Application of Trial Simulation in the Design of a Prospective Study for Concentration-QTc Analysis in Support of a Thorough QT Study Waiver

AAPS J. 2020 Aug 2;22(5):101. doi: 10.1208/s12248-020-00488-3.

Abstract

The concentration-QTc (C-QTc) analysis is often applied in the first-in-human (FIH) study to demonstrate the absence of a QTc effect in support of a TQT waiver. However, a C-QTc analysis without properly designed sensitivity could fail to conclude the absence of a QTc effect at high concentrations, even though the compound is QTc negative. This is because the 90% confidence interval (CI) of the model-derived ∆∆QTc grows wider with increasing concentration, and the upper-bound could cross the 10-ms threshold, even though the slope is close to 0. So far, there is no simple math formula to calculate the sensitivity/specificity of a C-QTc analysis. A PK/QTc trial simulation scheme was applied to optimize the design features of a C-QTc trial in FIH studies by evaluating the study's sensitivity over a wide concentration range, circumventing the problem of not knowing the target concentration during FIH studies. It was also used to ensure that the specificity of the trial was well-controlled. Simulation showed that the study sensitivity can be quantitatively gauged by optimizing the dose range, the number of samples per subjects or subject number, and by sampling around Tmax, and at steady-state. The specificity of the trial can also be evaluated with this approach, and it is important to combine model-derived ∆∆QTc and slope estimate in the evaluation. The trial simulation approach helps maximize the probability of success of C-QTc analyses in FIH studies intended to support a TQT waiver.

Keywords: Concentration-QTc (C-QTc) analysis; Probability of success; Study design optimization; Thorough QT waiver; Trial simulation.

MeSH terms

  • Arrhythmias, Cardiac / chemically induced*
  • Dose-Response Relationship, Drug*
  • Drug Evaluation / methods*
  • Humans
  • Models, Theoretical*
  • Prospective Studies
  • Research Design
  • Sensitivity and Specificity