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集中度-QTc建模的实用指南:一个动手的教程

Joanna Parkinson¹, Corina Dota², Dinko Rekić^3,4

¹Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden. Joanna.parkinson@astrazeneca.com.

Journal of pharmacokinetics and pharmacodynamics

|July 27, 2025

概括

本教程提供了用于度-QTc (C-QTc) 分析的实用R代码,这是评估药物对QT间隔影响的关键方法. 它详细介绍了准备数据,建模和预测,以准确评估药物安全性.

科学领域:

药理动力学和药理动力学
临床药理学临床药理学
生物统计学生物统计学

背景情况:

度-QTc (C-QTc) 分析是一种基于标准模型的方法,用于评估药物对QT间隔持续时间的影响.
国际协调理事会 (ICH) E14指南和科学白皮书建立了C-QTc建模方法.
实用实施指南和可重复的R代码对于科学家进行这些分析至关重要.

研究的目的:

为推C-QTc建模的实际实施提供实践教程.
为完整的C-QTc分析工作流提供R代码,从数据格式化到模型预测.
用现实世界的数据来说明方法,使用活性治疗和安慰剂.

主要方法:

使用R代码进行数据准备,探索性数据分析和线性混合效应 (LME) 模型拟合.
根据科学白皮书推的C-QTc方法的实施.
估计基线和安慰剂校正的QTc (ΔΔQTc) 的90%置信区间的上限.

主要成果:

该教程展示了使用真实QT研究数据进行C-QTc分析的可重现工作流.
提供的 R 代码有助于进行完整的分析,包括模型性能评估.
该工作流已在制药项目中成功应用,并被监管机构接受.

结论:

本教程为C-QTc分析提供了一个实用指南和可重复的R代码,支持药物安全性评估.
该方法确保准确估计药物诱导的QT间隔变化.
工作流被验证并接受监管提交,帮助科学家进行C-QTc分析.

关键词:

在C-QTc建模中.暴露/反应建模药理动力学/药理动力学在 QT 评估中,QT 评估

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