Institut National de Recherche et de Sécurité Pour la Prévention des Accidents du Travail et des Maladies Professionnelles (INRS), Dept Toxicologie et Biométrologie, 1 Rue du Morvan, 54519, Vandoeuvre-lès-Nancy, France.
Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, Strasbourg, France.
Regul Toxicol Pharmacol. 2024 May;149:105623. doi: 10.1016/j.yrtph.2024.105623. Epub 2024 Apr 15.
The Bone-Marrow derived Dendritic Cell (BMDC) test is a promising assay for identifying sensitizing chemicals based on the 3Rs (Replace, Reduce, Refine) principle. This study expanded the BMDC benchmarking to various in vitro, in chemico, and in silico assays targeting different key events (KE) in the skin sensitization pathway, using common substances datasets. Additionally, a Quantitative Structure-Activity Relationship (QSAR) model was developed to predict the BMDC test outcomes for sensitizing or non-sensitizing chemicals. The modeling workflow involved ISIDA (In Silico Design and Data Analysis) molecular fragment descriptors and the SVM (Support Vector Machine) machine-learning method. The BMDC model's performance was at least comparable to that of all ECVAM-validated models regardless of the KE considered. Compared with other tests targeting KE3, related to dendritic cell activation, BMDC assay was shown to have higher balanced accuracy and sensitivity concerning both the Local Lymph Node Assay (LLNA) and human labels, providing additional evidence for its reliability. The consensus QSAR model exhibits promising results, correlating well with observed sensitization potential. Integrated into a publicly available web service, the BMDC-based QSAR model may serve as a cost-effective and rapid alternative to lab experiments, providing preliminary screening for sensitization potential, compound prioritization, optimization and risk assessment.
骨髓来源树突状细胞（BMDC）测试是一种有前途的方法，可根据 3R（替代、减少、优化）原则识别致敏化学物质。本研究使用常见物质数据集，将 BMDC 基准测试扩展到针对不同关键事件（KE）的各种体外、化学计算和计算方法，以评估不同关键事件（KE）。此外，还开发了一种定量构效关系（QSAR）模型，用于预测 BMDC 测试对致敏或非致敏化学物质的结果。建模工作流程涉及 ISIDA（计算机设计和数据分析）分子片段描述符和 SVM（支持向量机）机器学习方法。无论考虑的 KE 如何，BMDC 模型的性能至少与所有 ECVAM 验证模型相当。与针对 KE3（与树突状细胞激活相关）的其他测试相比，BMDC 试验显示出更高的平衡准确性和敏感性，与局部淋巴结试验（LLNA）和人类标签都相关，这为其可靠性提供了额外的证据。共识 QSAR 模型显示出有希望的结果，与观察到的致敏潜力很好地相关。集成到公共可用的网络服务中，基于 BMDC 的 QSAR 模型可以作为实验室实验的经济高效且快速的替代方法，提供致敏潜力的初步筛选、化合物优先级排序、优化和风险评估。
