在机器学习中使用的方法技术,以支持基于药理动力学数据的个性化药物剂量方案:范围审查
Janthima Methaneethorn1, Khanita Duangchaemkarn2, Brad Reisfeld3,4
1Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand. janthima.methaneethorn@gmail.com.
Clinical pharmacokinetics
|August 14, 2025
概括
机器学习 (ML) 模型对个性化药物剂量有希望,性能与传统的药理动力学模型一样好或更好. 标准化ML方法对于临床使用至关重要.
科学领域:
- 药理动力学和药物开发
- 计算生物学和生物信息学
- 人工智能在医学中的应用
背景情况:
- 个性化药物剂量优化了具有高个体间变异性的药物的治疗结果.
- 种群药动力学 (PPK) 建模是标准的,但劳动密集型,需要专业知识.
- 机器学习 (ML) 为个性化药物剂量提供了一个有希望的替代方案.
研究的目的:
- 进行基于ML的药物动力学建模的范围审查,以优化剂量.
- 检查ML模型在这个领域的方法和预测性能.
- 确定目前的个人化药物剂量的ML应用程序中的差距.
主要方法:
- 在2025年5月之前,对五个数据库进行系统搜索.
- 包括对药物度或参数的ML和PPK模型预测进行比较的研究.
- 排除非英语研究,评论,协议和不使用ML进行个性化剂量的研究.
主要成果:
- 包括58项研究,增强,基于树的,基于实例的和回归模型是常见的ML方法.
- 31%的研究将ML与PPK模型集成;其他研究使用独立的ML模型.
- ML模型的预测准确性与PPK模型相比或更高,特别是对于具有高药理动力学变异性的药物.
结论:
- 在ML建模方法,特征选择和评估方法中存在实质性的异质性.
- 报告和方法的标准化对于ML模型的可重复性至关重要.
- 加强标准化将提高ML模型在个性化药物剂量的临床适用性.
相关概念视频
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