Arrhythmic hazard map for a 3D whole-ventricle model under multiple ion channel block

Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators

In silico trials for drug safety assessment require many high-fidelity 3D cardiac simulations to predict drug-induced QT interval prolongation, which is often computationally prohibitive. To streamline this process, we developed sex-specific emulators for a fast prediction of QT interval, trained on a dataset of 900 simulations. Our results show significant differences between 3D and 0D single-cell models as risk levels increase, underscoring the ability of 3D modeling to capture more complex cardiac responses. The emulators demonstrated an average error of 4% compared to simulations, allowing for efficient global sensitivity analysis and fast replication of in silico clinical trials. This approach enables rapid, multi-dose drug testing on standard hardware, addressing critical industry challenges around trial design, assay variability, and cost-effective safety evaluations. By integrating these emulators into drug development, we can improve preclinical reliability and advance the practical application of digital twins in biomedicine.

Virtual clinical QT exposure-response studies - A translational computational approach

BACKGROUND AND PURPOSE

A recent paradigm shift in proarrhythmic risk assessment suggests that the integration of clinical, non-clinical, and computational evidence can be used to reach a comprehensive understanding of the proarrhythmic potential of drug candidates. While current computational methodologies focus on predicting the incidence of proarrhythmic events after drug administration, the objective of this study is to predict concentration-response relationships of QTc as a clinical endpoint.

EXPERIMENTAL APPROACH

Full heart computational models reproducing human cardiac populations were created to predict the concentration-response relationship of changes in the QT interval as recommended for clinical trials. The concentration-response relationship of the QT-interval prolongation obtained from the computational cardiac population was compared against the relationship from clinical trial data for a set of well-characterized compounds: moxifloxacin, dofetilide, verapamil, and ondansetron.

KEY RESULTS

Computationally derived concentration-response relationships of QT interval changes for three of the four drugs had slopes within the confidence interval of clinical trials (dofetilide, moxifloxacin and verapamil) when compared to placebo-corrected concentration-ΔQT and concentration-ΔQT regressions. Moxifloxacin showed a higher intercept, outside the confidence interval of the clinical data, demonstrating that in this example, the standard linear regression does not appropriately capture the concentration-response results at very low concentrations. The concentrations corresponding to a mean QTc prolongation of 10 ms were consistently lower in the computational model than in clinical data. The critical concentration varied within an approximate ratio of 0.5 (moxifloxacin and ondansetron) and 1 times (dofetilide, verapamil) the critical concentration observed in human clinical trials. Notably, no other in silico methodology can approximate the human critical concentration values for a QT interval prolongation of 10 ms.

CONCLUSION AND IMPLICATIONS

Computational concentration-response modelling of a virtual population of high-resolution, 3-dimensional cardiac models can provide comparable information to clinical data and could be used to complement pre-clinical and clinical safety packages. It provides access to an unlimited exposure range to support trial design and can improve the understanding of pre-clinical-clinical translation.

HPC Framework for Performing in Silico Trials Using a 3D Virtual Human Cardiac Population as Means to Assess Drug-Induced Arrhythmic Risk

Following the 3 R's principles of animal research-replacement, reduction, and refinement-a high-performance computational framework was produced to generate a platform to perform human cardiac in-silico clinical trials as means to assess the pro-arrhythmic risk after the administrations of one or combination of two potentially arrhythmic drugs. The drugs assessed in this study were hydroxychloroquine and azithromycin. The framework employs electrophysiology simulations on high-resolution three-dimensional, biventricular human heart anatomies including phenotypic variabilities, so as to determine if differential QT-prolongation responds to drugs as observed clinically. These simulations also reproduce sex-specific ionic channel characteristics. The derived changes in the pseudo-electrocardiograms, calcium concentrations, as well as activation patterns within 3D geometries were evaluated for signs of induced arrhythmia. The virtual subjects could be evaluated at two different cycle lengths: at a normal heart rate and at a heart rate associated with stress as means to analyze the proarrhythmic risks after the administrations of hydroxychloroquine and azithromycin. Additionally, a series of experiments performed on reanimated swine hearts utilizing Visible Heart® methodologies in a four-chamber working heart model were performed to verify the arrhythmic behaviors observed in the in silico trials.The obtained results indicated similar pro-arrhythmic risk assessments within the virtual population as compared to published clinical trials (21% clinical risk vs 21.8% in silico trial risk). Evidence of transmurally heterogeneous action potential prolongations after providing a large dose of hydroxychloroquine was found as the observed mechanisms for elicited arrhythmias, both in the in vitro and the in silico models. The proposed workflow for in silico clinical drug cardiotoxicity trials allows for reproducing the complex behavior of cardiac electrophysiology in a varied population, in a matter of a few days as compared to the months or years it requires for most in vivo human clinical trials. Importantly, our results provided evidence of the common phenotype variants that produce distinct drug-induced arrhythmogenic outcomes.

