整合机器学习与药理动力学模型:科学机器学习在现有药理动力学模型中添加神经网络组件方面的好处
Diego Valderrama1,2, Ana Victoria Ponce-Bobadilla3, Sven Mensing3
1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
CPT: pharmacometrics & systems pharmacology
|October 16, 2023
概括
本研究介绍了用于可解释药理动力学 (PK) 建模的科学机器学习 (SciML) 框架. 这种方法可以准确地预测药物度和参数,即使吸收复杂且数据稀疏.
科学领域:
- 药理动力学 药理动力学
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 机器学习 (ML) 模型越来越多地用于药理动力学 (PK) 建模.
- 当前的ML方法往往充当"黑子",限制可解释性.
- 将机械学知识集成到用于 PK 建模的 ML 中仍然是一个挑战.
研究的目的:
- 开发一个可解释的科学机器学习 (SciML) 框架用于 PK 建模.
- 将机械学知识整合到用于PK分析的ML模型中.
- 使用SciML准确预测药物度和PK参数.
主要方法:
- 在SciML框架内使用一个单间PK模型.
- 使用神经网络来学习未知的药物吸收参数.
- 同时估计药物分发和消除参数.
- 用不同的采样策略生成模拟PK数据.
主要成果:
- 该SciML模型准确地预测了额外推算任务中的药物度.
- 该模型证明了对新剂量方案和患者的可靠预测.
- 即使使用稀疏的数据和复杂的吸收配置文件,也可以实现准确的预测.
- 该模型保留了古典隔间模型的解释性.
结论:
- 将生理结构集成到SciML模型中可以提高PK预测的准确性.
- SciML为复杂的PK建模提供了一个强大的,可解释的方法.
- 这一框架推动了在药物开发和个性化医疗领域的ML应用.
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