Use of high throughput ion channel profiling and statistical modeling to predict off-target arrhythmia risk - One pharma's experience and perspective.

INTRODUCTION: The use of high throughput patch clamp profiling to determine mixed ion channel-mediated arrhythmia risk was assessed using profiling data generated using proprietary internal and clinical reference compounds. We define the reproducibility of the platform and highlight inherent platform issues. The data generated was used to develop predictive models for cardiac arrhythmia risk, specifically Torsades de Pointes (TdP). METHODS: A retrospective analysis was performed using profiling data generated over a 3-year period, including patch clamp data from hERG, Cav1.2, and Nav1.5 (peak/late), together with hERG binding. RESULTS: Assay reproducibility was robust over the 3-year period examined. High throughput hERG patch IC50 values correlated well with GLP-hERG data (Pearson = 0.87). A disconnect between hERG binding and patch was observed for ∼10% compounds and trended with passive cellular permeability. hERG and Cav1.2 potency did not correlate for proprietary compounds, with more potent hERG compounds showing selectivity versus Cav1.2. For clinical compounds where hERG and Cav1.2 activity was more balanced, an analysis of TdP risk versus hERG/Cav1.2 ratio demonstrated low TdP probability when the hERG/Cav1.2 potency ratios were < 1. Modeling of clinical compound data revealed a lack of impact of the Nav1.5 (late) current in predicting TdP. Moreover, models using hERG binding data (ROC AUC = 0.876) showed an improved ability to predict TdP risk versus hERG patch clamp (ROC AUC = 0.787). DISCUSSION: The data highlight the value of high throughput patch clamp data in the prediction of TdP risk, as well as some potential limitations with this approach.

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 r2 = 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.