Shortening of the electromechanical window in the ketamine/xylazine-anesthetized guinea pig model to assess pro-arrhythmic risk in early drug development

Background: A negative electromechanical window (EMw) was recently proposed as a better preclinical tool than QTc interval to predict clinical pro-arrhythmic potential. As such, we utilized the ketamine/xylazine anesthetized guinea pig to characterize the EMw and QTc interval for a diverse set of reference agents with known clinical proarrhythmic potential. Then we determined the clinical proarrhythmia predictive capacity of EMw shortening compared to hERG inhibition or QTc interval prolongation alone. Methods: Changes in EMw and QTc interval by 26 reference agents were evaluated in the ketamine/xylazine-anesthetized guinea pig. Confusion matrix analysis using the hERG, QTc and EMw indexes (hERG IC50, QTc EC5 or the EMw EC-10 divided by their respective free therapeutic maximal plasma concentration) at various folds the therapeutic concentrations was conducted to assess the concordance of each index to predict clinical pro-arrhythmic risk. Results: Shortening of the EMw concomitant to an increase in QTc interval was observed in the GP with known pro-arrhythmic drugs. Non-torsadogenic compounds did not cause EMw shortening, although some prolonged the QTc interval. The preclinical: clinical concordance of the EMw index (88%) was similar (p > 0.05) to using QTc interval prolongation alone (85%) but significantly greater (p < 0.05) than using hERG inhibition alone (69%). In addition, the specificity when using the EMw(87%) was largely greater (p < 0.05) than using QTc interval (73%) or hERG inhibition (60%) alone. When the components of the response (duration of left ventricular pressure (LVP) cycle (QLVPend) or QT interval) that caused EMw shortening were considered, the concordance is further improved (>95%). Conclusion: EMw shortening improves QTc interval prolongation recording in early drug development and increases the translatability over existing preclinical tools in predicting clinical arrhythmias. (C) 2016 Elsevier Inc. All rights reserved.