Differentiating Drug-Induced Multichannel Block on the Electrocardiogram: Randomized Study of Dofetilide, Quinidine, Ranolazine, and Verapamil

A comprehensive review of 20 years of progress in nonclinical QT evaluation and proarrhythmic assessment

The assessment of drug-induced QT interval prolongation and associated proarrhythmic risks, such as Torsades de Pointes (TdP), has evolved significantly over the past decades. This review traces the development of nonclinical QT evaluation, highlighting key milestones and innovations that have shaped current practices in cardiac safety assessment. The emergence of regulatory guidelines, including International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) S7B, established a nonclinical framework for evaluating drug effects on cardiac repolarization, addressing concerns raised by drug withdrawals in the 1990s. Advances in in vitro, in vivo, and in silico models have enhanced the predictive accuracy of nonclinical studies, with the hERG assay and telemetry-based animal models becoming gold standards. Recent initiatives, such as the Comprehensive in vitro Proarrhythmia Assay (CiPA) and the Japan iPS Cardiac Safety Assessment (JiCSA), emphasize integrating mechanistic insights from human-derived cardiomyocyte models and computational approaches to refine risk predictions. The 2020s mark a shift toward integrated nonclinical-clinical risk assessments, as exemplified by the ICH E14/S7B Questions and Answers. These highlight the need of best practices for study design, data analysis, and interpretation to support regulatory decision-making. Furthermore, the adoption of New Approach Methodologies (NAMs) and reinforced adherence to 3Rs principles (Reduce, Refine, Replace) reflect a commitment to ethical and innovative safety science. This review underscores the importance of harmonized and translational approaches in cardiac safety evaluation, providing a foundation for advancing drug development while safeguarding patient safety. Future directions include further integration of advanced methodologies and regulatory harmonization to streamline nonclinical and clinical risk assessments.

Beyond the linear model in concentration-QT analysis

The white-paper regression model is the standard method for assessing QT liability of drugs. The quantity of interest, placebo-corrected QTc change from baseline (ΔΔQTc) with corresponding confidence interval (CI), is derived from the difference in model-estimated ΔQTc for active compound and placebo in a linear model. Model assumptions include linearity and no time delay between change in concentration and change in ΔQTc. Alternative models are commonly not considered unless there is a clear indication of inappropriateness of the assumptions. This work introduces several extensions for concentration-QT modeling in a pharmacometric context. The model is formulated as linear drug-effect model with treatment, nominal time, and centered baseline as covariates on the intercept. This approach enables straightforward use of other concentration-ΔQTc relationships, including loglinear, Emax, and indirect-effects models. In addition, the setup allows for the use of pharmacometric model assessments for ΔQTc and ΔΔQTc, including visual predictive checks and quantitative model comparison based on the Bayesian information criterion. The proposed approach is applied to several compounds from a previously published QTc study. The results suggest that a nonlinear mixed-effects model for ΔΔQTc and comparing a set of candidate models quantitatively can be a more powerful approach than fitting only the white-paper regression model. A semi-automated approach that compares nonlinear and hysteresis models to the linear model enables a reliable choice of the best model and determination of the degree of prolongation at the concentration of interest. Standard pharmacometric tools can assess the appropriateness of the models and the potential extent of hysteresis.

Elevating performance and interpretability of in silico classifiers for drug proarrhythmia risk evaluations using multi-biomarker approach with ranking algorithm

BACKGROUND AND OBJECTIVE

Using electrophysiological simulations and machine learning to predict drug proarrhythmia risk has gained popularity due to its effectiveness. The leading in silico drug assessment system mainly uses a single biomarker (qNet) to predict proarrhythmia risk, offering good performance and straightforward interpretation. Other advanced classifiers incorporating additional physiological biomarkers provide better predictive capabilities but are less intuitive. Thus, a method that accommodates multiple biomarkers while maintaining interpretability is needed.

METHODS

We enhance the current best ordinal logistic regression (OLR) model by adding more physiological biomarkers to overcome its limitations. We also introduce a general torsade metric score (TMS) for multi-biomarker approaches to facilitate easier interpretation. Additionally, a novel ranking algorithm based on a simple multi-criteria decision analysis method is employed to evaluate various classifiers against standard proarrhythmia risk criteria efficiently.

RESULTS

Our proposed method demonstrates that using multiple well-known biomarkers yields better performance than using qNet alone. Some accepted multi-biomarker OLR models do not incorporate qNet yet outperform those that do. Moreover, some ill-performing biomarkers when utilized individually can show improved performance in combination with other biomarkers.

