Geraldine Celliere1, Andreas Krause1, Guillaume Bonnefois, Jonathan Chauvin1
1Simulations Plus, CPP division
Introduction: The assessment of proarrhythmic risk via the analysis of QT interval prolongation is an integral part of non-antiarrhythmic drug development. Since the publication of the International Council for Harmonisation (ICH) E14 Questions and Answers guidance in 2015, QT interval prolongation assessment can be carried out with a concentration-QTc modeling approach as part of single- or multiple- dose escalation studies, instead of conducting a thorough QT/QTc study [1]. The scientific white paper by Garnett et al. [2] provides technical details for how to perform and report concentration-QTc modeling to support regulatory submissions. Their recommendations include a pre-specified linear mixed-effects model and a set of diagnostic plots to include in the report. This pre-specification provides the opportunity to automate the workflow. In this work, we present an R package which can perform a concentration-QTc analysis in an automated way from data preparation to full report generation. Methods: The automated workflow is implemented using R (for data preparation), MonolixSuite (for parameter estimation of nonlinear mixed-effect models accessed via R using the lixoftConnectors), and Quarto (for reporting). In order to implement it in Monolix, the linear model from the white paper is reformulated in a pharmacometrics context with a structural model and a statistical model (random effects and covariate effects). In addition to the linear concentration-QTc relationship, other non-linear relationships are implemented, including loglinear, Emax, Emax with sigmoidicity, and delayed-effect models based on an effect compartment. The workflow includes three main steps corresponding to three separate R functions: (1) Data preparation: depending on the columns already present in the input dataset and the study design, the function computes QTc (using Fridericia, Bazett, Framingham or Hodges correction), QTc baseline, baseline-corrected QTc (?QTc), baseline- and placebo-corrected QTc (??QTc), and average QTc over triplicates. The resulting formatted dataset also contains columns required for model implementation, such as time as a factor (categorical covariate), centered baseline as a continuous covariate, and drug concentration as an independent variable (called regressor in Monolix). (2) Concentration-QTc analysis via mixed-effects modeling: using the formatted dataset and a set of concentration-QTc models (linear, loglinear, Emax, Emax with sigmoidicity, delayed effect, or user-defined), Monolix projects are created and run to estimate the model parameters and, in particular, the relationship between concentration and ?QTc or ??QTc. (3) Reporting: the report generation is based on a Quarto template which includes the exploratory data analysis plots suggested in the white paper, a comparison of the different tested models, model diagnostic plots, and the 90% prediction interval for ??QTc. Standard text to interpret the results is also provided. The Quarto template can be modified by the user to customize or include additional elements. The proposed workflow and R functions are applied to several real and simulated datasets: the crossover datasets analyzed in Johannesen et al. [3-4], the simulated dataset from the white paper supplement which displays a non-linear concentration-QTc relationship [2], Vanoxerine with placebo and two different dose groups [5-6], and simulated data for parallel designs and designs with time-matched or single time point baseline records. Results: The R functions are able to handle the diverse designs and situations present in the tested real and simulated datasets. Only a few lines of R code are needed to create the report from the input dataset. Using numerical goodness of fit gives an objective measure for comparing several models to better capture the relationship between drug concentration and ??QTc, even in situations where exploratory data visualization does not show clear violations of assumptions required to apply the standard linear model. Conclusion: These findings demonstrate that the concentration-QTc R package provides a viable automated solution for QTc liability assessment. The associated time savings gives scientists the opportunity to focus on the interpretation of results rather than on the details of analysis implementation. The R functions can be freely downloaded from [7].
[1] ICH E14 Guideline (2015) The clinical evaluation of QT/QTc interval prolongation and proarrhythmic potential for non-antiarrhythmic drugs. Questions & answers (R3). [2] Garnett C, Bonate PL, Dang Q et al (2018) Scientific white paper on concentration-QTc modeling. J Pharmacokinet Pharmacodyn 45:383–397 [3] Johannesen L, Vicente J, Mason JW et al (2014) Differentiating drug-induced multichannel block on the electrocardiogram: randomized study of dofetilide, quinidine, ranolazine, and verapamil. Clin Pharmacol Ther 96:549–558. [4] Johannesen L, Vicente J, Mason JW et al. (2014) ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine 1.0.0. Data set to Johannesen et al (2014). [5] Preti A. Vanoxerine National Institute on Drug Abuse. Curr Opin Investig Drugs. 2000 Oct;1(2):241-51. [6] https://datashare.nida.nih.gov/study/nida-cpu-0002 [7] Simulations Plus, Inc. R package for concentration-QT analysis. https://monolixsuite.slp-software.com/r-functions/2024R1/package-for-conc-qtc-analysis
Reference: PAGE 33 (2025) Abstr 11367 [www.page-meeting.org/?abstract=11367]
Poster: Methodology - Other topics