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Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative was proposed that integrates in vitro testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs. The TdP risk classification performance using only a single TdP metric may require some improvements because of information limitations and the instability of generalizing results. This study evaluates the performance of TdP metrics from the in silico simulations of the Tomek-O'Hara Rudy (ToR-ORd) ventricular cell model for classifying the TdP risk of drugs. We utilized these metrics as an input to an artificial neural network (ANN)-based classifier. The ANN model was optimized through hyperparameter tuning using the grid search (GS) method to find the optimal model. The study outcomes show an area under the curve (AUC) value of 0.979 for the high-risk category, 0.791 for the intermediate-risk category, and 0.937 for the low-risk category. Therefore, this study successfully demonstrates the capability of the ToR-ORd ventricular cell model in classifying the TdP risk into three risk categories, providing new insights into TdP risk prediction methods. [ABSTRACT FROM AUTHOR]

Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative was proposed that integrates in vitro testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs. The TdP risk classification performance using only a single TdP metric may require some improvements because of information limitations and the instability of generalizing results. This study evaluates the performance of TdP metrics from the in silico simulations of the Tomek–O'Hara Rudy (ToR–ORd) ventricular cell model for classifying the TdP risk of drugs. We utilized these metrics as an input to an artificial neural network (ANN)-based classifier. The ANN model was optimized through hyperparameter tuning using the grid search (GS) method to find the optimal model. The study outcomes show an area under the curve (AUC) value of 0.979 for the high-risk category, 0.791 for the intermediate-risk category, and 0.937 for the low-risk category. Therefore, this study successfully demonstrates the capability of the ToR–ORd ventricular cell model in classifying the TdP risk into three risk categories, providing new insights into TdP risk prediction methods.

Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of druginduced cardiotoxicity. Method: We generated a virtual population of human ventricular cell models using a modified O'Hara-Rudy model, allowing inter-individual variation. IC50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (dVm dt repol, dVm dt max, Vmpeak, Vmresting, APDtri, APD90, APD50, Capeak, Cadiastole, Catri, CaD90, CaD50, qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validationwhile training themodel with 42 drugs and evaluated the model's performance with test data from 25 drugs. Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908-0.937), sensitivity of 0.926 (0.909-0.942), specificity of 0.921 (0.906-0.935), and AUC score of 0.964 (0.954-0.975). Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system. [ABSTRACT FROM AUTHOR]

Background: An electrical storm of Torsade de Pointes arrhythmias (TdP) can be reproducibly induced in the anesthetized chronic AV-block (CAVB) dog by infusion of the IKr-blocker dofetilide. Earlier studies showed that these arrhythmias 1) arise from locations with high spatial dispersion in repolarization (SDR) and 2) can be suppressed by high-rate pacing. We examined whether suppression of TdP by high-rate pacing is established through a decrease in SDR in the CAVB dog.Methods: Dofetilide (25 μg/kg in 5 min) was administered to 5 anesthetized CAVB dogs to induce TdP arrhythmias. During the experiments, animals were continuously paced from the right ventricular apex at 50 beats/minute (RVA50). Upon TdP occurrence and conversion, RVA pacing was consecutively set to 100, 80 and 60 beats/minute for 2 min, referred to as pacing blocks. To determine the additional anti-arrhythmic effects of HRP over defibrillation alone, the number of arrhythmic events and SDR at RVA100 were compared to data from three previously conducted experiments, in which dogs underwent the same experimental protocol but were paced at RVA60 upon TdP occurrence (RVA60retro). In all experiments, recordings included surface electrocardiogram and mapping by 56 intramural needles, each recording four electrograms, evenly inserted into the ventricular walls and septum. For each pacing block, the number of ectopic beats (EB), and TdP severity were scored. SDR was quantified as the average difference in repolarization time within four squared needles (SDRcubic).Results: In 4 out of 5 animals, pacing at RVA100 suppressed TdP occurrence. One dog could not be converted by defibrillation after the initial TdP. Compared to RVA50, pacing at RVA100, but not RVA80 and RVA60, significantly reduced the TdP score (78 ± 33 vs. 0 ± 0, p < 0.05 and vs. 12.5 ± 25 and 25 ± 50, both p > 0.05). The reduction in TdP score was reflected by a significant decrease in SDRcubic (125 ± 46 ms before TdP vs. 49 ± 18 ms during RVA100, p < 0.05), and SDR was smaller than in the RVA60retro animals (101 ± 52 ms, p < 0.05 vs. RVA100).Conclusion: In CAVB dogs, high-rate pacing effectively suppresses TdP, which, at least in part, results from a spatial homogenization of cardiac repolarization, as reflected by a decrease in SDR.

Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity.Method: We generated a virtual population of human ventricular cell models using a modified O’Hara-Rudy model, allowing inter-individual variation. IC50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (dVmdtrepol, dVmdtmax, Vmpeak, Vmresting, APDtri, APD90, APD50, Capeak, Cadiastole, Catri, CaD90, CaD50, qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model’s performance with test data from 25 drugs.Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908–0.937), sensitivity of 0.926 (0.909–0.942), specificity of 0.921 (0.906–0.935), and AUC score of 0.964 (0.954–0.975).Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system.

Torsade de Pointes is a polymorphic ventricular tachycardia which is as yet incompletely understood. While the onset of a TdP episode is generally accepted to be caused by triggered activity, the mechanisms for the perpetuation is still under debate. In this study, we analysed data from 54 TdP episodes divided over 5 dogs (4 female, 1 male) with chronic atrioventricular block. Previous research on this dataset showed both reentry and triggered activity to perpetuate the arrhythmia. 13 of those TdP episodes showed reentry as part of the driving mechanism of perpetuating the episode. The remaining 41 episodes were purely ectopic. Reentry was the main mechanism in long-lasting episodes (>14 beats), while focal sources were responsible for maintaining shorter episodes. Building on these results, we re-analysed the data using directed graph mapping This program uses principles from network theory and a combination of positional data and local activation times to identify reentry loops and focal sources within the data. The results of this study are twofold. First, concerning reentry loops, we found that on average non-terminating (NT) episodes (≥10 s) show significantly more simultaneous reentry loops than self-terminating (ST) TdP (<10 s). Non-terminating episodes have on average 2.72 ± 1.48 simultaneous loops, compared to an average of 1.33 ± 0.66 for self-terminating episodes. In addition, each NT episode showed a presence of (bi-)ventricular loops between 10.10% and 69.62% of their total reentry duration. Compared to the ST episodes, only 1 in 4 episodes (25%) showed (bi-)ventricular reentry, lasting only 7.12% of its total reentry duration. This suggests that while focal beats trigger TdP, macro-reentry and multiple simultaneous localized reentries are the major drivers of long-lasting episodes. Second, using heatmaps, we found focal sources to occur in preferred locations, instead of being distributed randomly. This may have implications on treatment if such focal origins can be disabled reliably. [ABSTRACT FROM AUTHOR]

Torsade de Pointes is a polymorphic ventricular tachycardia which is as yet incompletely understood. While the onset of a TdP episode is generally accepted to be caused by triggered activity, the mechanisms for the perpetuation is still under debate. In this study, we analysed data from 54 TdP episodes divided over 5 dogs (4 female, 1 male) with chronic atrioventricular block. Previous research on this dataset showed both reentry and triggered activity to perpetuate the arrhythmia. 13 of those TdP episodes showed reentry as part of the driving mechanism of perpetuating the episode. The remaining 41 episodes were purely ectopic. Reentry was the main mechanism in long-lasting episodes (>14 beats), while focal sources were responsible for maintaining shorter episodes. Building on these results, we re-analysed the data using directed graph mapping This program uses principles from network theory and a combination of positional data and local activation times to identify reentry loops and focal sources within the data. The results of this study are twofold. First, concerning reentry loops, we found that on average non-terminating (NT) episodes (≥10 s) show significantly more simultaneous reentry loops than self-terminating (ST) TdP (

