Torsadogenic Risk Research Articles

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach

Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presents substantial challenges in drug safety assessment. In this study, we propose that analyzing the physicochemical properties of pharmaceuticals can provide valuable insights into their potential for torsadogenic cardiotoxicity. Our research centers on estimating TdP risk based on the molecular structure of drugs. We introduce a novel quantitative structure-toxicity relationship (QSTR) prediction model that leverages an in silico approach developed by adopting the 4R rule in laboratory animals. This approach eliminates the need for animal testing, saves time, and reduces cost. Our algorithm has successfully predicted the torsadogenic risks of various pharmaceutical compounds. To develop this model, we employed Support Vector Machine (SVM) and ensemble techniques, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). We enhanced the model's predictive accuracy through a rigorous two-step feature selection process. Furthermore, we utilized the SHapley Additive exPlanations (SHAP) technique to explain the prediction of torsadogenic risk, particularly within the RF model. This study represents a significant step towards creating a robust QSTR model, which can serve as an early screening tool for assessing the torsadogenic potential of pharmaceutical candidates or existing drugs. By incorporating molecular structure-based insights, we aim to enhance drug safety evaluation and minimize the risks of drug-induced TdP, ultimately benefiting both patients and the pharmaceutical industry.

Action Potential Morphology Accurately Predicts Proarrhythmic Risk for Drugs With Potential to Prolong Cardiac Repolarization.

Drug-induced or acquired long QT syndrome occurs as a result of the unintended disruption of cardiac repolarization due to drugs that block cardiac ion channels. These side effects have been responsible for the withdrawal of a range of drugs from market and are a common reason for termination of the development of new drugs in the preclinical stage. Existing approaches to risk prediction are expensive and overly sensitive meaning that recently there have been renewed efforts, largely driven by the comprehensive proarrhythmic assay initiative, to develop more accurate methods for allocation of proarrhythmic risk. In this study, we aimed to quantify changes in the morphology of the repolarization phase of the cardiac action potential as an indicator of proarrhythmia, supposing that these shape changes might precede the emergence of ectopic depolarizations that trigger arrhythmia. To do this, we describe a new method of quantifying action potential morphology by measuring the radius of curvature of the repolarization phase both in simulated action potentials, as well as in action potentials measured from induced pluripotent stem cell-derived cardiomyocytes. Features derived from the curvature signal were used as inputs for logistic regressions to predict proarrhythmic risk. Optimal risk classifiers based on morphology were able to correctly classify risk to drugs in the comprehensive proarrhythmic assay initiative panels with very high accuracy (0.9375) and outperformed conventional metrics based on action potential duration at 90% repolarization, triangulation, and charge movement (qNet). Analysis of action potential morphology in response to proarrhythmic drugs improves prediction of torsadogenic risk. Furthermore, morphology metrics can be measured directly from the action potential, potentially eliminating the burden of undertaking complex screens of potency and drug-binding kinetics against multiple cardiac ion channels. As such, this method has the potential to improve and streamline regulatory assessment of proarrhythmia in preclinical drug development.

Electropharmacological Characterization of Licorice Using the Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes Sheets and the Chronic Atrioventricular Block Dogs.

Licorice has been traditionally prescribed for palpitation, whereas its overdose has caused lethal arrhythmias including torsade de pointes. Licorice contains glycyrrhizic acid of ≥ 2% (w/w), which is hydrolyzed to glycyrrhetinic acid (GRA) in the intestine. Since their cardiac electropharmacological properties are not fully understood, we assessed them to ask mechanism of licorice-induced torsade de pointes. GRA at 0.1, 1 and 10μg/mL was cumulatively applied to the human induced pluripotent stem cell-derived cardiomyocytes sheets (n = 6). GRA shortened spontaneous activation interval and repolarization period, and decreased maximum contraction velocity, indicating Ca2+ channel blockade. It prolonged effective refractory period and post-repolarization refractoriness with a steep frequency-dependency, whereas it delayed conduction with a modest use-dependency, resembling lidocaine in the mode of Na+ channel-blocking action. Meanwhile, Kanzoto containing a decoction of licorice alone in a dose of 2 or 6g/body/day was orally administered to the conscious chronic atrioventricular block dogs for 3days (n = 4). Kanzoto prolonged QT interval with increasing its temporal dispersion, suggesting K+ channel suppression, and slightly decreased the plasma K+ concentration without inducing torsade de pointes. Moreover, it significantly suppressed atrial and idioventricular rates, leading to sinus arrest along with the onset of ventricular fibrillation in one animal, possibly due to Na+ channel blockade. These results indicate that electropharmacological profile of licorice can be explained by Na+, Ca2+ and K+ channels blockade, which may be associated with low torsadogenic risk, but might contribute to the onset of other types of lethal ventricular arrhythmias.