CONCLUSION

The proposed approach offers an effective way of utilizing multiple biomarkers, including well-known ones, providing practical alternatives for proarrhythmia risk assessment. The interpretability of the accepted models is straightforward, thanks to the TMS thresholds for multi-biomarker OLR models that allow direct evaluation of the classification prediction of individual drugs.

Cardioprotective effects of H3 receptor activation could be double-sided: insights from isoproterenol-induced cardiac injury

Histamine H3 receptors (H3Rs) are known to modulate neurotransmitter release in the nervous system, but their role in cardiac injury remains unclear. The present study aimed to investigate the cardioprotective role of H3Rs in a mouse model of myocardial injury. Forty BALB/c male mice were divided into four groups: Control (SF), Isoproterenol (ISO), Imetit (IMT), and IMT + ISO. The IMT and IMT + ISO groups were pretreated orally with 10 mg/kg imetit-dihydrobromide(imetit) for 7 days. In the last 2 days, the ISO and IMT + ISO groups received a subcutaneous injection of 85 mg/kg isoproterenol to induce myocardial ischemia. Electrocardiogram (ECG) recordings were obtained, and heart tissues were analyzed histopathologically. The results demonstrated that the administration of imetit resulted in the prolongation of the PR interval in the IMT group. QRS and QT intervals were prolonged in the ISO group. The J-wave area in the ISO group was significantly larger than in the other groups. Histopathological analyses revealed the presence of small vacuoles, inflammatory cell infiltration, and collagen aggregates in cardiomyocytes in the ISO group. No significant cellular changes were observed in the IMT group, in contrast. The IMT + ISO group exhibited fewer ischemic findings than the ISO group. Immunohistochemical analyses revealed positive H3R immunoreactivity in all groups. Imetit pretreatment increased the immunoreactivity of H3Rs in both the IMT and IMT + ISO groups. The findings of this study suggest that H3Rs may be present on the postsynaptic side in cardiac myocytes, in addition to adrenergic presynaptic nerve endings. Furthermore, imetit has been found to significantly reduce the effects of myocardial ischemia by activating H3Rs. The better characterization of the postsynaptic role of H3Rs offers potential for the development of new therapeutic strategies.

Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing

Cardiac digital twins are computational tools capturing key functional and anatomical characteristics of patient hearts for investigating disease phenotypes and predicting responses to therapy. When paired with large-scale computational resources and large clinical datasets, digital twin technology can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source automated pipeline for personalising ventricular electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG). Using MRI-based anatomical models, a sequential Monte-Carlo approximate Bayesian computational inference method is extended to infer electrical activation and repolarisation characteristics from the ECG. Fast simulations are conducted with a reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics for repolarisation. For each patient, parameter uncertainty is represented by inferring an envelope of plausible ventricular models rather than a single one, which means that parameter uncertainty can be propagated to therapy evaluation. Furthermore, we have developed techniques for translating from reaction-Eikonal to monodomain simulations, which allows more realistic simulations of cardiac electrophysiology. The pipeline is demonstrated in three healthy subjects, where our inferred pseudo-diffusion reaction-Eikonal models reproduced the patient's ECG with a median Pearson's correlation coefficient of 0.9, and then translated to monodomain simulations with a median correlation coefficient of 0.84 across all subjects. We then demonstrate our digital twins for virtual evaluation of Dofetilide with uncertainty quantification. These evaluations using our cardiac digital twins reproduced dose-dependent QTc and T peak to T end prolongations that are in keeping with large population drug response data. The methodologies for cardiac digital twinning presented here are a step towards personalised virtual therapy testing and can be scaled to generate virtual populations for clinical trials to fast-track therapy evaluation. The tools developed for this paper are open-source, documented, and made publicly available.

