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

AI Article Synopsis

  • The review focuses on the shift towards tiered, mechanistically based approaches in chemical risk assessment that minimize animal testing, highlighting the role of adverse outcome pathways in measuring chemical toxicity.
  • Using hepatic steatosis as an example, it discusses various computational techniques for addressing data deficiencies in adverse outcome pathways, including structure-activity relationship models and omics data analysis.
  • The review proposes guidelines for integrating these in silico methods to enhance regulatory acceptance and advance toxicological risk assessment beyond just hepatic steatosis.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11508863PMC
http://dx.doi.org/10.3390/ijms252011154DOI Listing

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