Introduction: Torsade de pointes arrhythmias (TdP) in the chronic atrioventricular block (CAVB) dog model result from proarrhythmic factors, which trigger TdP and/or reinforce the arrhythmic substrate. This study investigated electrophysiological and arrhythmogenic consequences of severe bradycardia for TdP. Methods: Dofetilide (25 μg/kg per 5 min) was administered to eight anesthetized, idioventricular rhythm (IVR) remodeled CAVB dogs in two serial experiments: once under 60 beats per minute (bpm), right ventricular apex paced (RVA60) conditions, once under more bradycardic IVR conditions. Recordings included surface electrocardiogram and short-term variability (STV) of repolarization from endocardial unipolar electrograms. TdP inducibility (three or more episodes within 10 min after start of dofetilide) and arrhythmic activity scores (AS) were established. Mapping experiments in 10 additional dogs determined the effect of lowering rate on STV and spatial dispersion of repolarization (SDR) in baseline. Results: IVR-tested animals had longer baseline RR-interval (1,403 ± 271 ms) and repolarization intervals than RVA60 animals. Dofetilide increased STV similarly under both rhythm strategies. Nevertheless, TdP inducibility and AS were higher under IVR conditions (6/8 and 37 ± 27 vs. 1/8 and 8 ± 12 in RVA60, respectively, both p < 0.05). Mapping: Pacing from high (128 ± 10 bpm) to middle (88 ± 10 bpm) to experimental rate (61 ± 3 bpm) increased all electrophysiological parameters, including interventricular dispersion, due to steeper left ventricular restitution curves, and intraventricular SDR: maximal cubic dispersion from 60 ± 14 (high) to 69 ± 17 (middle) to 84 ± 22 ms (p < 0.05 vs. high and middle rate). Conclusion: In CAVB dogs, severe bradycardia increases the probability and severity of arrhythmic events by heterogeneously causing electrophysiological instability, which is mainly reflected in an increased spatial, and to a lesser extent temporal, dispersion of repolarization. [ABSTRACT FROM AUTHOR]

The proarrhythmic risk is a major concern in drug development. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative has proposed the JTpeak interval on electrocardiograms (ECGs) and qNet, an in silico metric, as new biomarkers that may overcome the limitations of the hERG assay and QT interval. In this study, we simulated body-surface ECGs from patch-clamp data using realistic models of the ventricles and torso to explore their suitability as new in silico biomarkers for cardiac safety. We tested seven drugs in this study: dofetilide (high proarrhythmic risk), ranolazine, verapamil (QT increasing, but safe), bepridil, cisapride, mexiletine, and diltiazem. Human ventricular geometry was reconstructed from computed tomography (CT) images, and a Purkinje fiber network was mapped onto the endocardial surface. The electrical wave propagation in the ventricles was obtained by solving a reaction-diffusion equation using finite-element methods. The body-surface ECG data were calculated using a torso model that included the ventricles. The effects of the drugs were incorporated in the model by partly blocking the appropriate ion channels. The effects of the drugs on single-cell action potential (AP) were examined first, and three-dimensional (3D) body-surface ECG simulations were performed at free Cmax values of 1×, 5×, and 10×. In the single-cell and ECG simulations at 5× Cmax, dofetilide, but not verapamil or ranolazine, caused arrhythmia. However, the non-increasing JTpeak caused by verapamil and ranolazine that has been observed in humans was not reproduced in our simulation. Our results demonstrate the potential of 3D body-surface ECG simulation as a biomarker for evaluation of the proarrhythmic risk of candidate drugs.