In vivo cardiovascular profile of ryanodine receptor 2 inhibitor M201-A: Utility as an anti-atrial fibrillatory drug for patients suffering from heart failure with preserved ejection fraction

Atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF) often coexist; however, clinically available anti-AF drugs can exacerbate symptoms of HFpEF. M201-A suppressed ryanodine receptor-mediated diastolic Ca2+ leakage, possibly inhibiting common pathological processes toward AF and HFpEF. To bridge the basic information to clinical practice, we assessed its cardiohemodynamic, anti-AF and ventricular proarrhythmic profile using halothane-anesthetized dogs (n = 4). M201-A hydrochloride in doses of 0.03, 0.3 and 3 mg/kg/10 min was intravenously administered, providing peak plasma concentrations of 0.09, 0.81 and 5.70 μg/mL, respectively. The high dose of M201-A showed various cardiovascular actions. Namely, M201-A increased mean blood pressure and tended to enhance isovolumetric ventricular relaxation without suppressing ventricular contraction or decreasing cardiac output. M201-A enhanced atrioventricular conduction, but hardy affected intra-atrial/ventricular conduction. Importantly, M201-A prolonged effective refractory period more potently in the atrium than in the ventricle, indicating that it may become an atrial-selective antiarrhythmic drug. Meanwhile, M201-A prolonged QT interval/QTcV, and showed reverse frequency-dependent delay of ventricular repolarization. M201-A prolonged J-Tpeakc without prolonging Tpeak-Tend or terminal repolarization period, indicating the risk of causing torsade de pointes is negligible. Thus, M201-A is expected to become a hopeful therapeutic strategy for patients having pathology of both AF and HFpEF.

Quantitatively Predicting Effects of Exercise on Pharmacokinetics of Drugs Using a Physiologically Based Pharmacokinetic Model

Exercise significantly alters human physiological functions, such as increasing cardiac output and muscle blood flow and decreasing glomerular filtration rate (GFR) and liver blood flow, thereby altering the absorption, distribution, metabolism, and excretion of drugs. In this study, we aimed to establish a database of human physiological parameters during exercise and to construct equations for the relationship between changes in each physiological parameter and exercise intensity, including cardiac output, organ blood flow (e.g., muscle blood flow and kidney blood flow), oxygen uptake, plasma pH and GFR, etc. The polynomial equation P = ΣaiHRi was used for illustrating the relationship between the physiological parameters (P) and heart rate (HR), which served as an index of exercise intensity. The pharmacokinetics of midazolam, quinidine, digoxin, and lidocaine during exercise were predicted by a whole-body physiologically based pharmacokinetic (WB-PBPK) model and the developed database of physiological parameters following administration to 100 virtual subjects. The WB-PBPK model simulation results showed that most of the observed plasma drug concentrations fell within the 5th-95th percentiles of the simulations, and the estimated peak concentrations (Cmax) and area under the curve (AUC) of drugs were also within 0.5-2.0 folds of observations. Sensitivity analysis showed that exercise intensity, exercise duration, medication time, and alterations in physiological parameters significantly affected drug pharmacokinetics and the net effect depending on drug characteristics and exercise conditions. In conclusion, the pharmacokinetics of drugs during exercise could be quantitatively predicted using the developed WB-PBPK model and database of physiological parameters. SIGNIFICANCE STATEMENT: This study simulated real-time changes of human physiological parameters during exercise in the WB-PBPK model and comprehensively investigated pharmacokinetic changes during exercise following oral and intravenous administration. Furthermore, the factors affecting pharmacokinetics during exercise were also revealed.

β-Adrenoceptor blockade can augment the torsadogenic action of risperidone

Risperidone is a second-generation antipsychotic for treating schizophrenia and bipolar disorder. It can potently inhibit IKr, but is classified into conditional risk for torsade de pointes (TdP) by CredibleMeds®. Our previous studies using chronic atrioventricular block dogs showed that risperidone alone did not induce TdP, and that dl-sotalol (β-adrenoceptor blockade plus IKr inhibition) induced TdP three times more frequently than d-sotalol (IKr inhibition alone). Since risperidone can block α1-adrenoceptor and decrease blood pressure, the resulting reflex-mediated increase of sympathetic tone on β-adrenoceptor might protect the heart from its IKr inhibition-associated TdP. To validate this hypothesis, risperidone was administered to chronic atrioventricular block dogs after β-adrenoceptor blocker atenolol infusion with monitoring J-Tpeak and Tpeak-Tend, which are proarrhythmic surrogate markers of "substrate" and "trigger" toward TdP, respectively. Atenolol alone induced TdP in 1 out of 5 dogs; moreover, an additional infusion of risperidone induced TdP in 3 out of 4 dogs. Risperidone prolonged QT interval, J-Tpeak and Tpeak-Tend in animals that induced TdP. These findings indicate that β-adrenoceptor blockade can diminish repolarization reserve to augment risperidone's torsadogenic potential, thus advising caution when using β-adrenoceptor blockers in patients with IKr inhibition-linked labile repolarization.