Quantitative male-to-female translation of cardiac electrophysiological response to drugs.

Despite evidence that women are at higher risk of drug-induced Torsade de Pointes and sudden cardiac death, female sex is vastly underrepresented in cardiovascular research studies and clinical trials, thus limiting our fundamental understanding of sex-specific arrhythmia mechanisms and our ability to predict arrhythmia propensity. Indeed, we have shown that adoption of male-biased metrics can lead to a systematic underestimation of torsadogenic risk in women. To facilitate evaluation of the impact of sex differences on cardiomyocyte function and arrhythmogenesis, we developed quantitative tools that predict the electrophysiological response to drug administration in female cardiomyocytes starting from data collected in males. Using our mathematical models of excitation-contraction coupling in male and female human ventricular myocytes, we generated populations of models by randomly varying baseline model parameters. We collected action potential (AP) and Ca2+ transient (CaT) properties in each model variants and used multivariable regression to construct a “male-to-female translator”, i.e., a set of regression coefficients mapping male features onto female electrophysiology. We validated our translator against independent simulated data sets describing the effects of a large group of drugs on the baseline male and female models. Our results show high accuracy of prediction of drug-induced effects on female AP and CaT, where the average discrepancy between predicted and actual biomarkers falls within 1-2%. Next, we will use "male-to-female" translated features as inputs into our previously developed female-specific classifier of drug-induced torsadogenic risk to test the suitability of our translator for cardiac safety assessment in women. Classifier performance obtained with simulated data as input features will be used as a benchmark. Completion of this work will facilitate the translatability of male-biased experimental observations to female physiology and advance the characterization of sex differences in cardiac electrophysiology and drug cardiotoxicity.

QInward variability-based in-silico proarrhythmic risk assessment of drugs using deep learning model.

Many researchers have suggested evaluation methods and Torsades de Pointes (TdP) metrics to assess the proarrhythmic risk of a drug based on the in silico simulation, as part of the Comprehensive in-vitro Proarrhythmia Assay (CiPA) project. In the previous study, we validated the robustness of 12 in silico features using the ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the in-vitro experimental datasets used; however, the OLR model using 12 in silico features did not provide desirable results. This study proposed a convolutional neural network (CNN) model using the variability of promising in silico TdP metrics hypothesizing that the variability of in silico features based on beats has more information than the single value of in silico features. We performed the action potential (AP) simulation using a human ventricular myocyte model to calculate seven in silico features representing the electrophysiological cell states of drug effects over 1,000 beats: qNet, qInward, intracellular calcium duration at returning to 50% baseline (CaD50) and 90% baseline (CaD90), AP duration at 50% repolarization (APD50) and 90% repolarization (APD90), and dVm/dtMax_repol. The proposed CNN classifier was trained using 12 train drugs and tested using 16 test drugs among CiPA drugs. The torsadogenic risk of drugs was classified as high, intermediate, and low risks. We determined the CNN classifier by comparing the classification performance according to the variabilities of seven in silico biomarkers computed from the in silico drug simulation using the Chantest dataset. The proposed CNN classifier performed the best when using qInward variability to classify the TdP-risk drugs with 0.94 AUC for high risk and 0.93 AUC for low risk. In addition, the final CNN classifier was validated using the qInward variability obtained after merging three in-vitro datasets, but the model performance decreased to a moderate level of 0.75 and 0.78 AUC. These results suggest the need for the proposed CNN model to be trained and tested using various types of drugs.