The proarrhythmic risk is a major concern in drug development. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative has proposed the JTpeak interval on electrocardiograms (ECGs) and qNet, an in silico metric, as new biomarkers that may overcome the limitations of the hERG assay and QT interval. In this study, we simulated body-surface ECGs from patch-clamp data using realistic models of the ventricles and torso to explore their suitability as new in silico biomarkers for cardiac safety. We tested seven drugs in this study: dofetilide (high proarrhythmic risk), ranolazine, verapamil (QT increasing, but safe), bepridil, cisapride, mexiletine, and diltiazem. Human ventricular geometry was reconstructed from computed tomography (CT) images, and a Purkinje fiber network was mapped onto the endocardial surface. The electrical wave propagation in the ventricles was obtained by solving a reaction-diffusion equation using finite-element methods. The body-surface ECG data were calculated using a torso model that included the ventricles. The effects of the drugs were incorporated in the model by partly blocking the appropriate ion channels. The effects of the drugs on single-cell action potential (AP) were examined first, and three-dimensional (3D) body-surface ECG simulations were performed at free Cmax values of 1×, 5×, and 10×. In the single-cell and ECG simulations at 5× Cmax, dofetilide, but not verapamil or ranolazine, caused arrhythmia. However, the non-increasing JTpeak caused by verapamil and ranolazine that has been observed in humans was not reproduced in our simulation. Our results demonstrate the potential of 3D body-surface ECG simulation as a biomarker for evaluation of the proarrhythmic risk of candidate drugs. [ABSTRACT FROM AUTHOR]

Introduction: In the chronic AV-block (CAVB) dog model, structural, contractile, and electrical remodeling occur, which predispose the heart to dofetilide-induced Torsade de Pointes (TdP) arrhythmias. Previous studies found a relation between electrical remodeling and inducibility of TdP, while structural remodeling is not a prerequisite for arrhythmogenesis. In this study, we prospectively assessed the relation between in vivo markers of contractile remodeling and TdP inducibility.Methods: In 18 anesthetized dogs, the maximal first derivative of left ventricular pressure (LV dP/dtmax) was assessed at acute AV-block (AAVB) and after 2 weeks of chronic AV-block (CAVB2). Using pacing protocols, three markers of contractile remodeling, i.e., force-frequency relationship (FFR), mechanical restitution (MR), and post-extrasystolic potentiation (PESP) were determined. Infusion of dofetilide (0.025 mg/kg in 5 min) was used to test for TdP inducibility.Results: After infusion of dofetilide, 1/18 dogs and 12/18 were susceptible to TdP-arrhythmias at AAVB and CAVB2, respectively (p = 0.001). The inducible dogs at CAVB2 showed augmented contractility at a CL of 1200 ms (2354 ± 168 mmHg/s in inducible dogs versus 1091 ± 59 mmHg/s in non-inducible dogs, p < 0.001) with a negative FFR, while the non-inducible dogs retained their positive FFR. The time constant (TC) of the MR curve was significantly higher in the inducible dogs (158 ± 7 ms versus 97 ± 8 ms, p < 0.0001). Furthermore, a linear correlation was found between a weighted score of the number and severity of arrhythmias and contractile parameters, i.e., contractility at CL of 1200 ms (r = 0.73, p = 0.002), the slope of the FFR (r = -0.58, p = 0.01) and the TC of MR (r = 0.66, p = 0.003). Thus, more severe arrhythmias were seen in dogs with the most pronounced contractile remodeling.Conclusion: Contractile remodeling is concomitantly observed with susceptibility to dofetilide-induced TdP-arrhythmias. The inducible dogs show augmented contractile remodeling compared to non-inducible dogs, as seen by a negative FFR, higher maximal response of MR and PESP and slowed MR kinetics. These altered contractility parameters could reflect disrupted Ca2+ handling and Ca2+-overload, which predispose the heart to delayed- and early afterdepolarizations that could trigger TdP-arrhythmias.