Single-cell ionic current phenotyping elucidates non-canonical features and predictive potential of cardiomyocytes during automated drug experiments

All new drugs must go through preclinical screening tests to determine their proarrhythmic potential. While these assays effectively filter out dangerous drugs, they are too conservative, often misclassifying safe compounds as proarrhythmic. In this study, we attempt to address this shortcoming with a novel, medium-throughput drug-screening approach: we use an automated patch-clamp system to acquire optimized voltage clamp (VC) and action potential (AP) data from human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) at several drug concentrations (baseline, 3×, 10× and 20× the effective free plasma concentrations). With our novel method, we show correlations between INa block and upstroke slowing after treatment with flecainide or quinine. Additionally, after quinine treatment, we identify significant reductions in current during voltage steps designed to isolate If and IKs. However, we do not detect any IKr block by either drug, and upon further investigation, do not see any IKr present in the iPSC-CMs when prepared for automated patch experiments (i.e. in suspension) - this is in contrast to similar experiments we have conducted with these cells using the manual patch setup. In this study, we: (1) present a proof-of-concept demonstration of a single-cell medium-throughput drug study, and (2) characterize the non-canonical electrophysiology of iPSC-CMs when prepared for experiments in a medium-throughput setting. KEY POINTS: Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer potential as an in vitro model to study the proarrhythmic potential of drugs, but insights from these cells are often limited by the low throughput of manual patch-clamp. In this study, we use a medium-throughput automated patch-clamp system to acquire action potential (AP) and complex voltage clamp (VC) data from single iPSC-CMs at multiple drug concentrations. A correlation between AP upstroke and INa transients was identified and drug-induced changes in ionic currents found. We also characterize the substantially altered physiology of iPSC-CMs when patched in an automated system, suggesting the need to investigate differences between manual and automated patch experiments.

Assessment of corrected JT-peak (JTpc) and Tpeak-to-Tend (TpTec) as proarrhythmia biomarkers in non-human primates: Outcome from a HESI consortium

INTRODUCTION

Corrected QT interval (QTc)is an established biomarker for drug-induced Torsade de Pointe (TdP), but with concerns for a false positive signal. Clinically, JTpc and TpTec have emerged as ECG sub-intervals to differentiate predominant hERG vs. mixed ion channel blocking drugs that prolong QTc.

METHODS

In a multicentric, prospective, controlled study, different proarrhythmic drug effects on QTc, JTpc and TpTec were characterized with cynomolgus monkeys using telemetry in a Lead II configuration for internal and external telemetry.Drugs and vehicle were administered orally (PO) to group size of 4 to 8 animals, in 4 laboratories.

RESULTS

In monkeys, dofetilide (0.03-0.3 mg/kg) was associated with exposure dependent QTc and JTpc increase, but no significant TpTec effect. Similarly, quinidine (2-50 mg/kg) increased QTc and JTpc but did not change TpTec. Mexiletine (1-15 mg/kg) and verapamil (50 mg/kg) did not induce any significant effect on QTc, JTpc or TpTec.

DISCUSSION

Clinically, predominant hERG blockers (dofetilide and quinidine) prolong QTc, JTpc and TpTec and are associated with increased risk for TdP. Results from this study demonstrate that ECG changes after dofetilide and quinidine administration to telemetered monkeys differ from the clinical response, lacking the expected effects on TpTec. Potential explanations for the lack of translation include physio-pharmacology species differences or ECG recording and analysis methodology variations. Mixed ion channel blockers verapamil and mexiletine administered to monkeys showed no significant QTc, JTpc or TpTec prolongation as expected based on the similar clinical response for these agents.

Optimized J to T peak and T peak to T end measurements in nonclinical species administered moxifloxacin and amiodarone

INTRODUCTION

Cardiovascular safety and the risk of developing the potentially fatal ventricular tachyarrhythmia, Torsades de Pointes (TdP), have long been major concerns of drug development. TdP is associated with a delayed ventricular repolarization represented by QT interval prolongation in the electrocardiogram (ECG), typically due to block of the potassium channel encoded by the human ether-a-go-go related gene (hERG). Importantly however, not all drugs that prolong the QT interval are torsadagenic and not all hERG blockers prolong the QT interval. Recent clinical reports suggest that partitioning the QT interval into early (J to T peak; JTp) and late repolarization (T peak to T end; TpTe) components may be valuable for distinguishing low-risk mixed ion channel blockers (hERG plus calcium and/or late sodium currents) from high-risk pure hERG channel blockers. This strategy, if true for nonclinical animal models, could be used to de-risk QT prolonging compounds earlier in the drug development process.

