT ECOLOGY DIVISION