Background: An electrical storm of Torsade de Pointes arrhythmias (TdP) can be reproducibly induced in the anesthetized chronic AV-block (CAVB) dog by infusion of the I Kr -blocker dofetilide. Earlier studies showed that these arrhythmias 1) arise from locations with high spatial dispersion in repolarization (SDR) and 2) can be suppressed by high-rate pacing. We examined whether suppression of TdP by high-rate pacing is established through a decrease in SDR in the CAVB dog. Methods: Dofetilide (25 μg/kg in 5 min) was administered to 5 anesthetized CAVB dogs to induce TdP arrhythmias. During the experiments, animals were continuously paced from the right ventricular apex at 50 beats/minute (RVA50). Upon TdP occurrence and conversion, RVA pacing was consecutively set to 100, 80 and 60 beats/minute for 2 min, referred to as pacing blocks. To determine the additional anti-arrhythmic effects of HRP over defibrillation alone, the number of arrhythmic events and SDR at RVA100 were compared to data from three previously conducted experiments, in which dogs underwent the same experimental protocol but were paced at RVA60 upon TdP occurrence (RVA60 retro ). In all experiments, recordings included surface electrocardiogram and mapping by 56 intramural needles, each recording four electrograms, evenly inserted into the ventricular walls and septum. For each pacing block, the number of ectopic beats (EB), and TdP severity were scored. SDR was quantified as the average difference in repolarization time within four squared needles (SDR cubic ). Results: In 4 out of 5 animals, pacing at RVA100 suppressed TdP occurrence. One dog could not be converted by defibrillation after the initial TdP. Compared to RVA50, pacing at RVA100, but not RVA80 and RVA60, significantly reduced the TdP score (78 ± 33 vs . 0 ± 0, p < 0.05 and vs . 12.5 ± 25 and 25 ± 50, both p > 0.05). The reduction in TdP score was reflected by a significant decrease in SDR cubic (125 ± 46 ms before TdP vs . 49 ± 18 ms during RVA100, p < 0.05), and SDR was smaller than in the RVA60 retro animals (101 ± 52 ms, p < 0.05 vs . RVA100). Conclusion: In CAVB dogs, high-rate pacing effectively suppresses TdP, which, at least in part, results from a spatial homogenization of cardiac repolarization, as reflected by a decrease in SDR., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Loen, Van Weperen, Beekman, Van Bavel, Meijborg, Van der Waal, Coronel, van der Heyden and Vos.)

Introduction: In the chronic AV-block (CAVB) dog model, structural, contractile, and electrical remodeling occur, which predispose the heart to dofetilide-induced Torsade de Pointes (TdP) arrhythmias. Previous studies found a relation between electrical remodeling and inducibility of TdP, while structural remodeling is not a prerequisite for arrhythmogenesis. In this study, we prospectively assessed the relation between in vivo markers of contractile remodeling and TdP inducibility. Methods: In 18 anesthetized dogs, the maximal first derivative of left ventricular pressure (LV dP/dtmax) was assessed at acute AV-block (AAVB) and after 2 weeks of chronic AV-block (CAVB2). Using pacing protocols, three markers of contractile remodeling, i.e., force-frequency relationship (FFR), mechanical restitution (MR), and post-extrasystolic potentiation (PESP) were determined. Infusion of dofetilide (0.025 mg/kg in 5 min) was used to test for TdP inducibility. Results: After infusion of dofetilide, 1/18 dogs and 12/18 were susceptible to TdP-arrhythmias at AAVB and CAVB2, respectively (p = 0.001). The inducible dogs at CAVB2 showed augmented contractility at a CL of 1200 ms (2354 ± 168 mmHg/s in inducible dogs versus 1091 ± 59 mmHg/s in non-inducible dogs, p < 0.001) with a negative FFR, while the non-inducible dogs retained their positive FFR. The time constant (TC) of the MR curve was significantly higher in the inducible dogs (158 ± 7 ms versus 97 ± 8 ms, p < 0.0001). Furthermore, a linear correlation was found between a weighted score of the number and severity of arrhythmias and contractile parameters, i.e., contractility at CL of 1200 ms (r = 0.73, p = 0.002), the slope of the FFR (r = -0.58, p = 0.01) and the TC of MR (r = 0.66, p = 0.003). Thus, more severe arrhythmias were seen in dogs with the most pronounced contractile remodeling. Conclusion: Contractile remodeling is concomitantly observed with susceptibility to dofetilide-induced TdP-arrhythmias. The inducible dogs show augmented contractile remodeling compared to non-inducible dogs, as seen by a negative FFR, higher maximal response of MR and PESP and slowed MR kinetics. These altered contractility parameters could reflect disrupted Ca2+ handling and Ca2+-overload, which predispose the heart to delayed- and early afterdepolarizations that could trigger TdP-arrhythmias. [ABSTRACT FROM AUTHOR]