METHODS

To explore this, we investigated JTp and TpTe in ECG data collected from telemetered dogs and/or monkeys administered moxifloxacin or amiodarone at doses targeting relevant clinical exposures. An optimized placement of the Tpeak fiducial mark was utilized, and all intervals were corrected for heart rate (QTc, JTpc, TpTec).

RESULTS

Increases in QTc and JTpc intervals with administration of the pure hERG blocker moxifloxacin and an initial QTc and JTpc shortening followed by prolongation with the mixed ion channel blocker amiodarone were detected as expected, aligning with clinical data. However, anticipated increases in TpTec by both standard agents were not detected.

DISCUSSION

The inability to detect changes in TpTec reduces the utility of these subintervals for prediction of arrhythmias using continuous single‑lead ECGs collected from freely moving dogs and monkeys.

Innovative approaches to cardiovascular safety pharmacology assessment

This editorial prefaces the annual themed issue on safety pharmacology (SP) methods which has been published since 2004 in the Journal of Pharmacological and Toxicological Methods (JPTM). Here we highlight content derived from the 2023 Safety Pharmacology Society (SPS) meeting held in Brussels, Belgium. The meeting generated 138 abstracts, reproduced in the current volume of JPTM. As in prior years, the manuscripts reflect various areas of innovation in SP including in silico modeling of stroke volume, cardiac output and systemic vascular resistance, computational approaches that compare drug-induced proarrhythmic sensitivity of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), an evaluation of the utility of the corrected J-Tpeak and Tpeak-to-Tend parameters from the ECG as potential proarrhythmia biomarkers, and the applicability of nonclinical concentration-QTc (C-QTc) modeling of data derived from the conduct of the in vivo QTc study as a component of the core battery of safety pharmacology studies.

Detecting QT prolongation from a single-lead ECG with deep learning

For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with continuous monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would enable out-of-hospital care. We therefore develop a deep learning model that infers QT intervals from ECG Lead-I-the lead that is often available in ambulatory ECG monitors-and use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. QTNet-a deep neural network that infers QT intervals from Lead-I ECG-was trained using over 3 million ECGs from 653 thousand patients at the Massachusetts General Hospital and tested on an internal-test set consisting of 633 thousand ECGs from 135 thousand patients. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another healthcare institution. On both evaluations, the model achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). Finally, QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. These results show that drug-induced QT prolongation risk can be tracked from ECG Lead-I using deep learning.

A Human-Centered AI Framework for Efficient Labelling of ECGs From Drug Safety Trials

Drug safety trials require substantial ECG labelling like, in thorough QT studies, measurements of the QT interval, whose prolongation is a biomarker of proarrhythmic risk. The traditional method of manually measuring the QT interval is time-consuming and error-prone. Studies have demonstrated the potential of deep learning (DL)-based methods to automate this task but expert validation of these computerized measurements remains of paramount importance, particularly for abnormal ECG recordings. In this paper, we propose a highly automated framework that combines such a DL-based QT estimator with human expertise. The framework consists of 3 key components: (1) automated QT measurement with uncertainty quantification (2) expert review of a few DL-based measurements, mostly those with high model uncertainty and (3) recalibration of the unreviewed measurements based on the expert-validated data. We assess its effectiveness on 3 drug safety trials and show that it can significantly reduce effort required for ECG labelling-in our experiments only 10% of the data were reviewed per trial-while maintaining high levels of QT accuracy. Our study thus demonstrates the possibility of productive human-machine collaboration in ECG analysis without any compromise on the reliability of subsequent clinical interpretations.

