使用机器学习增强的阅读交叉方法进行基于生理学的动力学 (PBK) 建模,用于跨物种推断
Yaoxing Wu1, Gabriel Sinclair1, Raghavendhran Avanasi1
1Product Safety, Syngenta Crop Protection LLC, Greensboro NC 27409, USA.
Environment international
|June 10, 2024
概括
这项研究引入了一种新的机器学习方法,用于对杀剂 (如普罗比可纳) 的生理基动力学 (PBK) 建模,通过弥合数据差距和量化物种间差异,实现准确的无动物风险评估.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 风险评估 风险评估
背景情况:
- 传统的农业化学风险评估在量化物种间差异方面面临挑战.
- 基于生理学的动力学 (PBK) 建模提供了无动物替代方案,但需要体内数据来验证.
- 数据的有限可用性阻碍了对某些化学品的PBK模型的监管接受.
研究的目的:
- 在缺乏体内药理动力学数据的情况下,为普罗比可纳开发PBK模型.
- 整合阅读交叉方法与机器学习进行客观模拟选择.
- 通过使用动物替代方法完善农业化学品的风险评估策略.
主要方法:
- 开发了PBK模型,使用适合目的的横读方法与层次聚类 (机器学习) 相结合.
- 使用可纳作为源化学物质,使用埃波西可纳,特布可纳和特里亚迪梅作为适用性评估的目标化学物质.
- 整合了构成性安德罗斯坦受体 (CAR) /孕妇X受体 (PXR) 激活,以模拟PBK模型中的代谢自诱导.
主要成果:
- 使用机器学习增强的交叉阅读方法,确定了difenoconazole作为最适合的propiconazole模拟物.
- 使用difenoconazole小鼠模型作为模板构建了一个propiconazole小鼠PBK模型.
- 应用平行图方法来开发普罗皮科纳的老鼠和人类模型,使定量剂量计外推.
结论:
- 综合机器学习和PBK建模方法成功地解决了用于普罗比可纳风险评估的数据缺口.
- 这种方法促进了可重复和客观的模拟选择,推进了无动物安全评估.
- 开发的模型为了解皮可纳剂量测量的物种间差异提供了定量基础.
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