Effects of amiodarone on short QT syndrome variant 3 in human ventricles: a simulation study

Impacts of gene variants on drug effects-the foundation of genotype-guided pharmacologic therapy for long QT syndrome and short QT syndrome

The clinical significance of optimal pharmacotherapy for inherited arrhythmias such as short QT syndrome (SQTS) and long QT syndrome (LQTS) has been increasingly recognised. The advancement of gene technology has opened up new possibilities for identifying genetic variations and investigating the pathophysiological roles and mechanisms of genetic arrhythmias. Numerous variants in various genes have been proven to be causative in genetic arrhythmias. Studies have demonstrated that the effectiveness of certain drugs is specific to the patient or genotype, indicating the important role of gene-variants in drug response. This review aims to summarize the reported data on the impact of different gene-variants on drug response in SQTS and LQTS, as well as discuss the potential mechanisms by which gene-variants alter drug response. These findings may provide valuable information for future studies on the influence of gene variants on drug efficacy and the development of genotype-guided or precision treatment for these diseases.

T-wave inversion through inhomogeneous voltage diffusion within the FK3V cardiac model

The heart beats are due to the synchronized contraction of cardiomyocytes triggered by a periodic sequence of electrical signals called action potentials, which originate in the sinoatrial node and spread through the heart's electrical system. A large body of work is devoted to modeling the propagation of the action potential and to reproducing reliably its shape and duration. Connection of computational modeling of cells to macroscopic phenomenological curves such as the electrocardiogram has been also intense, due to its clinical importance in analyzing cardiovascular diseases. In this work, we simulate the dynamics of action potential propagation using the three-variable Fenton-Karma model that can account for both normal and damaged cells through a the spatially inhomogeneous voltage diffusion coefficient. We monitor the action potential propagation in the cardiac tissue and calculate the pseudo-electrocardiogram that reproduces the R and T waves. The R-wave amplitude varies according to a double exponential law as a function of the (spatially homogeneous, for an isotropic tissue) diffusion coefficient. The addition of spatial inhomogeneity in the diffusion coefficient by means of a defected region representing damaged cardiac cells may result in T-wave inversion in the calculated pseudo-electrocardiogram. The transition from positive to negative polarity of the T-wave is analyzed as a function of the length and the depth of the defected region.

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.

Preclinical short QT syndrome models: studying the phenotype and drug-screening

Cardiovascular diseases are the main cause of sudden cardiac death (SCD) in developed and developing countries. Inherited cardiac channelopathies are linked to 5-10% of SCDs, mainly in the young. Short QT syndrome (SQTS) is a rare inherited channelopathy, which leads to both atrial and ventricular tachyarrhythmias, syncope, and even SCD. International European Society of Cardiology guidelines include as diagnostic criteria: (i) QTc ≤ 340 ms on electrocardiogram, (ii) QTc ≤ 360 ms plus one of the follwing, an affected short QT syndrome pathogenic gene mutation, or family history of SQTS, or aborted cardiac arrest, or family history of cardiac arrest in the young. However, further evaluation of the QTc ranges seems to be required, which might be possible by assembling large short QT cohorts and considering genetic screening of the newly described pathogenic mutations. Since the mechanisms underlying the arrhythmogenesis of SQTS is unclear, optimal therapy for SQTS is still lacking. The disease is rare, unclear genotype-phenotype correlations exist in a bevy of cases and the absence of an international short QT registry limit studies on the pathophysiological mechanisms of arrhythmogenesis and therapy of SQTS. This leads to the necessity of experimental models or platforms for studying SQTS. Here, we focus on reviewing preclinical SQTS models and platforms such as animal models, heterologous expression systems, human-induced pluripotent stem cell-derived cardiomyocyte models and computer models as well as three-dimensional engineered heart tissues. We discuss their usefulness for SQTS studies to examine genotype-phenotype associations, uncover disease mechanisms and test drugs. These models might be helpful for providing novel insights into the exact pathophysiological mechanisms of this channelopathy and may offer opportunities to improve the diagnosis and treatment of patients with SQT syndrome.