Effects of ranolazine on the arrhythmic substrate in hypertrophic cardiomyopathy

Introduction: Hypertrophic cardiomyopathy (HCM) is a leading cause of lethal arrhythmias in the young. Although the arrhythmic substrate has been hypothesised to be amenable to late Na+ block with ranolazine, the specific mechanisms are not fully understood. Therefore, this study aimed to investigate the substrate mechanisms of safety and antiarrhythmic efficacy of ranolazine in HCM. Methods: Computational models of human tissue and ventricles were used to simulate the electrophysiological behaviour of diseased HCM myocardium for variable degrees of repolarisation impairment, validated against in vitro and clinical recordings. S1-S2 pacing protocols were used to quantify arrhythmic risk in scenarios of (i) untreated HCM-remodelled myocardium and (ii) myocardium treated with 3µM, 6µM and 10µM ranolazine, for variable repolarisation heterogeneity sizes and pacing rates. ECGs were derived from biventricular simulations to identify ECG biomarkers linked to antiarrhythmic effects. Results: 10µM ranolazine given to models manifesting ventricular tachycardia (VT) at baseline led to a 40% reduction in number of VT episodes on pooled analysis of >40,000 re-entry inducibility simulations. Antiarrhythmic efficacy and safety were dependent on the degree of repolarisation impairment, with optimal benefit in models with maximum JTc interval <370 ms. Ranolazine increased risk of VT only in models with severe-extreme repolarisation impairment. Conclusion: Ranolazine efficacy and safety may be critically dependent upon the degree of repolarisation impairment in HCM. For moderate repolarisation impairment, reductions in refractoriness heterogeneity by ranolazine may prevent conduction blocks and re-entry. With severe-extreme disease substrates, reductions of the refractory period can increase re-entry sustainability.

A phase 1 trial of AP30663, a KCa2 channel inhibitor in development for conversion of atrial fibrillation

AIMS

AP30663 is a novel compound under development for pharmacological conversion of atrial fibrillation by targeting the small conductance Ca2+ activated K+ (KCa2) channel. The aim of this extension phase 1 study was to test AP30663 at higher single doses compared to the first-in-human trial.

METHODS

Sixteen healthy male volunteers were randomized into 2 cohorts: 6- and 8-mg/kg intravenous single-dose administration of AP30663 vs. placebo. Safety, pharmacokinetic and pharmacodynamic data were collected.

RESULTS

AP30663 was associated with mild and transient infusion site reactions with no clustering of other adverse events but with an estimated maximum mean QTcF interval prolongation of 45.2 ms (95% confidence interval 31.5-58.9) in the 6 mg/kg dose level and 50.4 ms (95% confidence interval 36.7-64.0) with 8 mg/kg. Pharmacokinetics was dose proportional with terminal half-life of around 3 h.

CONCLUSION

AP30663 in doses up to 8 mg/kg was associated with mild and transient infusion site reactions and an increase of the QTcF interval. Supporting Information support that the QTc effect may be explained by an off-target inhibition of the IKr channel.

Assessment of Quinidine-Induced Torsades de Pointes Risks Using a Whole-Body Physiologically Based Pharmacokinetic Model Linked to Cardiac Ionic Current Inhibition

The lethality of torsades de pointes (TdP) by drugs is one of main reasons that some drugs were withdrawn from the market. In order to assess drug-induced TdP risks, a model of cardiac ionic current suppression in human ventricular myocytes (ToR-ORd model), combined with the maximum effective free therapeutic plasma concentration or the maximum effective free therapeutic myocyte concentration was often used, with the latter proved to be more relevant and more accurate. We aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model, incorporated with a human cardiomyocyte pharmacodynamic (PD) model, to provide a comprehensive assessment of drug-induced TdP risks in normal and specific scenarios. Quinidine served as an example to validate the PBPK-PD model via predicting plasma quinidine concentrations and quinidine-induced changes in QT interval (ΔQTc). The predicted plasma quinidine concentrations and ΔQTc values following oral administration or intravenous administration of quinidine were comparable to clinic observations. Visual predictive checks showed that most of the observed plasma concentrations and ΔQTc values fell within the 5th and 95th percentiles of simulations. The validated PBPK-PD model was further applied to assess the TdP risks using frequencies of early afterdepolarization and long-QT syndrome occurrence in 4 scenarios, such as therapeutic dose, supra-therapeutic dose, alkalosis, and hyperkalemia in 200 human subjects. In conclusion, the developed PBPK-PD model may be applied to predict the quinidine pharmacokinetics and quinidine-induced TdP risks in healthy subjects, but also simulate quinidine-induced TdP risks under disease conditions, such as hypokalemia and alkalosis.

