Bayesian Approach Enabled Objective Comparison of Multiple Human iPSC-derived Cardiomyocytes' Proarrhythmia Sensitivities.

Abstract

The one-size-fits-all approach has been the mainstream in medicine, and the well-defined standards support the development of safe and effective therapies for many years. Advancing technologies, however, enabled precision medicine to treat a targeted patient population (e.g., HER2+ cancer). In safety pharmacology, computational population modeling has been successfully applied in virtual clinical trials to predict drug-induced proarrhythmia risks against a wide range of pseudo cohorts. In the meantime, population modeling in safety pharmacology experiments has been challenging. Here, we used five commercially available human iPSC-derived cardiomyocytes growing in 384-well plates and analyzed the effects of ten potential proarrhythmic compounds with four concentrations on their calcium transients (CaTs). All the cell lines exhibited an expected elongation or shortening of calcium transient duration with various degrees. Depending on compounds inhibiting several ion channels, such as hERG, peak and late sodium and L-type calcium or IKs channels, some of the cell lines exhibited irregular, discontinuous beating that was not predicted by computational simulations. To analyze the shapes of CaTs and irregularities of beat patterns comprehensively, we defined six parameters to characterize compound-induced CaT waveform changes, successfully visualizing the similarities and differences in compound-induced proarrhythmic sensitivities of different cell lines. We applied Bayesian statistics to predict sample populations based on experimental data to overcome the limited number of experimental replicates in high-throughput assays. This process facilitated the principal component analysis to classify compound-induced sensitivities of cell lines objectively. Finally, the association of sensitivities in compound-induced changes between phenotypic parameters and ion channel inhibitions measured using patch clamp recording was analyzed. Successful ranking of compound-induced sensitivity of cell lines was validated by visual inspection of raw data. Author SummaryCardiac safety is one of the most stringent regulatory risk monitoring during drug development. Regulatory agencies, including the Food and Drug Administration (FDA), require drug developers to conduct thorough preclinical and clinical studies to evaluate drug-induced proarrhythmia risks. The CiPA (Comprehensive in vitro Proarrhythmia Assay) initiative led by the FDA validated the applications of human cardiomyocytes derived from induced pluripotent stem cells in proarrhythmia risk assessments. The assay can be cost effective, and use of high-throughput approaches enables scale-up analysis. However, limited diversity in cell lines to recapitulate heterogeneity of human patients are still lacking in the cardiac safety analysis. We applied various computational tools to maximize capacity for analyzing diverse response of commercially available five cell lines against reference chemical compounds. Limited number of experimental data made it difficult to predict similarities and differences in drug responses among different cell lines. By applying Bayesian approach, our analysis could intuitively grasp the probabilities of expecting different responses from different cell lines.