A quantitative weight-of-evidence method for confidence assessment of adverse outcome pathway networks: a case study on chemical-induced liver steatosis.

Autores de IIS La Fe

• 

  Ramiro Jover Atienza

  Autor

Participantes ajenos a IIS La Fe

• Verhoeven, Anouk
• van Ertvelde, Jonas
• Boeckmans, Joost
• Gatzios, Alexandra
• Lindeman, Birgitte
• Lopez-Soop, Graciela
• Rodrigues, Robim M.
• Rapisarda, Anna
• Sanz-Serrano, Julen
• Stinckens, Marth
• Sepehri, Sara
• Teunis, Marc
• Vinken, Mathieu
• Jiang, Jian
• Vanhaecke, Tamara

Grupos

• Hepatología experimental y Trasplante hepático

  Investigador Principal

  Laia Tolosa Pardo

• Unidad Mixta de Investigación en Hepatología Experimental

  

  Investigador Principal

  Ramiro Jover Atienza

Abstract

The field of chemical toxicity testing is undergoing a transition to overcome the limitations of in vivo experiments. This evolution involves implementing innovative non-animal approaches to improve predictability and provide a more precise understanding of toxicity mechanisms. Adverse outcome pathway (AOP) networks are pivotal in organizing existing mechanistic knowledge related to toxicological processes. However, these AOP networks are dynamic and require regular updates to incorporate the latest data. Regulatory challenges also persist due to concerns about the reliability of the information they offer. This study introduces a generic Weight-of-Evidence (WoE) scoring method, aligned with the tailored Bradford-Hill criteria, to quantitatively assess the confidence levels in key event relationships (KERs) within AOP networks. We use the previously published AOP network on chemical-induced liver steatosis, a prevalent form of human liver injury, as a case study. Initially, the existing AOP network is optimized with the latest scientific information extracted from PubMed using the free SysRev platform for artificial intelligence (AI)-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). Additionally, a Shiny application is developed to facilitate user interaction with the dataset, promoting future updates. Our analysis of 173 research papers yielded 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, garnered the highest KER confidence scores. In conclusion, our study delivers a generic methodology for developing and assessing AOP networks. The quantitative WoE scoring method facilitates in determining the level of support for KERs within the optimized AOP network, offering valuable insights into its utility in both scientific research and regulatory contexts. KERs supported by robust evidence represent promising candidates for inclusion in an in vitro test battery for reliably predicting chemical-induced liver steatosis within regulatory frameworks.

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Keywords

• Bradford-Hill criteria; adverse outcome pathway; artificial intelligence; chemical toxicity; steatosis; weight-of-evidence