Assessing proarrhythmic potential of environmental chemicals using a high throughput in vitro-in silico model with human induced pluripotent stem cell-derived cardiomyocytes

Abstract

QT prolongation and the potentially fatal arrhythmia Torsades de Pointes are common causes for withdrawing or restricting drugs; however, little is known about similar liabilities of environmental chemicals. Current in vitro-in silico models for testing proarrhythmic liabilities, using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM), provide an opportunity to address this data gap. These methods are still low- to medium-throughput and not suitable for testing the tens of thousands of chemicals in commerce. We hypothesized that combining high-throughput population- based in vitro testing in hiPSC-CMs with a fully in silico data analysis workflow can offer sensitive and specific predictions of proarrhythmic potential. We calibrated the model with a published hiPSC-CM dataset of drugs known to be positive or negative for proarrhythmia and tested its performance using internal cross-validation and external validation. Additionally, we used computational down-sampling to examine three study designs for hiPSC-CM data: one replicate of one donor, five replicates of one donor, and one replicate of a population of five donors. We found that the population of five donors had the best performance for predicting proarrhythmic potential. The resulting model was then applied to predict the proarrhythmic potential of environmental chemicals, additionally characterizing risk through margin of exposure (MOE) calculations. Out of over 900 environmental chemicals tested, over 150 were predicted to have proarrhythmic potential, but only seven chemicals had a MOE < 1. We conclude that a high-throughput in vitro-in silico approach using population-based hiPSC-CM testing provides a reasonable strategy to screen environmental chemicals for proarrhythmic potential.

Keywords: environmental chemicals; human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM); in vitro-in vivo extrapolation (IVIVE); proarrhythmic potential; ventricular arrhythmia Torsade de Pointes (TdP).

Fig. 1.

Schmatic of (A) overall conceptual model and (B) model development workflow

Fig. 2.

Concentration-response modeling of representative chemicals for a given phenotype using QT prolongation (i.e., increasing decay-rise ratio) as example, based on three study designs with (Design A) single replicate of a single donor, (Design B) five replicates of a single donor, and (Design C) single replicates of population of five separate donors. The gray line represents the median estimate of concentration-response. The different colors of points represent different donors.

Fig. 3.

Logistic regression model calibration based on the study design with single replicates of population of five separate donors (Design C). (A) Results of curve fitting, (B) relationships among thresholds, sensitivity, specificity, true and geometric accuracy, (C) ROC curve and (D) predicted probability of positive proarrhythmic potential for drugs in Dataset I with the threshold (red dash line) determined by the maximum geometric accuracy. The red color points represent the drugs considered with positive QT/TdP risk category based on CredibleMeds database, and the blue ones the drugs considered negative. The solid points represent correct predictions and the open points are incorrect predictions.

Fig. 4.

Bar charts comparing the sensitivity, specificity and AUC of ROC among previous studies (Blinova et al. 2018) and the three study designs (Design A: single replicate of single donor, Design B: five replicates of single donor)-(Design C: single replicates of five donors) predicting proarrhythmic potential in training dataset (Dataset I), internal cross-validation (Dataset I), and external validation (Dataset II).

Fig. 5.

Bar charts summarizing the number of environmental chemicals (A: Dataset I, B: Dataset II), by chemical class, predicted to be positive or negative for proarrhythmic potential. The numbers above bars represent the number of environmental chemicals predicted to be positive for proarrhythmic potential out of total number of each chemical class.

Fig. 6.

Margin of exposure (MOE) for chemicals predicted to have proarrhythmic potential under Design C, based on the most sensitive cardio phenotype and median and upper-confidence bound exposure estimates. Chemicals are separated by chemical class and ordered from lowest to highest MOE in each class. The detailed information of chemical name, CAS number, and classes is listed in Table S4. The color band gradients show the regions where exposure is considered “unsafe” (MOE < 1), of “potential concern” (MOE between 1 and 100) and “safe” (MOE > 100) exposure, from darker to lighter yellow shades.