在外部评估人口药理动力学模型时,样本大小,采样策略或处理低于量化下限的度是否重要?
Mehdi El Hassani1,2, Uwe Liebchen3, Amélie Marsot4,5
1Faculté de pharmacie, Université de Montréal, 2940 chemin de Polytechnique, Montréal, QC, H3T 1J4, Canada. mehdi.el.hassani@umontreal.ca.
对人口药动力学模型的外部评价 (EE) 表明大样本大小是不必要的. 使用治疗药物监测 (TDM) 开发的模型可能更具概括性,并且基于概率的处理量化极限以下的数据可以改善偏差.
科学领域:
- 药学指标 (Pharmacometrics) 是一个指标.
- 临床药理学 临床药理学
- 药物开发 药物开发
背景情况:
- 选择适当的种群药理动力学 (PK) 模型对于精确的剂量定量至关重要.
- 外部评估 (EEs) 评估模型的适用性,但影响结果的研究设计因素不明.
- 这影响了临床上最相关的PK模型的选择.
研究的目的:
- 为了评估样本大小,抽样策略和低于定量化下限 (BLQ) 数据处理对EE结果的影响.
- 评估四个PK模型,使用万科米辛和托布拉米辛的模拟.
- 为了为临床应用PK模型的选择提供信息.
主要方法:
- 模拟了三种虚拟患者群体进行万科米辛和托布拉米辛治疗.
- 多样化的样本大小和采样策略.
- 我们比较了三种BLQ数据处理方法:丢弃,归算 (LLOQ/2) 和基于概率的方法.
- 使用NONMEM和R软件进行了EEs.
主要成果:
- 样本大小对EE结果的影响最小.
- 每名患者的样本增加并没有持续改善预测性能.
- 使用丰富采样开发的模型没有超过使用定期治疗药物监测 (TDM) 开发的模型.
- 基于概率的BLQ数据处理减少了预测低谷度的偏差.
结论:
- 对于EE研究来说,样本大小可能不是必不可少的.
- 基于TDM数据选择的模型可能提供更好的概括性.
- 需要为临床实施PK模型的EE制定标准指南.
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