Cardiac cells stimulated with an axial current-like waveform reproduce electrophysiological properties of tissue fibers

Background and objective: In silico electrophysiological models are generally validated by comparing simulated results with experimental data. When dealing with single-cell and tissue scales simultaneously, as occurs frequently during model development and calibration, the effects of inter-cellular coupling should be considered to ensure the trustworthiness of model predictions. The hypothesis of this paper is that the cell-tissue mismatch can be reduced by incorporating the effects of conduction into the single-cell stimulation current. Methods: Five different stimulation waveforms were applied to the human ventricular O’Hara-Rudy cell model. The waveforms included the commonly used monophasic and biphasic (symmetric and asymmetric) pulses, a triangular waveform and a newly proposed asymmetric waveform (stimulation A) that resembles the transmembrane current associated with AP conduction in tissue. A comparison between single-cell and fiber simulated results was established by computing the relative difference between the values of AP-derived properties at different scales, and by evaluating the differences in the contributions of ionic conductances to each evaluated property. As a proof of the benefit, we investigated multi-scale differences in the simulation of the effects induced by dofetilide, a selective IKr blocker with high torsadogenic risk, on ventricular repolarization at different pacing rates. Results: Out of the five tested stimulation waveforms, stimulation A produced the closest correspondence between cell and tissue simulations in terms of AP properties at steady-state and under dynamic pacing and of ionic contributors to those AP properties. Also, stimulation A reproduced the effects of dofetilide better than the other alternative waveforms, mirroring the ’beat-skipping’ behavior observed at fast pacing rates in experiments with human tissue. Conclusions: The proposed stimulation current waveform accounts for inter-cellular coupling effects by mimicking cell excitation during AP conduction. The proposed waveform improves the correspondence between simulation scales, which could improve the trustworthiness of single-cell simulations without adding computational cost.

Validation of Risk-Stratification Method for the Chronic Atrioventricular Block Cynomolgus Monkey Model and Its Mechanistic Interpretation Using 6 Drugs With Pharmacologically Distinct Profile.

Validation of risk-stratification method for the chronic atrioventricular block cynomolgus monkey model and its mechanistic interpretation was performed using 6 pharmacologically distinct drugs. The following drugs were orally administered in conscious state, astemizole: 1, 5, and 10 mg/kg (n = 6); haloperidol: 1, 10, and 30 mg/kg (n = 5); amiodarone: 30 mg/kg (n = 4); famotidine: 10 mg/kg (n = 4); levofloxacin: 100 mg/kg (n = 4); and tolterodine: 0.2, 1, and 4.5 mg/kg (n = 4). Astemizole of 5 and 10 mg/kg significantly prolonged ΔΔQTcF, whereas no significant change was observed by the others. Torsade de pointes (TdP) was induced by astemizole of 5 and 10 mg/kg in 3/6 and 6/6, and by haloperidol of 10 and 30 mg/kg in 1/5 and 1/5, respectively, which was not observed in the others. Torsadogenic risk of the drugs was quantified using the criteria for the monkey model specified in our previous study. Namely, high-risk drugs induced TdP at ≤ 3 times of their maximum clinical daily dose. Intermediate-risk drugs did not induce TdP at this dose range, but induced it at higher doses. Low/no-risk drugs never induced TdP at any dose tested. The magnitude of risk was intermediate for astemizole and haloperidol, and low/no risk for the others. The prespecified, risk-stratification method for the monkey model may solve the issue existing between nonclinical models and patients with labile repolarization, which can reinforce the regulatory decision-making and labeling at time of marketing application of nondouble-negative drug candidate (hERG assay positive and/or in vivo QT study positive).

Abstract P2109: Elaborating Safety Margins To Predict Drug Proarrhythmia Using Deep Learning And Patient-derived IPSCs.