Computational prediction of drug response in short QT syndrome type 1 based on measurements of compound effect in stem cell-derived cardiomyocytes

Short QT (SQT) syndrome is a genetic cardiac disorder characterized by an abbreviated QT interval of the patient's electrocardiogram. The syndrome is associated with increased risk of arrhythmia and sudden cardiac death and can arise from a number of ion channel mutations. Cardiomyocytes derived from induced pluripotent stem cells generated from SQT patients (SQT hiPSC-CMs) provide promising platforms for testing pharmacological treatments directly in human cardiac cells exhibiting mutations specific for the syndrome. However, a difficulty is posed by the relative immaturity of hiPSC-CMs, with the possibility that drug effects observed in SQT hiPSC-CMs could be very different from the corresponding drug effect in vivo. In this paper, we apply a multistep computational procedure for translating measured drug effects from these cells to human QT response. This process first detects drug effects on individual ion channels based on measurements of SQT hiPSC-CMs and then uses these results to estimate the drug effects on ventricular action potentials and QT intervals of adult SQT patients. We find that the procedure is able to identify IC50 values in line with measured values for the four drugs quinidine, ivabradine, ajmaline and mexiletine. In addition, the predicted effect of quinidine on the adult QT interval is in good agreement with measured effects of quinidine for adult patients. Consequently, the computational procedure appears to be a useful tool for helping predicting adult drug responses from pure in vitro measurements of patient derived cell lines.

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.

Computational Analysis of the Action of Chloroquine on Short QT Syndrome Variant 1 and Variant 3 in Human Ventricles

AIMS

The short QT syndrome (SQTS) is a rare genetic disorder associated with arrhythmias and sudden cardiac death (SCD). The SQTI and SQT3, SQTS variants, result from gain-of-function mutations (N588K and D172N, respectively) in the KCNH2-encoded and KCNJ2-encoded potassium channels, in which treatment with potassium channel blocking agents has demonstrated some efficacy. This study used in silico modelling to gain mechanistic insights into the actions of anti-malarial drug chloroquine (CQ) in the setting of SQTI and SQT3.

METHODS AND RESULTS

The ten Tusscher et al. human ventricle model was modified to a Markov chain formulation of $I_{J}$<r and a Hodgkin-Huxley formulation of $I_{J}$<1 describing SQTI and SQT3 mutant conditions, respectively. Cell models were incorporated into heterogeneous one-dimensional (ID) transmural ventricular strand model to assess prolongation of the QT intervals. The blocking effects of CQ on $I_{J}$<1 and $I_{J}$<r were modelled by using Hill coefficient and IC₅₀ from literatures. At the single cells, CQ prolonged the AP duration (APD) under both the SQTI and SQT3 conditions; at the multi-cell strand level, CQ prolonged the QT intervals and declined the T-wave amplitude under both conditions.

CONCLUSIONS

This computational study provides novel insights into the efficacy of CQ in the setting of SQTI and SQT3 variants, and indicates that CQ is a useful drug in the treatment of SQTS.

Molecular Insights into the Short QT Syndrome

Short QT syndrome (SQTS) is a myocardial conduction disorder characterized by a short QT interval on electrocardiogram and predisposition to familial atrial fibrillation and/or sudden cardiac death. Genetic SQTS is primarily caused by one or more cardiac ion channelopathies, in which either impaired depolarization currents, or enhanced repolarization currents, shorten cardiac action potential duration. Given that QT interval duration is not always predictive of arrhythmia burden and risk of death in SQTS, there is a need to understand the molecular mechanisms of the condition to improve risk prognostication and potential pharmacologic treatment. In the last decade, several computational advances and in vitro preclinical studies have provided insight into the molecular mechanisms underlying congenital SQTS. In this review, we discuss recent findings in SQTS molecular mechanisms and correlate these advances with clinical guidelines for SQTS diagnosis and treatment.