模拟临床试验以获得基于模型的精确剂量:使用华法林治疗作为一个用例
David Augustin1, Ben Lambert2, Martin Robinson1
1Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Frontiers in pharmacology
|November 6, 2023
概括
基于模型的精确剂量 (MIPD) 框架可以优化药物治疗方案. 药理动力学和药理动力学 (PKPD) 建模表明,在个性化华法林剂量方面,成功率最高,在治疗INR范围内达到75.1%的患者.
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
- 临床试验 临床试验
背景情况:
- 患者对治疗的反应有很大差异,需要对某些药物 (如化疗) 进行个性化剂量.
- 基于模型的精确剂量 (MIPD) 为量身定制药物疗法和减轻不良事件提供了一个有前途的方法.
- 现有的MIPD方法,包括回归,强化学习 (RL) 和药理动力学/药理动力学 (PKPD) 建模,缺乏统一的比较框架.
研究的目的:
- 开发和利用一个新的框架来模拟临床MIPD试验.
- 评估和比较三种不同的MIPD方法的有效性:神经网络回归,深度RL和PKPD建模.
- 评估每个MIPD方法的优点和局限性,以实现成功的治疗个性化.
主要方法:
- 开发一个模拟框架,模拟MIPD试验测试的临床复杂性.
- 该框架适用于华法林剂量,这是一个具有狭窄治疗指数的常见抗凝剂.
- 对神经网络回归,深度RL和PKPD建模进行比较分析,以预测最佳剂量策略.
主要成果:
- 在保持治疗INR方面,PKPD模型实现了最高的成功率 (75.1%),治疗范围 (TTR) 中最高的中位时间 (TTR) 为74%.
- 回归和深度RL模型显示较低的成功率 (分别为47.0%和65.8%) 和中位数TTR (45%和68%).
- PKPD和深度RL模型通过结合超出共同变量解释变量的监测数据,显示出更大的个性化,PKPD还进一步完善了预测个性化.
结论:
- PKPD 建模是一种非常成功和高效的 MIPD 方法,用于个性化华法林剂量.
- 开发的模拟框架为成本效益高的评估和推进MIPD战略提供了宝贵的工具.
- 不同的MIPD方法提供了不同程度的个性化,强调了基于治疗目标的模型选择的重要性.
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