Introduction: Drug-induced arrhythmias are a common cause for drug attrition during development and for restricted use or withdrawal from the market. Cell based assays to assess arrhythmia risk typically rely on the quantification of waveform features - e.g. action potential prolongation and after depolarizations - in the cells’ action potential. However, the predictive power of these approaches is limited. Hypothesis: We hypothesize that deep learning can extract features relevant to discriminating input classes in a systematic and unbiased manner, effectively removing the need for human-defined metrics. This can lead to a new model to estimate torsadogenic risk of drugs and evaluate the influence of myopathic gene variants on drug-induced arrhythmia. Methods: We optically recorded action potentials optically recorded for 40 drugs - characterized high, intermediate, and low or no torsadogenic risk in patients- at 8 concentrations in hiPSC-CMs from 3 healthy donors and 5 hiPSC-CMs isogenic lines carrying 5 gene variants that cause dilated and hypertrophic cardiomyopathies. We designed a convolutional neural network (CNN) to classify non-arrhythmic, arrhythmic and asystolic traces in hiPSC-CMs. Using the class probabilities measured by the CNN, we created torsadogenic and asystolic safety margins for each drug and cell line. Results: The arrhythmic class probability computed by the CNN, provided a continuous, dose-dependent metric of the proarrhythmic risk of drugs in healthy and cardiomyopathic hiPSC-CMs. We used this metric to estimate safety margins for drug-induced arrhythmia and achieved a 0.942 AUC in classifying drugs of high-intermediate risk from safe ones. We used this approach to discern the contribution of putative genetic risk factors to arrhythmia susceptibility by comparing the risk profiles of the same drugs in healthy and isogenic hiPSC-CMs carrying causal HCM and DCM gene variants that are associated with arrhythmia in patients. Conclusions: We conclude that deep learning algorithms can effectively evaluate proarrhythmic risk of small molecules. Moreover, they can also be used to discern heightened arrhythmic risk caused by genetic mutations that increase the propensity for drug-induced arrhythmia in patients.

Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk

Background and ObjectiveIn silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets. MethodsThe effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database. ResultsPopulation of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of IKr, IKs, and INaK and higher conductances of ICaL, INaL, and INCX, which are more prone to develop TdP. ConclusionsThe methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.

Occurrence of early afterdepolarization under healthy or hypertrophic cardiomyopathy conditions in the human ventricular endocardial myocyte: In silico study using 109 torsadogenic or non-torsadogenic compounds

The goal of the CiPA initiative (Comprehensive in vitro Proarrhythmia Assay) was to assess a more accurate prediction of new drug candidate proarrhythmic severe liabilities such as torsades de pointes, for example. This new CiPA paradigm was partly based on in silico reconstruction of human ventricular cardiomyocyte action potential useful to identify repolarization abnormalities such early afterdepolarization (EAD), for example. Using the ToR-ORd algorithm (Tomek-Rodriguez-O'Hara-Rudy dynamic model), the aim of the present work was (i) to identify intracellular parameters leading to EAD occurrence under healthy and hypertrophic cardiomyopathy (HCM) conditions and (ii) to evaluate the prediction accuracy of compound torsadogenic risk based on EAD occurrence using a large set of 109 torsadogenic and non-torsadogenic compounds under both experimental conditions. In silico results highlighted the crucial involvement of Ca++ handling in the ventricular cardiomyocyte intracellular subspace compartment for the initiation of EAD, demonstrated by a higher amplitude of Ca++ release from junctional sarcoplasmic reticulum to subspace compartments (Jrel) measured at EAD take-off voltage in the presence vs. the absence of EAD initiated either by high IKr inhibition or by high enough concentration of a torsadogenic compound under both experimental conditions. Under healthy or HCM conditions, the prediction accuracy of the torsadogenic risk of compound based on EAD occurrence was observed to be 61 or 92%, respectively. This high accuracy under HCM conditions was discussed regarding its usefulness for cardiac safety pharmacology at least at early drug screening/preclinical stage of the drug development process.