The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a global initiative intended to improve drug proarrhythmia risk assessment using a new paradigm of mechanistic assays. Under the CiPA paradigm, the relative risk of drug-induced Torsade de Pointes (TdP) is assessed using an in silico model of the human ventricular action potential (AP) that integrates in vitro pharmacology data from multiple ion channels. Thus, modeling predictions of cardiac risk liability will depend critically on the variability in pharmacology data, and uncertainty quantification (UQ) must comprise an essential component of the in silico assay. This study explores UQ methods that may be incorporated into the CiPA framework. Recently, we proposed a promising in silico TdP risk metric (qNet), which is derived from AP simulations and allows separation of a set of CiPA training compounds into Low, Intermediate, and High TdP risk categories. The purpose of this study was to use UQ to evaluate the robustness of TdP risk separation by qNet. Uncertainty in the model parameters used to describe drug binding and ionic current block was estimated using the non-parametric bootstrap method and a Bayesian inference approach. Uncertainty was then propagated through AP simulations to quantify uncertainty in qNet for each drug. UQ revealed lower uncertainty and more accurate TdP risk stratification by qNet when simulations were run at concentrations below 5× the maximum therapeutic exposure (Cmax). However, when drug effects were extrapolated above 10× Cmax, UQ showed that qNet could no longer clearly separate drugs by TdP risk. This was because for most of the pharmacology data, the amount of current block measured was

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na+ and Ca2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca2+-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a global initiative intended to improve drug proarrhythmia risk assessment using a new paradigm of mechanistic assays. Under the CiPA paradigm, the relative risk of drug-induced Torsade de Pointes (TdP) is assessed using an in silico model of the human ventricular action potential (AP) that integrates in vitro pharmacology data from multiple ion channels. Thus, modeling predictions of cardiac risk liability will depend critically on the variability in pharmacology data, and uncertainty quantification (UQ) must comprise an essential component of the in silico assay. This study explores UQ methods that may be incorporated into the CiPA framework. Recently, we proposed a promising in silico TdP risk metric (qNet), which is derived from AP simulations and allows separation of a set of CiPA training compounds into Low, Intermediate, and High TdP risk categories. The purpose of this study was to use UQ to evaluate the robustness of TdP risk separation by qNet. Uncertainty in the model parameters used to describe drug binding and ionic current block was estimated using the non-parametric bootstrap method and a Bayesian inference approach. Uncertainty was then propagated through AP simulations to quantify uncertainty in qNet for each drug. UQ revealed lower uncertainty and more accurate TdP risk stratification by qNet when simulations were run at concentrations below 5× the maximum therapeutic exposure (Cmax). However, when drug effects were extrapolated above 10× Cmax, UQ showed that qNet could no longer clearly separate drugs by TdP risk. This was because for most of the pharmacology data, the amount of current block measured was <60%, preventing reliable estimation of IC50-values. The results of this study demonstrate that the accuracy of TdP risk prediction depends both on the intrinsic variability in ion channel pharmacology data as well as on experimental designconsiderations that preclude an accurate determination of drug IC50-values in vitro. Thus, we demonstrate that UQ provides valuable information about in silico modeling predictions that can inform future proarrhythmic risk evaluation of drugs under the CiPA paradigm. [ABSTRACT FROM AUTHOR]

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms fromrabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na+ and Ca2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca2+-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines. [ABSTRACT FROM AUTHOR]

Keywords: human pluripotent stem cell derived cardiomyocytes; genetic modifier; long QT syndrome; safety pharmacology; computational models; atrial fibrilation; Torsade de pointes; CAVB dog model EN human pluripotent stem cell derived cardiomyocytes genetic modifier long QT syndrome safety pharmacology computational models atrial fibrilation Torsade de pointes CAVB dog model 1 2 2 06/23/22 20220621 NES 220621 Cardiac arrhythmias are associated with a high rate of morbidity and mortality and cause more than 250,000 deaths annually in the United States ([1]). In this Research Topic we included preclinical studies addressing diverse issues in acquired and inherited arrhythmias, anti-arrhythmic drugs, and new therapy approaches; this in order to further define the precise cellular and molecular basis of cardiac arrhythmias and improve the available therapeutic options. [Extracted from the article]