ArticleJournal of pharmacokinetics and pharmacodynamics2025
Practical guide to concentration-QTc modeling: a hands-on tutorial.
Article in Journal of pharmacokinetics and pharmacodynamics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Concentration-QTc Analysis Supporting Thorough QT Study Waiver for Forvisirvat: Electrocardiogram Assay Sensitivity Demonstrated via Meal Effect.Journal of clinical pharmacology · 2026Trial
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Identifiers
40715879What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.