优化治疗药物监测:机器学习模型的作用
Hamza Sayadi1, Yeleen Fromage2, Marc Labriffe1,3
1Department of Pharmacology, Toxicology and Pharmacovigilance, Dupuytren University Hospital (CHU Dupuytren), Limoges, France.
Expert review of clinical pharmacology
|December 29, 2025
概括
机器学习 (ML) 通过克服传统的局限性来增强治疗药物监测 (TDM). 机器学习模型预测药物暴露和优化剂量,为个性化药物治疗铺平道路.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 临床药房 临床药房
背景情况:
- 传统的治疗药物监测 (TDM) 具有准确性和适应性限制,阻碍了复杂患者的最佳治疗.
- 具有狭窄治疗指数的药物需要精确的剂量来最大限度地提高疗效并最大限度地降低毒性.
- 机器学习 (ML) 提供了一种数据驱动的方法来改善TDM结果.
研究的目的:
- 审查ML在TDM中的演变和应用.
- 探索基本的ML模型来预测药物暴露.
- 讨论先进的ML应用程序,如第一剂量优化和混合模型.
主要方法:
- 文献综述综合了ML在TDM中的进展情况.
- 使用现实世界或模拟数据分析基本的ML模型.
- 检查将药物动力学框架与ML集成的混合模型.
主要成果:
- 机器学习模型可以从稀疏的数据中预测药物暴露.
- 扩展ML技术,以主动优化第一剂量.
- 混合模型将生理解释性与ML的纠正能力相结合.
结论:
- TDM的未来涉及到ML与机械模型的融合.
- 临床翻译需要解决数据访问,可解释性和工作流集成方面的挑战.
- 基于机器学习的工具,包括数字双胞胎,可以带来主动的,个性化的剂量作为标准的护理.
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