Characterization of cardiovascular profile of anti-influenza drug peramivir: A reverse-translational study using the isoflurane-anesthetized dog

An injectable anti-influenza drug peramivir has been reported to induce QT-interval prolongation in some phase III studies, although its thorough QT/QTc study was negative. We investigated the discrepancy among those clinical studies using isoflurane-anesthetized beagle dogs (n = 4). Peramivir in doses of 1 mg/kg/10 min (sub-therapeutic dose) followed by 10 mg/kg/10 min (clinically-relevant dose) was intravenously administered. Peramivir prolonged QT interval/QTcV and Tpeak-Tend, and tended to delay ventricular repolarization in a reverse-frequency dependent manner, indicating IKr inhibition in vivo. Meanwhile, peramivir did not alter P-wave duration, PR interval or QRS width, indicating a lack of impact on cardiac conduction via Na+ or Ca2+ channel inhibition in vivo. Peramivir prolonged Tpeak-Tend and tended to prolong terminal repolarization period, which would develop substrates for initiating and maintaining spiral reentry, respectively. Meanwhile, peramivir did not prolong J-Tpeakc, which could not induce early afterdepolarization, a trigger inducing torsade de pointes. Thus, our results support that clinical dose exposure of peramivir can delay the ventricular repolarization in influenza patients. Peramivir has only a small potential to induce torsade de pointes in patients with the intact hearts, but caution should be paid on its use for patients formerly having the trigger for torsade de pointes.

Toward Digital Twin Technology for Precision Pharmacology

The authors demonstrate the feasibility of technological innovation for personalized medicine in the context of drug-induced arrhythmia. The authors use atomistic-scale structural models to predict rates of drug interaction with ion channels and make predictions of their effects in digital twins of induced pluripotent stem cell-derived cardiac myocytes. The authors construct a simplified multilayer, 1-dimensional ring model with sufficient path length to enable the prediction of arrhythmogenic dispersion of repolarization. Finally, the authors validate the computational pipeline prediction of drug effects with data and quantify drug-induced propensity to repolarization abnormalities in cardiac tissue. The technology is high throughput, computationally efficient, and low cost toward personalized pharmacologic prediction.

Analysis of cardiohemodynamic and electrophysiological effects of morphine along with its toxicokinetic profile using the halothane-anesthetized dogs

Although morphine has been used for treatment-resistant dyspnea in end-stage heart failure patients, information on its cardiovascular safety profile remains limited. Morphine was intravenously administered to halothane-anesthetized dogs (n=4) in doses of 0.1, 1 and 10 mg/kg/10 min with 20 min of observation period. The low and middle doses attained therapeutic (0.13 µg/mL) and supratherapeutic (0.97 µg/mL) plasma concentrations, respectively. The low dose hardly altered any of the cardiovascular variables except that the QT interval was prolonged for 10-15 min after its start of infusion. The middle dose reduced the preload and afterload to the left ventricle for 5-15 min, then decreased the left ventricular contractility and mean blood pressure for 10-30 min, and finally suppressed the heart rate for 15-30 min. Moreover, the middle dose gradually but progressively prolonged the atrioventricular conduction time, QT interval/QTcV, ventricular late repolarization period and ventricular effective refractory period without altering the intraventricular conduction time, ventricular early repolarization period or terminal repolarization period. A reverse-frequency-dependent delay of ventricular repolarization was confirmed. The high dose induced cardiohemodynamic collapse mainly due to vasodilation in the initial 2 animals by 1.9 and 3.3 min after its start of infusion, respectively, which needed circulatory support to treat. The high dose was not tested further in the remaining 2 animals. Thus, intravenously administered morphine exerts a rapidly appearing vasodilator action followed by slowly developing cardiosuppressive effects. Morphine can delay the ventricular repolarization possibly through IKr inhibition in vivo, but its potential to develop torsade de pointes will be small.

Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity

BACKGROUND AND OBJECTIVE

In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods.

METHODS

Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels.

RESULTS

Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K+]o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values.

CONCLUSIONS

This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.

Personalized, intuitive & visual QT-prolongation monitoring using patient-specific QTc threshold with pseudo-coloring and explainable AI

BACKGROUND

Drug-induced QT-prolongation increases the risk of TdP arrhythmia attacks and sudden cardiac death. However, measuring the QT-interval and determining a precise cut-off QT/QTc value that could put a patient at risk of TdP is challenging and influenced by many factors including female sex, drug-free baseline, age, genetic predisposition, and bradycardia.

OBJECTIVES

This paper presents a novel approach for intuitively and visually monitoring QT-prolongation showing a potential risk of TdP, which can be adjusted according to patient-specific risk factors, using a pseudo-coloring technique and explainable artificial intelligence (AI).

