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However, there are no perfect biomarkers for cardiac safety, and the risk is high for killing drugs before their benefits are clearly evaluated.

Some years ago, the U.S. FDA and European Medicines Agency published their recommendations for ECG QT/QTc studies (Thorough QT studies, or TQT) to evaluate the proarrhythmic potential of non-antiarrhythmic drugs.¹ The basic assumption behind these recommendations is that the QT-interval is a sufficient biomarker for the arrhythmogenic potential of drugs.

This requires very accurate acquisition and analysis of the 12-lead surface ECG.

Manual challenges

Measuring small effects of drugs on cardiac polarization and repolarization in the surface ECG with the necessary statistical relevance is not easy. All TQT studies, therefore, have to prove that the applied procedure is capable of detecting the small drug-induced effect on the QT interval.

TQT studies typically run a positive control group and placebo group in combination with the group treated with the drug under evaluation. Currently, each ECG is evaluated manually by experienced staff, which requires a significant budget for QT studies. Although humans are very good at pattern recognition, a manual analysis of ECGs introduces statistical errors due to inter-reader and intra-reader variability.

This problem has been well known since the mid-1980s, when a systematic evaluation of cardiologist in comparison to computer algorithms was done in the CSE study.² Today, most ECG core labs are automatically tracking the variability introduced by their staff to ensure sufficient quality of the ECG analysis. Together with the positive and placebo control in TQT studies, this works well and with the necessary accuracy.

There have been several attempts to do the ECG analysis automatically, especially the QT measurement.^3,4 But so far, no one seems to have proven well enough the accuracy of fully automatic procedures in TQT studies to persuade the pharmaceutical industry to take the risk of submitting a fully automated analysis to the FDA.

The reason for this lack of proof is that before new methods can be applied in real cases, they must be proven with existing data to be capable of delivering the same accuracy compared with the reference of a fully manual approach.

Partnering seeds

To date, however, these data have not been available for academic or commercial research institutions. Only recently, the THEW initiative,⁵ a public/private partnership under the leadership of the University of Rochester and FDA, has made an attempt to fill this gap by publishing data and conducting further research on automatic analysis methods.

The THEW project is a very promising start, but to date it is not sufficient to compare fully automatic procedures against a real gold standard. A real gold standard would mean to have ECG data sets—free of any commercial conflict of interest—made available to the public, where the reference (the key to the results) is nonpublic and held by an independent organization, such as the FDA.

These data sets should contain at least three TQT studies (with placebo and positive control) analyzed by different core labs and a nonprofit organization to ensure high data quality. Public and private organizations could then run the data sets with their automatic measurement methods and send their analysis to the organization holding the key (i.e., the expected results). The methods would then be proven independently and free of commercial interest.