Scientific white paper on concentration-QTc modeling

The QT effects of five "QT-positive" and one negative drug were tested to evaluate whether exposure-response analysis can detect QT effects in a small study with healthy subjects. Each drug was given to nine subjects (six for placebo) in two dose levels; positive drugs were chosen to cause 10 to 12 ms and 15 to 20 ms QTcF prolongation. The slope of the concentration/ΔQTc effect was significantly positive for ondansetron, quinine, dolasetron, moxifloxacin, and dofetilide. For the lower dose, an effect above 10 ms could not be excluded, i.e., the upper bound of the confidence interval for the predicted mean ΔΔQTcF effect was above 10 ms. For the negative drug, levocetirizine, a ΔΔQTcF effect above 10 ms was excluded at 6-fold the therapeutic dose. The study provides evidence that robust QT assessment in early-phase clinical studies can replace the thorough QT study. Show more

This White Paper, written collaboratively by members of the Cardiac Safety Research Consortium from academia, industry, and regulatory agencies, discusses different methods to characterize the QT effects for drugs that have a substantial direct or indirect effect on heart rate. Descriptions and applications are provided for individualized QT-R-R correction, Holter bin, dynamic QT beat-to-beat, pharmacokinetic-pharmacodynamic modeling, and QT assessment at constant heart rate. Most of these techniques are optimally performed using continuous electrocardiogram data obtained in clinical studies designed to characterize a drug's effect on the QT interval. An important study design element is the collection of drug-free data over a range of heart rates seen on treatment. The range of heart rates is increased at baseline by using ambulatory electrocardiogram recordings in addition to those collected under semisupine, resting conditions. Discussions in this study summarize areas of emerging consensus and other areas in which consensus remains elusive and provide suggestions for additional research to further increase our knowledge and understanding of this topic. Copyright © 2012 Mosby, Inc. All rights reserved. Show more

An increasing number of thorough QT (TQT) reports are being submitted to the US Food and Drug Administration's interdisciplinary review team for QT (IRT-QT), requiring time-intensive quantitative analyses by a multidisciplinary review team within 45 days. This calls for systematic learning to guide future trials and policies by standardizing and automating the QT analyses to improve review efficiency, provide consistent advice, and enable pooled data analyses to answer key regulatory questions. The QT interval represents the time from initiation of ventricular depolarization to completion of ventricular repolarization recorded by electrocardiograph (ECG) and is used in the proarrhythmic risk assessment. The developed QT knowledge management system is implemented in the R package "QT." Data from 11 crossover TQT studies including time-matched ECGs and pharmacokinetic measurements following single doses of 400 to 1200 mg moxifloxacin were used for the QT analysis example. The automated workflow was divided into 3 components (data management, analysis, and archival). The generated data sets, scripts, tables, and graphs are automatically stored in a queryable repository and summarized in an analysis report. More than 100 TQT studies have been analyzed using the system since 2007. This has dramatically reduced the time needed to review TQT studies and has made the IRT-QT reviews consistent across reviewers. Furthermore, the system enables leveraging prior knowledge through pooled data analyses to answer policy-related questions and to understand the various effects that influence study results. Show more