Transition mechanisms from atrial flutter to atrial fibrillation during anti-tachycardia pacing therapy

BACKGROUND

Atrial anti-tachycardia pacing (aATP) has been shown to be effective for the termination of atrial tachyarrhythmias, but its success rate is still not high enough.

OBJECTIVE

The main objective of this study was to investigate the mechanisms of atrial flutter (AFL) termination by aATP and the transition from AFL to atrial fibrillation (AF) during aATP.

METHODS

We developed a multi-scale model of the human atrium based on magnetic resonance images and examined the atrial electrophysiology of AFL during aATP with a ramp protocol.

RESULTS

In successful cases of aATP, paced excitation entered the excitable gap and collided with the leading edge of the reentrant wave front. Furthermore, the excitation propagating in the opposite direction collided with the trailing edge of the reentrant wave to terminate AFL. The second collision was made possible by the distribution of the wave propagation velocity in the atria. The detailed analysis revealed that the slowing of propagation velocity occurred at the exit of the sub-Eustachian isthmus, probably due to source-sink mismatch. During the transition from AFL to AF, the excitation collided with the refractory zone of the preceding wave and broke into multiple wave fronts to induce AF. A similar observation was made for the transition from AF to sinus rhythm. In both cases, the complex anatomy of the atria played an essential role.

CONCLUSION

The complex anatomy of atria plays an essential role in the maintenance of stable AFL and its termination by aATP, which were revealed by the realistic three-dimensional simulation model.

Low-energy defibrillation using a base-apex epicardial electrode

BACKGROUND

Current implantable cardioverter defibrillators (ICDs) require electric conduction with high voltage and high energy, which can impair cardiac function and induce another malignant arrhythmia. As a result, there has been a demand for an ICD that can effectively operate with lower energy to mitigate the risks of a strong electric shock.

METHODS

A pair of sheet-shaped electrodes covering the heart were analyzed in three configurations (top-bottom, left-right, and front-back) using a heart simulator. We also varied the distance between the two electrodes (clearance) to identify the electrode shape with the lowest defibrillation threshold (DFT). We also investigated the ICD shock waveform, shock direction, and the effect of the backside insulator of the electrode.

RESULTS

The DFT was high when the clearance was too small and the DFT was high even when the clearance was too large, suggesting that an optimal value clearance. The top-bottom electrodes with optimal clearance showed the lowest DFT when the biphasic shocks set the top electrode to a high potential first and then the bottom electrode was set to a high potential. An interval between a first shock waveform and a second shock waveform should be provided for low-energy defibrillation. Because the insulator prevents unnecessary current flow to the backside, the DFT of the electrodes with insulators is less than those without insulators.

CONCLUSION

Painless defibrillation using sheet-shaped electrodes on the epicardium is predicated on the basis of results using a heart simulator.

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

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. IC 50 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 , Vm peak , Vm resting , APD tri , APD 90 , APD 50 , Ca peak , Ca diastole , Ca tri , CaD 90 , CaD 50 , 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.

UT-Heart: A Finite Element Model Designed for the Multiscale and Multiphysics Integration of our Knowledge on the Human Heart

To fully understand the health and pathology of the heart, it is necessary to integrate knowledge accumulated at molecular, cellular, tissue, and organ levels. However, it is difficult to comprehend the complex interactions occurring among the building blocks of biological systems across these scales. Recent advances in computational science supported by innovative high-performance computer hardware make it possible to develop a multiscale multiphysics model simulating the heart, in which the behavior of each cell model is controlled by molecular mechanisms and the cell models themselves are arranged to reproduce elaborate tissue structures. Such a simulator could be used as a tool not only in basic science but also in clinical settings. Here, we describe a multiscale multiphysics heart simulator, UT-Heart, which uses unique technologies to realize the abovementioned features. As examples of its applications, models for cardiac resynchronization therapy and surgery for congenital heart disease will be also shown.

[Cardiovascular safety pharmacology: in vitro strategies and improvements in technology and evaluation]

Safety pharmacology studies have been clearly defined through discussions at the International Council for Harmonization of Pharmaceutical Regulations (ICH), and are conducted as non-clinical studies according to the ICH S7A and S7B to ensure the safety of subjects participating in clinical studies. The representative of in vitro studies of cardiovascular system is hERG assay, but CiPA recommendations by FDA/HESI (multi-ion channel assays, simulation with in silico model using the multi-ion channel data, human iPS cell-derived cardiomyocyte assay), a new clinical risk prediction strategy that makes effective use of non-clinical data is being established. In addition, regarding the risk of heart failure that induced by anticancer drugs, which are attracting attention as a social problem, technology development has been made centering on human iPS cell-derived cardiomyocytes. There are many issues to be solved, but active challenges are being taken globally to bridge the gap between clinical and non-clinical.

