Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.

Data de publicació: 17/10/2024 Data Ahead of Print: 17/10/2024

Autors de IIS La Fe

• Laia Tolosa Pardo

  Autor

Autors aliens a IIS La Fe

• Ortega-Vallbona R
• Palomino-Schätzlein M
• Benfenati E
• Ecker GF
• Gozalbes R
• Serrano-Candelas E

Grups d'Investigació

• Hepatología experimental y Trasplante hepático

  Investigador Principal

  Laia Tolosa Pardo

• Unidad Mixta de Investigación en Hepatología Experimental

  

  Investigador Principal

  Ramiro Jover Atienza

Abstract

The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure-activity relationship models, quantitative structure-activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks. Emphasizing the regulatory acceptance of each technique, we examine how these methodologies can be integrated to provide a comprehensive understanding of chemical toxicity. This review highlights the transformative impact of in silico techniques in toxicology, proposing guidelines for their application in evidence gathering for developing and filling data gaps in adverse outcome pathway networks. These guidelines can be applied to other cases, advancing the field of toxicological risk assessment.

Keywords

• adverse outcome pathway; computational toxicity; hepatic steatosis; molecular mechanisms; new generation risk assessment