Quantitative Comparison of Effects of Dofetilide, Sotalol, Quinidine, and Verapamil between Human Ex vivo Trabeculae and In silico Ventricular Models Incorporating Inter-Individual Action Potential Variability

Abstract

Background:In silico modeling could soon become a mainstream method of pro-arrhythmic risk assessment in drug development. However, a lack of human-specific data and appropriate modeling techniques has previously prevented quantitative comparison of drug effects between in silico models and recordings from human cardiac preparations. Here, we directly compare changes in repolarization biomarkers caused by dofetilide, dl-sotalol, quinidine, and verapamil, between in silico populations of human ventricular cell models and ex vivo human ventricular trabeculae. Methods and Results:Ex vivo recordings from human ventricular trabeculae in control conditions were used to develop populations of in silico human ventricular cell models that integrated intra- and inter-individual variability in action potential (AP) biomarker values. Models were based on the O'Hara-Rudy ventricular cardiomyocyte model, but integrated experimental AP variability through variation in underlying ionic conductances. Changes to AP duration, triangulation and early after-depolarization occurrence from application of the four drugs at multiple concentrations and pacing frequencies were compared between simulations and experiments. To assess the impact of variability in IC50 measurements, and the effects of including state-dependent drug binding dynamics, each drug simulation was repeated with two different IC50 datasets, and with both the original O'Hara-Rudy hERG model and a recently published state-dependent model of hERG and hERG block. For the selective hERG blockers dofetilide and sotalol, simulation predictions of AP prolongation and repolarization abnormality occurrence showed overall good agreement with experiments. However, for multichannel blockers quinidine and verapamil, simulations were not in agreement with experiments across all IC50 datasets and I_Kr block models tested. Quinidine simulations resulted in overprolonged APs and high incidence of repolarization abnormalities, which were not observed in experiments. Verapamil simulations showed substantial AP prolongation while experiments showed mild AP shortening. Conclusions: Results for dofetilide and sotalol show good agreement between experiments and simulations for selective compounds, however lack of agreement from simulations of quinidine and verapamil suggest further work is needed to understand the more complex electrophysiological effects of these multichannel blocking drugs.

Keywords: cardiac modeling; dofetilide; quinidine; safety pharmacology; sotalol; verapamil.

Figure 1

Variability of APD30/50/90 values under control conditions for ventricular trabeculae from different donor hearts. Each dot indicates mean results of 30 action potentials from one trabeculae, each row represents data from a different donor heart. Top: 1 Hz pacing. Bottom: 2 Hz pacing.

Figure 2

Distributions of experimental and model biomarkers. Normalized histograms and probability density estimates for biomarkers from all trabeculae (green, n = 89) and from all populations of models (blue, n = 860) under control conditions 1 Hz pacing.

Figure 3

AP traces of models in each heart-specific population of models. AP traces for each heart-specific population of models, and trace from the ORd baseline model for reference. 2/16 hearts did not have any of the 20,000 candidate models in range for all biomarkers and therefore had no accepted models, so are excluded from the figure. 860 out of the 20,000 candidate models were accepted into at least one population.

Figure 4

Calibration of heart-specific populations of models using biomarker ranges. Values from all individual trabeculae (colored dots–each color corresponds to a donor heart), and for all models accepted into any population (gray dots) are shown for APD90 vs. triangulation, two of the five biomarkers used to construct the populations, which are also biomarkers of drug-induced pro-arrhythmic risk.

Figure 5

Dofetilide. Changes to APD90 and triangulation relative to control from application of 0.01 and 0.1 μM dofetilide during 1 and 2 Hz pacing. In each panel, response is shown for (left to right): human ventricular trabeculae, populations of models using drug effects calculated using data from Crumb et al. from Kramer et al. and from use of the hERG model by Li et al. As Crumb and Kramer datasets both measured only hERG IC50s for dofetilide, unlike the other tested compounds, there is only one result from use of the dynamic model. Dots indicate results from individual trabeculae and models, crosses show the result from the baseline ORd model. Red symbols indicate simulations and experiments where repolarization abnormalities occurred.

Figure 6

Sotalol. Changes to APD90 and triangulation relative to control from application of 10 and 100 μM sotalol during 1 and 2 Hz pacing. In each panel, response is shown for (left to right): human ventricular trabeculae, populations of models using drug effects calculated using data from Crumb et al. from Crumb et al. with I_Kr replaced by the Li et al. I_Kr model; from Kramer et al. and from Kramer et al. with I_Kr replaced by the Li et al. I_Kr model. Dots indicate results from individual trabeculae and models, crosses show the result from the baseline ORd model.

Figure 7

Quinidine. Changes to APD90 and triangulation relative to control from application of 1 and 10 μM quinidine during 1 and 2 Hz pacing. In each panel, response is shown for (left to right): human ventricular trabeculae, populations of models using drug effects calculated using data from Crumb et al. from Crumb et al. with I_Kr replaced by the Li et al. I_Kr model; from Kramer et al.; and from Kramer et al. with I_Kr replaced by the Li et al. I_Kr model. Dots indicate results from individual trabeculae and models, crosses show the result from the baseline ORd model. Red symbols indicate simulations and experiments where repolarization abnormalities occurred.

Figure 8

Verapamil. Changes to APD90 and triangulation relative to control from application of 0.1 and 1 μM verapamil during 1 and 2 Hz pacing. In each panel, response is shown for (left to right): human ventricular trabeculae, populations of models using drug effects calculated using data from Crumb et al.; from Crumb et al. with I_Kr replaced by the Li et al. I_Kr model; from Kramer et al. and from Kramer et al. with I_Kr replaced by the Li et al. I_Kr model. Dots indicate results from individual trabeculae and models, crosses show the result from the baseline ORd model.

Figure 9

Summary of average difference in mean and standard deviation between experiment and simulation for ΔAPD90 and ΔTriangulation. Absolute differences in mean (top) and standard deviation (bottom) between experiments and simulations for ΔAPD90 and ΔTriangulation are shown for dofetilide (left) and sotalol (right), for each drug block dataset. Differences in mean are shown for both the mean of the populations of models (dark blue) and the single biomarker value produced by the ORd baseline model (light blue), while differences in standard deviation can only be shown for the populations of models. Values for each drug block dataset are averaged across all concentrations and frequencies used in this study. Results for dofetilide show only one block dataset for the dynamic hERG model as neither Crumb nor Kramer IC50 datasets contained non-hERG IC50s for dofetilide (for sotalol, Kramer et al. measured an IC50 for Cav 1.2).