Parkinson Joanna, Dota Corina, Rekić Dinko
Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
Cardiovascular Safety Center of Excellence and SKGs, Global Patient Safety, Oncology R&D, AstraZeneca, Gothenburg, Sweden.
J Pharmacokinet Pharmacodyn. 2025 Jul 26;52(4):43. doi: 10.1007/s10928-025-09981-8.
Concentration-QTc (C-QTc) analysis is a model-based method widely used to assess the impact of drugs on QT interval duration. C-QTc modelling was enabled to be used after the publication of the International Council for Harmonisation (ICH) E14 Questions and Answers guidance document in 2015, followed by the Scientific White Paper on C-QTc modelling (Garnett et al. J Pharmacokinet Pharmacodyn 45(3):383-397 2018), which included technical details and recommendations on how to perform and report the modelling. This hands-on tutorial aims to provide a practical implementation of the recommended C-QTc modelling methodology, including R code to perform the complete analysis, from data formatting to model predictions. The target audience is scientists who will perform C-QTc analyses. The tutorial uses real data from a previously published QT study by (Johannesen et al.Clin Pharmacol Ther 96(5):549-558 2014), focusing on two active treatments (dofetilide and verapamil) and placebo to illustrate positive and negative QT signals. The methodology implemented in this tutorial follows the recommendations outlined in the White paper. This tutorial includes practical steps for preparing an analysis-ready dataset, conducting exploratory data analysis, fitting the linear mixed effects (LME) model, assessing model performance and estimating the upper limit of the two-sided 90% confidence interval (CI) of baseline and placebo-corrected QTc (ΔΔQTc). Reproducibility of this workflow is ensured through the use of pkgr to manage R packages. The R codes provided as part of this tutorial were successfully used for several projects within the AstraZeneca portfolio and accepted by health authorities as part of QTc submissions.
浓度-校正QT间期（C-QTc）分析是一种基于模型的方法，广泛用于评估药物对QT间期持续时间的影响。2015年国际协调理事会（ICH）E14问答指导文件发布后，C-QTc建模开始得以使用，随后又有关于C-QTc建模的科学白皮书（加尼特等人，《药代动力学与药效学杂志》45(3):383-397，2018年），其中包括关于如何进行和报告建模的技术细节与建议。本实践教程旨在提供推荐的C-QTc建模方法的实际应用，包括从数据格式化到模型预测进行完整分析的R代码。目标受众是将进行C-QTc分析的科学家。本教程使用了（约翰内森等人，《临床药理学与治疗学》96(5):549-558，2014年）之前发表的一项QT研究的真实数据，重点关注两种活性治疗药物（多非利特和维拉帕米）以及安慰剂，以说明阳性和阴性QT信号。本教程中实施的方法遵循了白皮书中概述的建议。本教程包括准备分析就绪数据集、进行探索性数据分析、拟合线性混合效应（LME）模型、评估模型性能以及估计基线和安慰剂校正QTc（ΔΔQTc）的双侧90%置信区间（CI）上限的实际步骤。通过使用pkgr来管理R包，确保了此工作流程的可重复性。作为本教程一部分提供的R代码已成功用于阿斯利康产品组合中的多个项目，并被卫生当局接受作为QTc提交内容的一部分。