Considering Population Variability of Electrophysiological Models Improves the in Silico Assessment of Drug-Induced Torsadogenic Risk

Considering Population Variability of Electrophysiological Models Improves the in Silico Assessment of Drug-Induced Torsadogenic Risk

Measurement of Early and Late Repolarization Periods in Addition to QT Interval to Help Predict the Torsadogenic Risk of Donepezil Based on Reverse Translational Animal Research on Its Proarrhythmic Potential.

Measurement of Early and Late Repolarization Periods in Addition to QT Interval to Help Predict the Torsadogenic Risk of Donepezil Based on Reverse Translational Animal Research on Its Proarrhythmic Potential.

Measurement of Early and Late Repolarization Periods in Addition to QT Interval to Help Predict the Torsadogenic Risk of Donepezil Based on Reverse Translational Animal Research on Its Proarrhythmic Potential - Reply.

Measurement of Early and Late Repolarization Periods in Addition to QT Interval to Help Predict the Torsadogenic Risk of Donepezil Based on Reverse Translational Animal Research on Its Proarrhythmic Potential - Reply.

Cardiotoxicity assessment using 3D vascularized cardiac tissue consisting of human iPSC-derived cardiomyocytes and fibroblasts

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are used for cardiac safety assessment but have limitations for the evaluation of drug-induced contractility. Three-dimensional (3D) cardiac tissues are similar to native tissue and valuable for the assessment of contractility. However, a longer time and specialized equipment are required to generate 3D tissues. We previously developed a simple method to generate 3D tissue in a short period by coating the cell surfaces with extracellular matrix proteins. We hypothesized that this 3D cardiac tissue could be used for simultaneous evaluation of drug-induced repolarization and contractility. In the present work, we examined the effects of several compounds with different mechanisms of action by cell motion imaging. Consequently, human ether-a-go-go-related gene (HERG) channel blockers with high arrhythmogenic risk caused prolongation of contraction-relaxation duration and arrhythmia-like waveforms. Positive inotropic drugs, which increase intracellular Ca2+ levels or myocardial Ca2+ sensitivity, caused an increase in maximum contraction speed (MCS) or average deformation distance (ADD) (ouabain, 138% for MCS at 300 nM; pimobendane, 132% for ADD at 3 μM). For negative inotropic drugs, verapamil reduced both MCS and ADD (61% at 100 nM). Thus, this 3D cardiac tissue detected the expected effects of various cardiovascular drugs, suggesting its usefulness for cardiotoxicity evaluation.

Sex-Specific Classification of Drug-Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning.

Torsade de Pointes (TdP), a rare but lethal ventricular arrhythmia, is a toxic side effect of many drugs. To assess TdP risk, safety regulatory guidelines require quantification of hERG channel block in vitro and QT interval prolongation in vivo for all new therapeutic compounds. Unfortunately, these have proven to be poor predictors of torsadogenic risk, and are likely to have prevented safe compounds from reaching clinical phases. Although this has stimulated numerous efforts to define new paradigms for cardiac safety, none of the recently developed strategies accounts for patient conditions. In particular, despite being a well-established independent risk factor for TdP, female sex is vastly under-represented in both basic research and clinical studies, and thus current TdP metrics are likely biased toward the male sex. Here, we apply statistical learning to synthetic data, generated by simulating drug effects on cardiac myocyte models capturing male and female electrophysiology, to develop new sex-specific classification frameworks for TdP risk. We show that (i) TdP classifiers require different features in females vs. males; (ii) male-based classifiers perform more poorly when applied to female data; and (iii) female-based classifier performance is largely unaffected by acute effects of hormones (i.e., during various phases of the menstrual cycle). Notably, when predicting TdP risk of intermediate drugs on female simulated data, male-biased predictive models consistently underestimate TdP risk in women. Therefore, we conclude that pipelines for preclinical cardiotoxicity risk assessment should consider sex as a key variable to avoid potentially life-threatening consequences for the female population.

Torsadogenic Action of Cisapride, dl-Sotalol, Bepridil, and Verapamil Analyzed by the Chronic Atrioventricular Block Cynomolgus Monkeys: Comparison With That Reported in the CiPA In Silico Mechanistic Model.

