对多重流细胞测试和毒基因组学特征用于基因毒性预测的分析:模型性能和案例研究方法
Tomás Lagunas1, Fjodor Melnikov1, Gabby Cole1
1Department of Translational Safety, Genentech, Inc., South San Francisco, California, USA.
Environmental and molecular mutagenesis
|July 23, 2025
概括
机器学习模型使用MultiFlow和MicroFlow试验准确预测基因毒性及其作用机制. 整合毒基因组学进一步完善这些预测,提高药物安全性评估.
科学领域:
- 生物医学科学 生物医学科学
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 基因毒性测试在药物开发中至关重要,以评估癌症风险.
- 传统的测试提供有限的机械细节,需要先进的方法.
研究的目的:
- 评估机器学习 (ML) 模型的基因毒性和作用机制 (MoA) 预测.
- 使用MultiFlow和MicroFlow试验数据比较ML模型性能.
- 整合毒基因组数据,以提高对基因毒性机制的理解.
主要方法:
- 实施的ML模型与内部的MultiFlow和MicroFlowDNA损伤测定数据.
- 收集和分析剂量反应数据,以改善测试推断.
- 利用毒基因组数据 (TGx-DDI,RNA-seq) 进行复杂化合物的案例研究.
主要成果:
- ML模型实现了高准确性:96%的MoA和99%的基因毒性预测.
- 剂量反应分析改善了测试推断和模型精度.
- 毒基因组学整合为基因毒性途径提供了更深入的机制性见解.
结论:
- 与MultiFlow和MicroFlow测定集成的ML模型显著改善了基因毒性预测.
- 毒基因组数据集成为详细的基因毒性机制阐明提供了强大的框架.
- 这种方法可以提高药物开发过程中安全性评估的准确性.
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