Chloroquine and hydroxychloroquine provoke arrhythmias at concentrations higher than those clinically used to treat COVID-19: A simulation study

Abstract

The risk of fatal arrhythmias is the major concern for using chloroquine (CQ) or hydroxychloroquine (HCQ) to treat coronavirus disease 2019 (COVID‐19), but the reported number of life‐threatening arrhythmic events or deaths is relatively small. The objective of this study was to assess the arrhythmogenic risk of these two drugs using a multiscale heart simulation, which allows testing even at high concentrations, including those that cause fatal arrhythmias. We measured the inhibitory action of CQ, HCQ, and HCQ with 30 μM azithromycin (AZ) on six ion currents (fast [INa] and late [INa,L] components of the sodium current, L‐type calcium current [ICa,L], rapid [IKr/hERG], and slow [IKs] components of delayed rectifier potassium, and inward rectifier potassium [IK1]) over a wide range of concentrations using the automated patch‐clamp system. Using the concentration–inhibition relationship that was thus obtained, we simulated the drug effects while increasing the concentration until the life‐threatening arrhythmia, torsade de pointes (TdP), was observed. The obtained threshold concentrations for TdP were 12.5, 35, and 22.5 μM for CQ, HCQ, and HCQ with AZ, respectively. Adding therapeutic concentrations of mexiletine or verapamil successfully prevented the occurrence of TdP, and verapamil was more effective. CQ, HCQ, and HCQ with AZ thresholds for TdP were larger than both antiviral concentrations that were reported by in vitro experiments and free plasma concentrations that were attained by the clinically used dosage. The current simulation data provided a safety margin to the currently used clinical dose for CQ and HCQ/AZ.

In Silico Classifiers for the Assessment of Drug Proarrhythmicity

Drug-induced torsade de pointes (TdP) is a life-threatening ventricular arrhythmia responsible for the withdrawal of many drugs from the market. Although currently used TdP risk-assessment methods are effective, they are expensive and prone to produce false positives. In recent years, in silico cardiac simulations have proven to be a valuable tool for the prediction of drug effects. The objective of this work is to evaluate different biomarkers of drug-induced proarrhythmic risk and to develop an in silico risk classifier. Cellular simulations were performed using a modified version of the O'Hara et al. ventricular action potential model and existing pharmacological data (IC₅₀ and effective free therapeutic plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk (51 positive). For each compound, four biomarkers were tested: T_x (drug concentration leading to a 10% prolongation of the action potential over the EFTPC), T_qNet (net charge carried by ionic currents when exposed to 10 times the EFTPC with respect to the net charge in control), T_triang (triangulation for a drug concentration of 10 times the EFTPC over triangulation in control), and T_EAD (drug concentration originating early afterdepolarizations over EFTPC). Receiver operating characteristic (ROC) curves were built for each biomarker to evaluate their individual predictive quality. At the optimal cutoff point, accuracies for T_x, T_qNet, T_triang, and T_EAD were 89.9, 91.7, 90.8, and 78.9% respectively. The resulting accuracy of the hERG IC₅₀ test (current biomarker) was 78.9%. When combining T_x, T_qNet and T_triang into a classifier based on decision trees, the prediction improves, achieving an accuracy of 94.5%. The sensitivity analysis revealed that most of the effects on the action potential are mainly due to changes in I_Kr, I_CaL, I_NaL and I_Ks. In fact, considering that drugs affect only these four currents, TdP risk classification can be as accurate as when considering effects on the seven main currents proposed by the CiPA initiative. Finally, we built a ready-to-use tool (based on more than 450 000 simulations), which can be used to quickly assess the proarrhythmic risk of a compound. In conclusion, our in silico tool can be useful for the preclinical assessment of TdP-risk and to reduce costs related with new drug development. The TdP risk-assessment tool and the software used in this work are available at https://riunet.upv.es/handle/10251/136919.