In order to bridge the gap of information between the in silico model and human subjects, we evaluated torsadogenic risk of cisapride, dl-sotalol, bepridil and verapamil selected from 12 training compounds in the comprehensive in vitro proarrhythmia assay using the chronic atrioventricular block monkeys. Cisapride (0, 1, and 5 mg/kg, n = 5 for each dose), dl-sotalol (0, 1, 3, and 10 mg/kg, n = 5 for each dose), bepridil (0, 10, and 100 mg/kg, n = 4 for each dose), verapamil (0, 1.5, 15, and 75 mg/kg, n = 4 for each dose) were orally administered to the monkeys in conscious state. Five mg/kg of cisapride, 1, 3, and 10 mg/kg of dl-sotalol and 100 mg/kg of bepridil prolonged ΔΔQTcF, which was not observed by verapamil. Torsade de pointes was induced by 5 mg/kg of cisapride in 2 out of 5 animals, by 10 mg/kg of dl-sotalol in 5 out of 5 and by 100 mg/kg of bepridil in 2 out of 4, which was not induced by verapamil. These torsadogenic doses were normalized by their maximum clinical daily ones to estimate torsadogenic risk. The order of risk was dl-sotalol >bepridil ≥cisapride >verapamil in our study. Since the order was bepridil ≥dl-sotalol >cisapride >verapamil in comprehensive in vitro proarrhythmia assay (CiPA) in silico mechanistic model validation, sympathetic regulation on the heart may play a pivotal role in the onset of torsade de pointes in vivo.

Defined Solution Corrects Phenotypic Response and Improves Detection Accuracy of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes to the 28 Comprehensive In Vitro Proarrhythmia Assay Standards

We reported that blockade of Ca2+ channel increased beating rate on human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) when in hiPSC-CM culture media, but decreased beating rate if extracellular potassium concentration was elevated above certain threshold. When hiPSC-CMs are used for cardiac safety assessment of drug candidates, a corrected phenotype of Ca2+ channel blockade, particularly for drug candidates possessing multiple cardiac ion channel activities, is expected to minimize exaggerated mitigation of human ether-à-go-go-related gene channel blockage and yield improved accuracy. To further demonstrate this concept, we tested the 28 Comprehensive in vitro Proarrhythmia Assay standards using 10 mM K+ defined solution, and compared the results with data from study using hiPSC-CM culture media. Identical test standard dimethyl sulfoxide stocks, experimental procedure, and data analysis method were used to reduce unrelated variation. We found that in the high-risk category, under 10 mM K+ condition, the early afterdepolarization (EAD)-causing concentrations for azimilide, D, l-sotalol, disopyramide, dofetilide, ibutilide, quinidine, and vandetanib were 14-fold, 0.7-fold, 14-fold, 1.6-fold, 0.01-fold, 3.2-fold, and 33-fold of their respective clinical free therapeutic concentrations. For intermediate-risk standards, 10 mM K+ condition still made hiPSC-CMs more sensitive to detect EADs in cases of cisapride, domperidone, droperidol, and pimozide. Also, the net effect of clozapine was shown to be due to Ca2+ channel blockade. The low-risk standards, metoprolol, mexiletine, and ranolazine no longer stopped hiPSC-CMs beating in 10 mM K+ condition. We conclude that 10 mM K+ condition improved detection accuracy for high- and intermediate-risk compounds, without overestimating torsadogenic risk of low-risk standards. Finally, we proposed an empirical proarrhythmic score system, helping to evaluate torsadogenic liability of drug candidates tested with hiPSC-CMs.

Risk Assessment of Drug-Induced Long QT Syndrome for Some COVID-19 Repurposed Drugs.

