基于模型的深度Q网络以指导儿童克朗氏病的因弗力西马布剂量
Kei Irie1, Phillip Minar2,3, Jack Reifenberg4
1Division of Translational and Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Clinical pharmacology and therapeutics
|November 5, 2025
概括
这项研究引入了深度Q网络 (DQN),用于克罗恩病的个性化因弗利克西马布剂量,自动化模型知情精度剂量 (MIPD). 人工智能模型成功优化了药物疗法,改善了目标实现和现实世界的低谷水平.
科学领域:
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 基于模型的精确剂量 (MIPD) 优化了使用药物动力学/药物动力学 (PK/PD) 模型的药物治疗.
- 传统的MIPD方法是手动的,耗时的,需要专门的专业知识.
- 强化学习 (RL) 提供了一个可扩展的,自动化的方法来优化剂量决策.
研究的目的:
- 开发和评估一个基于模型的深度Q网络 (DQN),用于克罗恩病的个性化因弗利西马布剂量.
- 为了实现MIPD的自动化和提高MIPD的效率,用于Infliximab治疗.
- 评估使用DQN用于个性化剂量策略的可行性.
主要方法:
- 一个DQN在一个模拟环境中训练了一个人群PK模型,个体间的可变性和试验误差.
- 虚拟患者被用来探索在各种输液点的剂量策略.
- 奖励功能优先考虑目标度的最低值,同时惩罚过度处理和额外的输液.
主要成果:
- 在虚拟患者中,DQN政策实现了高目标实现概率 (92.9%在输注4,98.4%在输注5).
- 高剂量 (11-20 mg/kg) 很少被选择 (0.2%的病例).
- 现实世界的回顾性验证表明,DQN的建议导致了接近目标范围的低谷水平.
结论:
- 基于DQN的药物可以有效地为克罗恩病患者个性化Infliximab剂量.
- 这种方法增强和自动化MIPD,可能改善治疗结果.
- 这些发现支持使用人工智能来优化儿科患者个性化药物治疗.
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