稀疏的药物动力学生物等价性研究的基于模型的生物等价性方法:模型选择还是模型平均?
Morgane Philipp1, Adrien Tessier2, Mark Donnelly3
1Université Paris Cité, IAME, INSERM, Paris, France.
模型选择和模型平均化改善了基于模型的生物等价性 (BE) 测试,用于稀疏的药理动力学 (PK) 数据. 这些方法可以控制统计错误,并在药物吸收研究中保持高强度.
科学领域:
- 药理动力学 药理动力学
- 药物开发 药物开发
- 统计建模 统计建模
背景情况:
- 生物等价性 (BE) 研究评估药物产品的等价性.
- 像非分区分析 (NCA) 这样的传统方法对于稀疏的数据具有挑战性.
- 推基于模型 (MB) 的方法,但风险是由于模型错误规范导致的I型错误.
研究的目的:
- 将基于模型的BE (MBBE) 研究与稀疏的PK采样进行模型选择 (MS) 和模型平均 (MA) 的比较.
- 用MS和MA来评估MB-TOST的性能,而不是使用单一指定的模型.
主要方法:
- 从双向交叉BE研究设计中模拟的药理动力学 (PK) 数据.
- 应用基于模型的双单面测试 (MB-TOST) 使用候选模型,MS和MA.
- 在零和替代假设下评估的I型错误率和统计能力.
主要成果:
- 模型选择和模型平均控制的I型错误率在0.05或以下.
- 与使用真实PK模型相比,这些方法实现了类似或更高的统计能力.
- 即使真实模型不在候选人池中,性能也很强.
结论:
- 建议在MB-TOST之前对具有稀疏PK数据的BE研究进行模型选择.
- 当候选模型表现出类似的Akaike信息标准值时,建议采用模型平均值.
- 这些方法提高了复杂药物配方的BE评估的可靠性.
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