Ionic mechanisms of ST segment elevation in electrocardiogram during acute myocardial infarction

ST elevation on an electrocardiogram is a hallmark of acute transmural ischemia. However, the underlying mechanism remains unclear. We hypothesized that high ischemic sensitivities of epicardial adenosine triphosphate-sensitive potassium (IKATP) and sodium (INa) currents play key roles in the genesis of ST elevation. Using a multi-scale heart simulation under moderately ischemic conditions, transmural heterogeneities of IKATP and INa created a transmural gradient, opposite to that observed in subendocardial injury, leading to ST elevation. These heterogeneities also contributed to the genesis of hyper-acute T waves under mildly ischemic conditions. By contrast, under severely ischemic conditions, although action potentials were suppressed transmurally, the potential gradient at the boundary between the ischemic and normal regions caused ST elevation without a contribution from transmural heterogeneity. Thus, transmural heterogeneities of ion channel properties may contribute to the genesis of ST-T changes during mild or moderate transmural ischemia, while ST elevation may be induced without the contribution of heterogeneity under severe ischemic conditions.

In silico models for evaluating proarrhythmic risk of drugs

Safety evaluation of drugs requires examination of the risk of generating Torsade de Pointes (TdP) because it can lead to sudden cardiac death. Until recently, the QT interval in the electrocardiogram (ECG) has been used in the evaluation of TdP risk because the QT interval is known to be associated with the development of TdP. Although TdP risk evaluation based on QT interval has been successful in removing drugs with TdP risk from the market, some safe drugs may have also been affected due to the low specificity of QT interval-based evaluation. For more accurate evaluation of drug safety, the comprehensive in vitro proarrhythmia assay (CiPA) has been proposed by regulatory agencies, industry, and academia. Although the CiPA initiative includes in silico evaluation of cellular action potential as a component, attempts to utilize in silico simulation in drug safety evaluation are expanding, even to simulating human ECG using biophysical three-dimensional models of the heart and torso under the effects of drugs. Here, we review recent developments in the use of in silico models for the evaluation of the proarrhythmic risk of drugs. We review the single cell, one-dimensional, two-dimensional, and three-dimensional models and their applications reported in the literature and discuss the possibility of utilizing ECG simulation in drug safety evaluation.

Three-Dimensional Heart Model-Based Screening of Proarrhythmic Potential by in silico Simulation of Action Potential and Electrocardiograms

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.

Clinical and pharmacological application of multiscale multiphysics heart simulator, UT-Heart

A heart simulator, UT-Heart, is a finite element model of the human heart that can reproduce all the fundamental activities of the working heart, including propagation of excitation, contraction, and relaxation and generation of blood pressure and blood flow, based on the molecular aspects of the cardiac electrophysiology and excitation-contraction coupling. In this paper, we present a brief review of the practical use of UT-Heart. As an example, we focus on its application for predicting the effect of cardiac resynchronization therapy (CRT) and evaluating the proarrhythmic risk of drugs. Patient-specific, multiscale heart simulation successfully predicted the response to CRT by reproducing the complex pathophysiology of the heart. A proarrhythmic risk assessment system combining in vitro channel assays and in silico simulation of cardiac electrophysiology using UT-Heart successfully predicted druginduced arrhythmogenic risk. The assessment system was found to be reliable and efficient. We also developed a comprehensive hazard map on the various combinations of ion channel inhibitors. This in silico electrocardiogram database (now freely available at http://ut-heart.com/) can facilitate proarrhythmic risk assessment without the need to perform computationally expensive heart simulation. Based on these results, we conclude that the heart simulator, UT-Heart, could be a useful tool in clinical medicine and drug discovery.

Arrhythmic hazard map for a 3D whole-ventricle model under multiple ion channel block

Background and Purpose

To date, proposed in silico models for preclinical cardiac safety testing are limited in their predictability and usability. We previously reported a multi‐scale heart simulation that accurately predicts arrhythmogenic risk for benchmark drugs.

Experimental Approach

We created a comprehensive hazard map of drug‐induced arrhythmia based on the electrocardiogram (ECG) waveforms simulated under wide range of drug effects using the multi‐scale heart simulator described here, implemented with cell models of human cardiac electrophysiology.

Key Results

A total of 9075 electrocardiograms constitute the five‐dimensional hazard map, with coordinates representing the extent of the block of each of the five ionic currents (rapid delayed rectifier potassium current (I_Kr), fast (I_Na) and late (I_Na,L) components of the sodium current, L‐type calcium current (I_Ca,L) and slow delayed rectifier current (I_Ks)), involved in arrhythmogenesis. Results of the evaluation of arrhythmogenic risk based on this hazard map agreed well with the risk assessments reported in the literature. ECG databases also suggested that the interval between the J‐point and the T‐wave peak is a superior index of arrhythmogenicity when compared to the QT interval due to its ability to characterize the multi‐channel effects compared with QT interval.

Conclusion and Implications

Because concentration‐dependent effects on electrocardiograms of any drug can be traced on this map based on in vitro current assay data, its arrhythmogenic risk can be evaluated without performing costly and potentially risky human electrophysiological assays. Hence, the map serves as a novel tool for use in pharmaceutical research and development.