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ADPO:自动区分辅助的参数优化

Rong Chen1, Mark Sale2, Alex Mazur2

  • 1Data Sciences Software Division, Certara Inc., 4 Radnor Corporate Center, Suite 350, Radnor, 19087, PA, USA. rong.chen@certara.com.

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|September 22, 2025
PubMed
概括
此摘要是机器生成的。

自动分化 (AD) 现在在城NLME 8.6中,显著加快了药理动力学/药理动力学 (PK/PD) 模型分析. 这种新方法,即自动区分辅助参数优化 (ADPO),与传统方法相比,可减少20-50%的计算时间.

关键词:
自动差异化自动差异化一个双数的数字.一个人的脸,一个人的脸.有限差异 有限差异.在PK/PD中,PK/PD是最常见的.参数估计的参数估计.

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科学领域:

  • 制药指标 (Pharmacometrics) 是一个指标.
  • 计算科学 计算科学
  • 机器学习 机器学习

背景情况:

  • 准确的导数计算对于非线性混合效应建模至关重要.
  • 像有限差的传统方法可以是计算密集的.
  • 尼克斯NLME是用于药理动力学/药理动力学 (PK/PD) 分析的广泛使用的软件.

研究的目的:

  • 在Phoenix NLME中引入自动差异化 (AD) 进行增强的参数优化.
  • 与传统的有限差异方法相比,评估基于AD的方法的性能和效率.
  • 评估AD对各种人口PK/PD建模算法的影响.

主要方法:

  • 在Phoenix NLME 8.6.6中实现了AD作为"自动区分辅助参数优化" (ADPO).
  • 应用ADPO到一阶条件估计扩展最小方程 (FOCE ELS),拉普拉斯和自适应高斯方程 (AGQ) 算法.
  • 使用四种PK/PD模型和各种ODE解决器对有限差异 (FD) 方法进行基准ADPO.

主要成果:

  • ADPO和传统的FOCE ELS (使用FD) 证明了可比的准确性和稳定性.
  • 在所有测试的ODE解决方案中,ADPO显著减少了计算时间,一般为20%至50%.
  • 一个使用voriconazole模型的具体案例显示,使用ADPO和"自动检测"ODE解决器,运行时间减少了95%.

结论:

  • 在城NLME中,ADPO为PK/PD建模提供了相当大的计算优势.
  • 支持ADPO的"快速优化"选项为传统梯度计算方法提供了更有效的替代方案.
  • 应用AD代表了加速复杂的药理动力学和药理动力学分析的重大进展.