From Qualitative to Quantitative AOP: A Case Study of Neurodegeneration

Abstract

Adverse outcome pathways (AOPs) include a sequence of events that connect a molecular-level initiating event with an adverse outcome at the cellular level for human health endpoints, or at the population level for ecological endpoints. When there is enough quantitative understanding of the relationships between key events in an AOP, a mathematical model may be developed to connect key events in a quantitative AOP (qAOP). Ideally, a qAOP will reduce the time and resources spent for chemical toxicity testing and risk assessment and enable the extrapolation of data collected at the molecular-level by in vitro assays, for example, to predict whether an adverse outcome may occur. Here, we review AOPs in the AOPWiki, an AOP repository, to determine best practices that would facilitate conversion from AOP to qAOP. Then, focusing on a particular case study, acetylcholinesterase inhibition leading to neurodegeneration, we describe specific methods and challenges. Examples of challenges include the availability and collection of quantitative data amenable to model development, the lack of studies that measure multiple key events, and model accessibility or transferability across platforms. We conclude with recommendations for improving key event and key event relationship descriptions in the AOPWiki that facilitate the transition of qualitative AOPs to qAOPs.

Adverse outcome pathways for engineered systems

“…It has evolved to include networks of AOPs, which capture broader knowledge and are likely to be the functional unit of prediction for most real-life scenarios [8]. Ideally, AOPs would evolve into quantitative AOPs (qAOPs) when there is enough quantitative understanding of the KERs, allowing for a faster, less expensive way to predict AOs [9]. While regulators are still considering the most useful ways to use AOPs, there have been a few success stories.…”