Correcting QT Correction: Correcting the QT Interval for RR in TQT Trials Potentially Causes Regulatory Overestimation of Drug Related QT Changes
Authors: Ayala‐Lopez Nadia , McGuinn W. David 
Published: June 2021

The FDA requires thorough QT clinical trials (TQT) of most new drugs to define their pharmacodynamic effect on the QT interval with the aim of reducing drug induced arrhythmias. The standard statistical method for TQTs applies a mathematical “correction” to the QT interval based (erroneously) on model equations derived by Bazett or Fridericia in 1920. The intent of QT correction is to negate the effect of the RR interval on QT and normalize QT to 60 BPM. The standard method systematically subtracts placebo and baseline QTc values from the QTc values of the treatment group to obtain ΔΔQTc at pharmacological Tmax. Nevertheless, the removal of RR from the relationship artificially decreases the variance of the values leading to incorrect sample sizes and the potential for type 1 and 2 errors. QT “correction” also artificially increases the values of QT particularly at smaller values of RR. This method also assumes that the drug has no important effects on RR; this is frequently not the case. Using publicly available data from an FDA sponsored trial, we reanalyzed QT and RR values from volunteers given dofetilide, verapamil, ranolazine, and quinidine in a simple linear mixed effect model. We excluded baseline values from the analysis as they provided no drug concentration information. We made no corrections to the QT interval, but rather included RR explicitly in the model allowing for interaction between RR and concentration. Volunteer ID was a random effect. The model is shown below.

To generate model estimates to demonstrate the drug effect at 60 bpm, the stated goal of QT correction, we set the value of RR = 1000 ms in the model solution. We set the value of concentration for the estimates to the maximum concentration for each volunteer to obtain the maximum difference from placebo. We analyzed each drug treatment separately then compared the results with those from the placebo group using Student's t‐test. The values in the table below are means among the volunteers. All values are in msec, all results were significant at p<0.05. The table compares our values to those of Johannesen et al. (2014), the original TQT analysis of the dataset. Column 2 shows that the drugs have a considerable effect on RR, contrary to the assumptions in the original analysis. Column 4 shows the actual values for QT at Tmax. Column 6 gives the ΔQT relative to placebo in our analysis. Column 8 shows that the standard TQT analysis recommended by the FDA consistently overestimates the drug induced change in QT. The exclusion of RR from QT analysis has obscured the role of that parameter in the QT interval. Drugs that change the QT interval may change the relationship between QT and RR. QT “correction” renders these changes invisible. Incorrect estimates of the pharmacodynamic effect of a drug on the QT interval may lead to misinformed regulatory or commercial decisions. We conclude that the statistical methods of the TQT studies should include RR explicitly in the model.

Model

urn:x-wiley:08926638:fsb2fasebj2018321supplementlb628:equation:fsb2fasebj2018321supplementlb628-math-0001

This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.