最后,贝叶斯的方法超过了对busulfan精确剂量的非分隔分析
Jasmine H Hughes1, Janel Long-Boyle2,3, Ron J Keizer4
1InsightRX, 548 Market St. #88083, San Francisco, CA, 94104, USA. jasmine@insight-rx.com.
对于像busulfan这样的药物来说,剂量个性化是关键. 后期最大贝叶斯 (MAP) 方法显示,在优化药物剂量方面,模拟目标实现率高于非分区分析 (NCA).
科学领域:
- 药理动力学和药理动力学
- 计算生物学和生物信息学
- 临床药理学 临床药理学
背景情况:
- 剂量个性化提高了患者对治疗指数狭窄和高个体间可变性药物的治疗结果,例如busulfan.
- 非分组分析 (NCA) 和最大后期贝叶斯式 (MAP) 方法是优化药物剂量的标准方法.
- 这些方法如何估计患者特异性药理动力学参数及其对剂量优化影响的差异尚未完全理解.
研究的目的:
- 为了比较NCA和MAP方法在估计药理动力学参数和实现目标药物暴露中的性能,使用busulfan作为模型.
- 评估不同假设和数据处理对度-时间曲线 (AUC) 估计下的面积的影响.
- 通过使用NCA和MAP方法来评估剂量调整的模拟目标实现率.
主要方法:
- 来自246名患者的布苏尔方药理学数据的回顾性分析.
- 对比NCA (有和没有峰值延伸) 和MAP贝叶斯估计 (使用单间舒克拉和双间隔麦肯模型).
- 布兰德-阿尔特曼分析,以达成对现实数据的共识,并模拟剂量调整以实现目标.
主要成果:
- 所有方法都与真实数据有很好的一致性 (相关系数为0.945-0.998).
- 与随后的间隔相比,NCA和MAP之间的协议在第一个剂量间隔期间更高.
- 模拟剂量调整显示,MAP (91-93%) 与NCA (63-66%) 的真实目标实现率更高,尽管两者都估计了高实现率 (>98%).
结论:
- 虽然AUC估计与NCA和MAP之间的相关性很好,但MAP贝叶斯估计导致了优异的模拟目标实现.
- 估计AUC的差异受到输液阶段度曲线和处理时间依赖的清除的假设的影响.
- 在切换估计方法或改变输注时间等参数时,可能需要调整目标暴露水平.
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