The risk‐benefit ratio associated with the use of repurposed drugs to treat severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2)‐related infectious coronavirus disease 2019 (COVID‐19) is complicated because benefits are awaited, not proven. A thorough literature search was conducted to source information on the pharmacological properties of 5 drugs and 1 combination (azithromycin, chloroquine, favipiravir, hydroxychloroquine, remdesivir, and lopinavir/ritonavir) repurposed to treat COVID‐19. A risk assessment of drug‐induced long QT syndrome (LQTS) associated with COVID‐19 repurposed drugs was performed and compared with 23 well‐known torsadogenic and 10 low torsadogenic risk compounds. Computer calculations were performed using pharmacokinetic and pharmacodynamic data, including affinity to block the rapid component of the delayed rectifier cardiac potassium current (IKr) encoded by the human ether‐a‐go‐go gene (hERG), propensity to prolong cardiac repolarization (QT interval) and cause torsade de pointes (TdP). Seven different LQTS indices were calculated and compared. The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database was queried with specific key words relating to arrhythmogenic events. Estimators of LQTS risk levels indicated a very high or moderate risk for all COVID‐19 repurposed drugs with the exception for azithromycin, although cases of TdP have been reported with this drug. There was excellent agreement among the various indices used to assess risk of drug‐induced LQTS for the 6 repurposed medications and 23 torsadogenic compounds. Based on our results, monitoring of the QT interval shall be performed when some COVID‐19 repurposed drugs are used, as such monitoring is possible for hospitalized patients or with the use of biodevices for outpatients.

A web-based tool for the early identification and real time assessment of drug-induced proarrhythmic and torsade de pointes safety risk

Abstract Introduction It is well recognized that early identification of drug-induced proarrhythmic safety risks is crucial to drug development for ethical, animal sparing and costs reduction considerations. The availability, however, of easily accessible, user-friendly tools for real time assessments of the proarrhythmic potential of chemical compounds has been lacking. The novel Tx index, implemented in the presented web-based tool, was applied to a dataset of 84 compounds. Materials and methods The tool is based on 206,766 cellular simulations of compound-induced effects on Action Potential Duration (APD) in isolated endocardial, midmyocardial, and epicardial cells and on 7,072 tissue simulations on QT prolongation in a virtual tissue. Simulations were performed by blocking the slow and the fast components of the delayed rectifier current (IKs and IKr, respectively) and the L-type calcium current (ICaL) at different levels. Based on these simulations, four Tx indices were defined as the ratio of drug concentration leading to a 10% prolongation of the APDendo, APDmid, APDepi or QT over the maximum Effective Free Therapeutic Plasma Concentration (EFTPC), respectively. A dataset of 44 non-torsadogenic and 40 torsadogenic drug compounds was used to validate the performance of the tool. The workflow of the web-based tool was built on the cloud environment, in compliance with the highest standards of security and privacy. hERG test (positive response: hERG pIC50 >6) was applied to the 84 compounds to compare performances. Results Receiver operating characteristic (ROC) curves were constructed on the four estimated Tx indices for each compound in the dataset to enable the identification of torsadogenic potential cut-off values2. These were identified as 8, 8, and 6.4 for Tx-APDendo, Tx-APDmid, Tx-APDepi and as 9.2 for Tx-QT, respectively. The classification of the 84 compounds resulted in an accuracy ranging between 87% and 88% for the four Tx indices Tx-APDendo, Tx-APDmid, Tx-APDepi and Tx-QT. Discussion and conclusion hERG block exhibits poor performance. When applying the hERG test to the 84 compounds, it exhibited a TPR of 55%, a TNR of 89%, and an A of 73%, in close agreement with previous studies. In comparison, the in silico Tx tests described in this study yield TPRs of 85%, TNRs of 86–89% and As of 86–87%. This method does not include drug effects on Na+ channels, which is related to the misclassification of 3 compounds (quetiapine, ranolazine, and lamotrigine – significant Na+ channels blockers at EFTPC). Future work will include this channel. The presented web-based tool is a highly innovative method for an accurate torsadogenic risk assessment. Each assessment required only a few seconds of computational time. Illustration workflow of the web tool Funding Acknowledgement Type of funding source: None

In silico classifier for early screening of drug-induced torsadogenic risk

In silico classifier for early screening of drug-induced torsadogenic risk