Based on the results of a Horizon Scanning exercise sponsored by the Society of Environmental Toxicology and Chemistry that focused on advancing the adverse outcome pathway (AOP) framework, the development of guidance related to AOP network development was identified as a critical need. This not only included questions focusing directly on AOP networks, but also on related topics such as mixture toxicity assessment and the implementation of feedback loops within the AOP framework. A set of two articles has been developed to begin exploring these concepts. In the present article (part I), we consider the derivation of AOP networks in the context of how it differs from the development of individual AOPs. We then propose the use of filters and layers to tailor AOP networks to suit the needs of a given research question or application. We briefly introduce a number of analytical approaches that may be used to characterize the structure of AOP networks. These analytical concepts are further described in a dedicated, complementary article (part II). Finally, we present a number of case studies that illustrate concepts underlying the development, analysis, and application of AOP networks. The concepts described in the present article and in its companion article (which focuses on AOP network analytics) are intended to serve as a starting point for further development of the AOP network concept, and also to catalyze AOP network development and application by the different stakeholder communities. Environ Toxicol Chem 2018;37:1723-1733. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.
Toxicological responses to stressors are more complex than the simple one-biological-perturbation to one-adverse-outcome model portrayed by individual adverse outcome pathways (AOPs). Consequently, the AOP framework was designed to facilitate de facto development of AOP networks that can aid in the understanding and prediction of pleiotropic and interactive effects more common to environmentally realistic, complex exposure scenarios. The present study introduces nascent concepts related to the qualitative analysis of AOP networks. First, graph theory-based approaches for identifying important topological features are illustrated using 2 example AOP networks derived from existing AOP descriptions. Second, considerations for identifying the most significant path(s) 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 (part I), these concepts set the stage for the development of tools and case studies that will facilitate more rigorous analysis of AOP networks, and the utility of AOP network-based predictions, for use in research and regulatory decision-making. The present study addresses one of the major themes identified through a Society of Environmental Toxicology and Chemistry Horizon Scanning effort focused on advancing the AOP framework. Environ Toxicol Chem 2018;37:1734-1748. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.
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