Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways

This paper presents a framework for organizing and accessing mechanistic data on chemical interactions. The framework is designed to support the assessment of risks from combined chemical exposures. The framework covers interactions between chemicals that occur over the entire source-to-outcome continuum including interactions that are studied in the fields of chemical transport, environmental fate, exposure assessment, dosimetry, and individual and population-based adverse outcomes. The framework proposes to organize data using a semantic triple of a chemical (subject), has impact (predicate), and a causal event on the source-to-outcome continuum of a second chemical (object). The location of the causal event on the source-to-outcome continuum and the nature of the impact are used as the basis for a taxonomy of interactions. The approach also builds on concepts from the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP). The framework proposes the linking of AEPs of multiple chemicals and the AOP networks relevant to those chemicals to form AEP-AOP networks that describe chemical interactions that cannot be characterized using AOP networks alone. Such AEP-AOP networks will aid the construction of workflows for both experimental design and the systematic review or evaluation performed in risk assessments. Finally, the framework is used to link the constructs of existing component-based approaches for mixture toxicology to specific categories in the interaction taxonomy.

These slides are the sole product of the author, have not been reviewed by the U.S. Environmental Protection Agency, and may not reflect the agency's policy. 9/10/2020 2

Contributors
• Justin Teeguarden, Yu-Maei Tan, Steven Edwards (and others) for developing the Aggregate Exposure Pathway that made this possible • Mark Nelms, Jane Ellen Simmons, Stephen Edwards for thoughtful analysis on Adverse Outcome Pathways and mixtures that anticipated much of this talk • SETAC Pellston workshop on advancing the AOP framework • Jeremy Leonard who coauthored an earlier paper on the taxonomy portion of the framework and Lyle Burgoon and Annie Jarabek for their work on the cited case studies and the sections on the application of the framework • Encouragement and thoughtful comments from David Herr and Rory Connolly • All errors and flaws are mine 9/10/2020 4 Background 9/10/2020 5 Challenge of chemical interactions, mixture toxicity, and the exposome • Mixture toxicity is a function of the combinations of chemicals involved in the interaction • The number of combinations are far larger than the number of chemicals • Humans and ecological receptors are exposed to millions of complex mixtures • Exposures need not be concurrent. Chemical X's effects may persist and affect the impacts of future exposures to chemical Y • The combination of all exposure sources forms the exposome that has been shown to have significant impacts human health 6 9/10/2020 Historical approaches to assessing chemical interactions in animal models Defined by response data for groups of chemicals measured separately and together Such data provides the basis for categories of interaction: • Movement to in vitro and in chemico models of toxicity from in vivo models • Leveraging in vivo and in vitro data to make in silico predictions • Movement from empirical to mechanistic-based findings for toxicity, exposure, and risk analyses • Building pipelines for high-throughput analyses • These tools give insights on the mechanisms of toxicity but not necessarily a finding of toxicity 8 9/10/2020 Adverse Outcome and Aggregate Exposure Pathways (AEP and AOP) Created to meet the need for flexible frameworks to organize, hold, and make use of data from existing toxicity studies, new findings, and survey results Based on concepts from graph theory and Resource Description Framework (RDF) approaches Together they cover the entire source-to-outcome continuum The terms interaction and noninteraction are already defined in mixture toxicology • Existing definitions derived from empirical data on dose and response • Interaction: The combined dose response cannot be explained by response addition or dose addition • Non interaction: The combined dose response can be explained by response addition or dose addition • New definitions derived from mechanism • Interaction: The ability of one chemical (X) to cause a change in the source-tooutcome continuum of a second chemical (Y) for a defined AO • Non-interaction: The lack of the ability of X to cause a change the source tooutcome of Y at any dose of X below the maximum tolerated dose of X (similar to the definition of "no apparent influence")

Interactions have direction
In vivo and in vitro models of do not indicate what chemical X is doing to the toxicity of chemical Y or what Y is doing to the toxicity X.
But mechanistic findings are directed -X changes the toxicity of Y by a specific mechanism 9/10/2020 17

Mechanism of a directed chemical interaction
When two chemicals cause a common AO It may be useful to model how chemical X changes the toxicity of chemical Y and how chemical Y changes the toxicity of chemical X 9/10/2020 19 A taxonomy of chemical interactions 9/10/2020 20 Taxonomy is offered as a useful framework for organizing findings on chemical interactions • Covers all interactions that occur over the source-tooutcome continuum • The system of categories are: • Exhaustive -all interactions fall into one of the categories • Mutually exclusive (an interaction will fall into only one category) • Binary interactions 9/10/2020 21  The framework and informatics 9/10/2020 30

Subject: Chemical X
• Chemical X is defined as the "acting" agent • Chemical X, or its effects, must share the environment/organism during the time of the release-exposure-response events of chemical Y • The ability of chemical X to act are due to its physical, chemical, or toxicological properties • Chemical X has its own AEP and AOP separate from chemical Y's • Such data are metadata for chemical X in the semantic triple

Synergy
Synergy occurs between two chemicals, X and Y, when a prior, or concurrent, exposure to chemical X causes an increase in the response to a release of Y from a source by: 1) increasing the ratio of the amount of Y released by a source and the TSE for Y, or its active metabolite (kinetic synergy), or 2) increasing the probability that a MIE of given intensity and duration will result in the AO (dynamic synergy).