种群药动力学模型中的间偶变性:可识别性,影响性,相互依赖性和衍生研究设计建议
Emily Behrens1, Sebastian G Wicha2
1Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Bundesstraße 45, 20146, Hamburg, Germany.
Journal of pharmacokinetics and pharmacodynamics
|April 11, 2025
概括
在稀疏的药理动力学研究中建模间场变性 (IOV) 是复杂的. 这种模拟发现,包括更多的场合和低谷样本可以改善参数估计和IOV检测,这对于准确的临床试验设计至关重要.
科学领域:
- 药物指标 (Pharmacometrics) 是一个指标.
- 临床药理学 临床药理学
- 统计建模 统计建模
背景情况:
- 在药代动力学 (PK) 参数中建模间场变性 (IOV) 存在挑战,特别是在稀疏的研究设计中.
- 了解IOV的影响对于准确的药物开发和临床试验设计至关重要.
研究的目的:
- 评估IOV对PK参数估计的影响,使用随机模拟和估计 (SSE).
- 在临床试验中确定IOV检测所需的最小样本大小.
- 评估忽视IOV对参数估计和度-时间曲线 (AUC) 计算下的面积的影响.
主要方法:
- 使用随机模拟和估计 (SSE) 进行了一项模拟研究.
- 模拟了25%的间隔变化 (IOV) 和75%的变化系数 (CV).
- 两种采样方案 (有和没有谷底样本) 在多个场合进行了比较.
主要成果:
- 检测IOV的功率随着事件数量 (OCC) 的增加而增加,而I型错误率仍然可以接受.
- 包括低谷样本在各种评估中显著改善了性能.
- 参数估计在更多的OCC和更高的IOV效应大小下更精确.
- 忽视真实IOV导致了参数估计的大量偏差和不准确性,特别是对于个体间的变化和残余误差.
- 在三个OCC中,至少需要1050名患者才能在研究的场景中达到≥95%的功率.
- 没有考虑IOV的错误指定的模型导致扭曲的AUC分布.
结论:
- 在稀疏的设计中,准确地建模间场变异性对于可靠的药物动力学参数估计至关重要.
- 增加病例数和纳入低谷样品可以提高IOV的检测和特征.
- 如果不能建模IOV,可能会显著影响参数估计的准确性和像AUC这样的衍生指标,可能会影响临床试验结果.
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