An optimized adverse outcome pathway network for chemical-induced liver steatosis.

Verhoeven, A. (Speaker)
Jonas van Ertvelde (Contributor)
Boeckmans, J. (Contributor)
Gatzios, A. (Contributor)
Ramiro Jover (Contributor)
Birgitte Lindeman (Contributor)
Graciela Lopez (Contributor)
Marcelino Rodrigues, R. (Contributor)
Anna Rapisarda (Contributor)
Sanz Serrano, J. (Contributor)
Stinckens, M. (Contributor)
Sepehri, S. (Contributor)
Vinken, M. (Contributor)
Jiang, J. (Contributor)
Vanhaecke, T. (Contributor)

Activity: Talk or presentation › Talk or presentation at a conference

Description

The field of chemical toxicity testing is shifting to address the limitations of traditional in vivo studies. This transition focuses on implementing innovative non-animal methods to enhance predictability and provide deeper insights into toxicity mechanisms. Adverse Outcome Pathway (AOP) networks play a crucial role in structuring existing mechanistic knowledge related to toxicological processes. However, AOP networks are continuously evolving and need frequent updates to integrate new data. In addition, regulatory challenges remain, primarily due to concerns about the reliability of the information these
networks provide.This study introduces a Weight-of-Evidence (WoE) scoring method, aligning with the tailored BradfordHill criteria, to quantitatively assess the confidence in key event relationships (KERs) within AOP networks. The existing AOP network was optimized with the latest scientific knowledge extracted from PubMed using the SysRev platform for artificial intelligence-based abstract inclusion and standardized data collection. The resulting optimized AOP network, constructed using Cytoscape, visually represents confidence levels through node size (key event, KE) and edge thickness (KERs). The analysis of 173 research papers resulted in 100 unique KEs and 221 KERs among which 72 KEs and 170 KERs, respectively, have not been previously documented in the prior AOP network or AOP-wiki. Notably, modifications in de novo lipogenesis, fatty acid uptake and mitochondrial beta-oxidation, leading to lipid accumulation and liver steatosis, obtained the highest KER confidence scores.
In conclusion, this study delivers a generic methodology for developing and assessing AOP networks. The quantitative WoE scoring method facilitates in assessing the level of support for KERs within the
optimized AOP network, providing valuable insights for its application in scientific research and regulatory settings

Period	29 Nov 2024
Event title	OpenTox Virtual Conference 2024
Event type	Conference