The potential of AOP networks for reproductive and developmental toxicity assay development

Abstract

Historically, the prediction of reproductive and developmental toxicity has largely relied on the use of animals. The adverse outcome pathway (AOP) framework forms a basis for the development of new non-animal test methods. It also provides biological context for mechanistic information from existing assays. However, a single AOP may not capture all events that contribute to any relevant toxic effect, even in single chemical exposure scenarios. AOP networks, defined as sets of AOPs sharing at least one common element, are capable of more realistically representing potential chemical effects. They provide information on interactions between AOPs and have the potential to reveal previously unknown links between biological pathways. Analysis of these AOP networks can aid the prioritization of assay development, whether the goal is to develop a single assay with predictive utility of multiple outcomes, or development of assays that are highly specific for a particular mode of action. This paper provides a brief overview of the AOPs related to reproductive and developmental toxicity currently available in the AOP Wiki (http://aopwiki.org), and gives an example of an AOP network based on five reproductive and developmental toxicity-related AOPs for fish to illustrate how AOP networks can be used for assay development and refinement.

Mechanistic study of chlordecone-induced endocrine disruption: Based on an adverse outcome pathway network

“…It is worth noting that AOPs are usually presented linearly, and AOP networks are required to describe most toxicologically relevant processes and to predict AOs in most real-world scenarios (Knapen et al, 2015). In this regard, the in vivo effects of chlordecone on the HPG axis of Chinese rare minnow were studied by determining a series of parameters at different levels as adverse outcomes and key events defined in the AOPs related to the predicted MIEs.…”

“…Recently, the adverse outcome pathway (AOP) has been advocated for chemical risk assessment and mechanistic study (Ankley et al, 2010;Groh et al, 2015;Zhou, 2015). This approach presents a detailed progression of a series of key events (KE) following a molecular initiating event (MIE) leading to an adverse outcome (AO) within a logical framework (Knapen et al, 2015), thus facilitate greater understanding at theoretic level. Since a single AOP may not capture all events contributing to any relevant toxic effect, AOP networks are therefore required to present more realistically potential effects and to reveal previously unknown links between biological pathways (Knapen et al, 2015).…”

“…This approach presents a detailed progression of a series of key events (KE) following a molecular initiating event (MIE) leading to an adverse outcome (AO) within a logical framework (Knapen et al, 2015), thus facilitate greater understanding at theoretic level. Since a single AOP may not capture all events contributing to any relevant toxic effect, AOP networks are therefore required to present more realistically potential effects and to reveal previously unknown links between biological pathways (Knapen et al, 2015). However, there is still considerable work needed to demonstrate and maximize the utility of this framework.…”

“…Besides, the database of Pubchem Bioassay (NIH, 2016), which mainly uses cell-based and biochemical in vitro tests, also provided evidence for agonistic and/or antagonistic activity of androgen receptor (AR) and Cytochrome P450 19A (CYP19A) by chlordecone. The agonism and/or antagonism of steroid receptors and CYP19A as MIE may lead to different series of KEs, some of which could be contrary as indicated by the already described AOPs (Knapen et al, 2015). Hence it is interesting to figure out the prospective MIE and unravel the process driving the observed effects upon chlordecone exposure.…”

Users' Handbook supplement to the Guidance Document for developing and assessing Adverse Outcome Pathways

“…Through their defacto construction as more AOPs are added to the AOP-KB, AOP networks can be viewed as capturing broader knowledge concerning the range of possible AOs a perturbation may cause, or the range of ways in which a particular adverse outcome may occur. AOP networks are also critical for addressing exposures to multiple stressors that lead to the same AO or to individual stressors that perturb multiple MIEs (Knapen et al, 2015;Villeneuve et al, 2014a, b) and for understanding potential interactions between co-occurring AOPs.…”