From Causal Networks to Adverse Outcome Pathways: A Developmental Neurotoxicity Case Study

Abstract

The last decade has seen the adverse outcome pathways (AOP) framework become one of the most powerful tools in chemical risk assessment, but the development of new AOPs remains a slow and manually intensive process. Here, we present a faster approach for AOP generation, based on manually curated causal toxicological networks. As a case study, we took a recently published zebrafish developmental neurotoxicity network, which contains causally connected molecular events leading to neuropathologies, and developed two new adverse outcome pathways: Inhibition of Fyna (Src family tyrosine kinase A) leading to increased mortality via decreased eye size (AOP 399 on AOP-Wiki) and GSK3beta (Glycogen synthase kinase 3 beta) inactivation leading to increased mortality via defects in developing inner ear (AOP 410). The approach consists of an automatic separation of the toxicological network into candidate AOPs, filtering the AOPs according to available evidence and length as well as manual development of new AOPs and weight-of-evidence evaluation. The semiautomatic approach described here provides a new opportunity for fast and straightforward AOP development based on large network resources.

An integrative systems biology strategy to support the development of adverse outcome pathways (AOPs): a case study on radiation-induced microcephaly

“…Despite the existence of guidelines, ensuring the proper development of an AOP remains a time-consuming manual process, essential for establishing a comprehensive set of biological evidence. To accelerate this process, there is a need for automated approaches (Ramšak et al, 2022). This systems biology approach aligns with this requirement by enabling the collection and analysis of a large volume of data from diverse Frontiers in Cell and Developmental Biology frontiersin.org sources, supporting the development of AOPs and, consequently, New Approaches Methodologies (NAMs) in toxicology.…”

Harnessing Computational Methods to Characterize Chemical Impacts on Biodiversity

“…Additionally, based on conserved key events or biological mechanisms, AOPs can be extrapolated across species as well. While the number of AOPs is still relatively small to describe the full spectrum of biological effects across all species (459 AOPs as of January 2024), there have been numerous calls for computational methods to develop AOPs, 51,52 suggesting the number will increase in the future (there were only 274 AOPs in January 2020 43 ). ML-based methods can be used by integrating existing chemical toxicity test data for different species to predict chemical impacts both for new chemicals and new species.…”