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Adverse outcome pathway networks II: Network analytics
Citation:
Villeneuve, Dan, M. Angrish, M. Fortin, I. Katsiadaki, M. Leonard, L. Margiotta-Casaluci, S. Munn, J. O'Brien, N. Pollesch, C. Smith, X. Zhang, AND D. Knapen. Adverse outcome pathway networks II: Network analytics. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, 37(6):1734-1748, (2018).
Impact/Purpose:
The US EPA is developing more cost effective and efficient ways to evaluate chemical safety using high throughput and computationally based testing strategies. An important component of this approach is the ability to translate chemical effects on fundamental biological processes like enzyme activities, gene expression, and basic cellular functions into what those effects mean to human health or ecosystem sustainability. The adverse outcome pathway framework was developed to facilitate that translation. The current presentation focuses on how to apply that framework to predict more complex interactions resulting from exposure to chemicals that cause multiple biological effects in an organism or exposures to mixtures of chemicals. It summarizes results of an expert workshop and lays out fundamental concepts that are expected to guide the analysis of AOP networks to support their application in research, risk assessment, and regulatory decision-making. This is foundational research aimed at addressing the challenges to predictive risk assessment that are posed by exposure to multiple chemicals, pleiotropic effects of single chemical exposures, and the diversity of effects chemicals may cause in different taxa, life-stages, or sexes of organisms. This research directly supports Task 2.3 under CSS Project 17.01.
Description:
The US EPA is developing more cost effective and efficient ways to evaluate chemical safety using high throughput and computationally based testing strategies. An important component of this approach is the ability to translate chemical effects on fundamental biological processes like enzyme activities, gene expression, and basic cellular functions into what those effects mean to human health or ecosystem sustainability. The adverse outcome pathway framework was developed to facilitate that translation. The current work focuses on how to apply that framework to predict more complex interactions resulting from exposure to chemicals that cause multiple biological effects in an organism or exposures to mixtures of chemicals. This is critical work, as most real-world exposures to chemicals involve these more complex scenarios. Exposures to multiple stressors and knowledge that stressors may have more than one effect on biological systems and those effects may vary depending on the life-stage, sex, and taxa exposed and/or the route by which those exposures occur, represent a significant challenge in risk assessment. The adverse outcome pathway (AOP) framework was designed to allow for development of AOP networks that could aid the analysis of pleiotropic and interactive effects for these complex exposure scenarios. The present paper introduces nascent concepts related to analysis of AOP networks. Graph theory-based approaches for identifying important topological features are illustrated using two example AOP networks derived from extant AOP descriptions. Considerations for identifying the most significant paths through an AOP network from either a biological or risk assessment perspective are described. Finally, approaches for identifying interactions among AOPs that may result in additive, synergistic, or antagonistic responses, or previously undefined emergent patterns of response, are introduced. Along with a companion article (Knapen et al. part I), these concepts set the stage for development of tools and case studies that will facilitate more rigorous analysis of AOP networks and evaluation of the utility of AOP network-based predictions for use in research and regulatory decision-making. This addresses one of the major themes identified through a SETAC Horizon Scanning effort focused on advancing the AOP framework.
