关于使用深模型对药物度进行可靠和可解释的预测的诱导偏差
Alexander Janssen1, Frank C Bennis2,3, Marjon H Cnossen4
1Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. a.janssen@amsterdamumc.nl.
Journal of pharmacokinetics and pharmacodynamics
|March 27, 2024
概括
生理上受约束的深模型 (DCM) 提高了药物度预测的稳定性,特别是在有限的数据的情况下. 多分支网络可以提高药物动力学建模的准确性和可解释性.
科学领域:
- 药理动力学和机器学习
- 计算药理学计算药理学
- 药物开发 药物开发
背景情况:
- 传统的药理动力学 (PK) 模型包含了诱导偏差,以实现现实的药物度预测.
- 将这些偏见集成到标准的神经网络中具有挑战性,但对于强度和性能至关重要.
- 深层隔间模型 (DCM) 提供了一个使用神经网络进行PK建模的框架.
研究的目的:
- 通过生理约束来增强深层隔间模型 (DCM) 架构,以实现更强大,更现实的药理动力学预测.
- 引入多分支网络,以改善 PK 分析中的协变效应建模和可解释性.
- 评估受约束的DCM与标准DCM和现有人口PK模型的性能.
主要方法:
- 通过将生理现实主义纳入建筑中,开发了一个受约束的深模型 (DCM).
- 使用模拟研究来评估约束对模型稳定性的影响,特别是在稀疏数据场景中.
- 提出并实施了多分支网络架构,以隔离协变效应并提高模型的可解释性.
- 使用现实数据集,将受约束模型的预测性能与标准DCM和传统的人口PK模型进行了比较.
主要成果:
- 生理约束在稀疏的数据设置中显著提高了模型稳定性,并导致了更现实的度-时间曲线.
- 多分支网络架构有效地减少了虚假的共同变量效应,并实现了学习函数的可视化,提高了可解释性.
- 约束模型在现实数据集上显示出比标准DCM更高的预测精度.
- 多分支制约网络在预测血友病A患者的药物度方面表现优于以前的人口PK模型.
结论:
- 基于生理学的约束有效地提高了基于深度学习的药物动力学模型的稳定性和现实性.
- 拟议的可解释的多分支网络架构增强了信任,并促进了机器学习在临床实践中的采用.
- 受约束的DCM代表了在临床环境中准确可靠的药物度预测的有希望的进步.
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