ProtoQSAR S.L., Calle Nicolás Copérnico 6, Parque Tecnológico de Valencia, 46980 Paterna, Spain.
Unidad de Hepatología Experimental, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Av. Fernando Abril Martorell 106, 46026 Valencia, Spain.
Int J Mol Sci. 2024 Oct 17;25(20):11154. doi: 10.3390/ijms252011154.
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.
化学风险评估的不断发展的格局越来越注重开发分层的、基于机制的方法，避免使用动物实验。在这种情况下，不良结局途径在评估各种类型的化学诱导毒性方面变得越来越重要。本文以肝脂肪变性为例，探讨了多种计算技术的应用，如结构-活性关系模型、定量结构-活性关系模型、读-写方法、组学数据分析和基于结构的方法，以填补不良结局途径网络中的数据空白。强调了每种技术的监管接受程度，我们研究了如何将这些方法整合起来，以提供对化学毒性的全面理解。本文强调了计算技术在毒理学中的变革性影响，提出了应用这些技术在不良结局途径网络中积累和填补数据空白的指南。这些指南可以应用于其他情况，推动毒理学风险评估领域的发展。
