Methods Inf Med 2004; 43(05): 445-450
DOI: 10.1055/s-0038-1633895
Original Article
Schattauer GmbH

Correcting the QT Interval for Changes in HR in Pre-clinical Drug Development

M. Meyners
1   Department of Medical Data Services, Biostatistics Group, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
,
M. Markert
2   Department of Drug Discovery Support, General Pharmacology Group, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
05 February 2018 (online)

Summary

Objectives: Estimation of possible cardiovascular side effects belongs to the safety assessment of every drug candidate. Drug-induced prolongation of the QT interval can result in life-threatening ventricular arrhythmia. In pre-clinical drug development, animal experiments are used to study this possible effect. Researchers have become aware that correction formulae derived for human beings are not applicable to animal experiments.

Methods: We investigated some of the proposed models by comparing the outcomes of the analyses on the same data. The data was derived from telemetry measurements on Labrador dogs. We propose the use of both the correlation with heart rate (or RR interval) and a measure of predictive performance. As a sufficiently large number of observations were available, the data was subdivided into a training and a test set. The training set serves to estimate the respective parameters while the test set is used to determine the performance of the model. Here, a kind of PRESS statistic was used. Next, the models were considered for treated animals, using the estimated parameters. Both positive and negative controls were used.

Conclusions: Most models under consideration performed quite well. These models eliminated the correlation for the most part and were reasonably predictive. Furthermore, they reliably differentiate between positive and negative controls. The next steps in identifying the best correction will be to consider additional compounds as well as other species to validate our current results.

 
  • References

  • 1 Jackman WM, Friday KJ, Anderson JL, Aliot EM, Clark M, Lazzara R. The long QT syndromes: a critical review, new clinical observations and a unifying hypothesis. Prog Cardiovasc Dis 1988; 31: 115-72.
  • 2 Schwartz PJ, Locati E, Napolitano C, Priori SG. The long QT-syndrome. In Zipes DP, Jaliffe J. (eds) Cardiac electrophysiology: From cell to bedside. 2nd ed. Philadelphia: Saunders; 1995: 788-811.
  • 3 Bazett JC. An analysis of time relations of electrocardiograms. Heart 1920; 7: 353-67.
  • 4 Fridericia LS. Die Systolendauer im Elektrokardiogramm bei normalen Menschen und bei Herzkranken. Acta Med Scand 1920; 53: 469-86.
  • 5 Valensi PE, Johnson NB, Maison-Blanche P, Extramania F, Motte G, Coumel P. Influence of cardiac autonomic neuropathy on heart rate dependence of ventricular repolarization in diabetic patients. Diabetes Care 2002; 25: 918-23.
  • 6 Davey P. How to correct the QT interval for the effects of heart rate in clinical studies. J Pharmacol Toxicol Meth 2002; 48: 3-9.
  • 7 Rautaharju PM, Zhang ZM. Linearly scaled, rateinvariant normal limits for QT interval: Eight decades of incorrect application of power functions. J Cardiovasc Electrophysiol 2002; 13: 1211-8.
  • 8 Batey AJ, Doe CPA. A method for QT correction based on beat-to-beat analysis of the QT/RR interval relationship in conscious telemetred beagle dogs. J Pharmacol Toxicol Meth 2002; 48: 11-9.
  • 9 Matsunaga T, Mitsui T, Harada T, Inokuma M, Murano H, Shibutani Y. QT corrected for heart rate and relation between QT and RR intervals in beagle dogs. J Pharmacol Toxicol Meth 1997; 38: 201-9.
  • 10 Puddu PE, Jouve R, Mariotti S, Giampaoli S, Lanti M, Reale A, Menotti A. Evaluation of 10 QT prediction formulas in 881 middle aged men from the seven countries study: emphasis on the cubic root Fridericia’s equation. J Electrocardiol 1988; 21: 219-29.
  • 11 Raunig D, De Pasquale MJ, Huang CH, Winslow R, Fossa AA. Statistical analysis of QT interval as a function of changes in RR interval in the conscious dog. J Pharmacol Toxicol Meth 2001; 46: 1-11.
  • 12 Markert M, Klumpp A, Trautmann T, Guth B. A novel propellant-free inhalation drug delivery system for cardiovascular drug safety evaluation in conscious dogs. J Pharmacol Toxicol Meth 2004; 50: 109-19.
  • 13 ICH S7B Draft Consensus Guideline: The Nonclinical Evaluation of the Potential for Delayed Ventricular Repolarization (QT Interval Prolongation) by Human Pharmaceuticals. Available at. http://www.ich.org/MediaServer.jser?@_ID=505&@_MODE=GLB
  • 14 Sarma JSM, Sarma RJ, Bilitch M, Katz D, Song SL. An exponential formula for heart rate dependence of QT interval during exercise and cardiac pacing in humans: reevaluation of Bazett’s formula. Am J Cardiol 1984; 54: 103-8.
  • 15 Malik M, Färbom P, Batchvarov V, Hnatkova K, Camm AJ. Relation between QT and RR intervals is highly individual among healthy subjects: implications for the heart rate correction of the QT interval. Heart 2002; 87: 220-8.
  • 16 Dmitrienko A, Smith B. Repeated-measures models in the analysis of QT interval. Pharm Stat 2003; 2: 175-90.