试管中的化学命运:用于基于细胞的测试的生理生物动力学 (PBK) 模型
Daniela Brenner1, Kévin Bernal2, Eliška Sychrová1
1RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic.
ALTEX
|January 8, 2026
概括
一个新的计算模型,INSIGHT,通过预测细胞内的化学命运来改善体外毒理学测试. 这增强了实验设计和定量 in vitro-to-in vivo 推断,以更好地评估风险.
科学领域:
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
- 药理动力学 药理动力学
背景情况:
- 在体外毒理学测试对于预测体内结果和理解毒性反应至关重要.
- 准确地描述化学物质暴露和细胞系统中的命运对于可靠的体外试验至关重要.
- 当前的方法往往缺乏足够的细节来描述测试系统中的化学行为.
研究的目的:
- 开发一种新的in silico模型,INSIGHT (In Silico Guide for Harmonized in vitro Testing),用于更好地表征化学命运在体外.
- 整合生理和物理化学参数以指导体外测定设计.
- 增强量化体外到体外抽取 (qIVIVE) 和下一代风险评估 (NGRA) 工作流程.
主要方法:
- 通过整合基于虚拟细胞的试验模型和虚拟体内分布模型,开发了INSIGHT模型.
- 使用广泛的文献数据和原始实验结果对模型进行校准.
- 利用了42种化学物质和7种不同的细胞系 (HepaRG, HepG2, 3T3 Balb/c, PC12, MCF-7, RTgill-W1, HEK293) 的数据,其来源和容量各不相同.
主要成果:
- 当代谢和运输参数得到信息时,INSIGHT模型在多个细胞系中展示了灵活性和稳健性.
- 准确确定logPow,分区系数,透性和代谢过程对于计算细胞间线变异性至关重要.
- 该模型成功地将动态细胞过程与化学分离相结合.
结论:
- INSIGHT提供了一种强大的工具,用于协调体外测试,并改善细胞系统中化学行为的预测.
- 准确的参数化是模型在处理化学和细胞系变异性方面的成功的关键.
- 这种方法显著推进了qIVIVE,并通过提供对体外毒理学的更有机理性的理解来加强NGRA.
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