Characterization of a high throughput human stem cell cardiomyocyte assay to predict drug-induced changes in clinical electrocardiogram parameters

Human induced pluripotent stem cell derived cardiomyocytes (hIPSC-CM's) play an increasingly important role in the safety profiling of candidate drugs. For such models to have utility a clear understanding of clinical translation is required. In the present study we examined the ability of our hIPSC-CM model to predict the clinically observed effects of a diverse set of compounds on several electrocardiogram endpoints, including changes in QT and QRS intervals. To achieve this, compounds were profiled in a novel high throughput voltage sensitive dye platform. Measurements were taken acutely (30 min) and chronically (24 h) to ensure that responses from compounds with slow onset kinetics or that affected surface ion channel expression would be captured. In addition, to avoid issues associated with changes in free drug levels due to protein binding, assays were run in serum free conditions. Changes in hIPSC-CM threshold APD90 values correlated with compound plasma exposures that produced a +10 ms change in clinical QTc (Pearson r(2) = 0.80). In addition, randomForest modeling showed high predictivity in defining TdP risk (AUROC value = 0.938). Risk associated with QRS prolongation correlated with an increase in action potential rise-time (AUROC value = 0.982). The in-depth understanding of the clinical translatability of our hIPSC-CM model positions this assay to play a key role in defining cardiac risk early in drug development. Moreover, the ability to perform longer term studies enables the detection of compounds that may not be highlighted by more acute assay formats, such as inhibitors of hERG trafficking.

Automated summary

This study examined the ability of hIPSC-CM model to predict clinically observed effects of various compounds on electrocardiogram endpoints. Measurements were taken acutely and chronically to capture responses from compounds with slow onset kinetics. The study showed high predictivity in defining TdP risk and correlation between QRS prolongation risk and an increase in action potential rise-time.