METHODS

We extended the development and evaluation of an explainable AI-based technique- visualized using pseudo-color on the ECG signal, thus intuitively 'explaining' how its decision was made -to detect QT-prolongation showing a potential risk of TdP according to a cut-off personalized QTc value (using Bazett's ∆QTc > 60 ms relative to drug-free baseline and Bazett's QTc > 500 ms as examples), and validated its performance using a large number of ECGs (n = 5050), acquired from a clinical trial assessing the effects of four known QT-prolonging drugs versus placebo on healthy subjects. We compared this new personalized approach to our previous study that used a more general approach using the QT-nomogram.

RESULTS AND CONCLUSIONS

The explainable AI-based algorithm can accurately detect QT-prolongation when adjusted to a personalized patient-specific cut-off QTc value showing a potential risk of TdP. Using ∆QTc > 60 ms relative to drug-free baseline and QTc > 500 ms as examples, the algorithm yielded a sensitivity of 0.95 and 0.79, and a specificity of 0.95 and 0.98, respectively. We found that adjusting pseudo-coloring according to Bazett's ∆QTc > 60 ms relative to a drug-free baseline personalized to each patient provides better sensitivity than using Bazett's QTc > 500 ms, which could underestimate a potentially clinically significant QT-prolongation with bradycardia.

Multitargeting in cardioprotection: An example of biaromatic compounds

A multitarget drug design approach is actively developing in modern medicinal chemistry and pharmacology, especially with regard to multifactorial diseases such as cardiovascular diseases, cancer, and neurodegenerative diseases. A detailed study of many well-known drugs developed within the single-target approach also often reveals additional mechanisms of their real pharmacological action. One of the multitarget drug design approaches can be the identification of the basic pharmacophore models corresponding to a wide range of the required target ligands. Among such models in the group of cardioprotectors is the linked biaromatic system. This review develops the concept of a "basic pharmacophore" using the biaromatic pharmacophore of cardioprotectors as an example. It presents an analysis of possible biological targets for compounds corresponding to the biaromatic pharmacophore and an analysis of the spectrum of biological targets for the five most known and most studied cardioprotective drugs corresponding to this model, and their involvement in the biological effects of these drugs.

Characterization of electropharmacological profile of an anti-atrial fibrillatory drug vernakalant along with potential risk toward torsade de pointes: Translational studies using isoflurane-anesthetized dogs and isolated rat aortic preparations

We simultaneously assessed electropharmacological effects of anti-atrial fibrillatory drug vernakalant and its potential risk toward torsade de pointes. Vernakalant hydrochloride in doses of 0.3 and 3 mg/kg/10 min was intravenously administered to isoflurane-anesthetized beagle dogs without (n = 5) and with (n = 4) α-adrenoceptor blockade. Its vascular effect was analyzed using the rat aortae (n = 12). Vernakalant increased total peripheral vascular resistance and preload to left ventricle, leading to transient elevation of mean blood pressure indirectly via non-adrenergic pathway. Vernakalant suppressed sinus automaticity, ventricular contractility and intra-atrial/atrioventricular nodal/intraventricular conductions, and decreased cardiac output. Moreover, vernakalant prolonged atrial/ventricular effective refractory period by 53/55 ms, respectively, whereas it delayed ventricular repolarization in a reverse frequency-dependent manner. The extent of prolongation in early/late ventricular repolarization and electrically vulnerable period was 26/32 and 9 ms, respectively when QT-interval prolongation was the greatest. We compared them with those of known anti-atrial fibrillatory drugs; ranolazine, amiodarone, dronedarone, dl-sotalol and bepridil. The magnitude of vernakalant to alter those variables was the greater among those drugs except that the atrial selectivity was the lesser of those. Thus, vernakalant is expected to be efficacious against atrial fibrillation, but caution should be excised on its use for patients having labile ventricular function and repolarization.

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG

QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can increase the risk of QT prolongation and therefore require strict post-administration monitoring and dosage control. Measurement of the QT interval from the 12-lead electrocardiogram (ECG) by a trained expert, in a clinical setting, is the accepted method for tracking QT prolongation. Recent advances in wearable ECG technology, however, raise the possibility of automated out-of-hospital QT tracking. Applications of Deep Learning (DL) - a subfield within Machine Learning - in ECG analysis holds the promise of automation for a variety of classification and regression tasks. In this work, we propose a residual neural network, QTNet, for the regression of QT intervals from a single lead (Lead-I) ECG. QTNet is trained in a supervised manner on a large ECG dataset from a U.S. hospital. We demonstrate the robustness and generalizability of QTNet on four test-sets; one from the same hospital, one from another U.S. hospital, and two public datasets. Over all four datasets, the mean absolute error (MAE) in the estimated QT interval ranges between 9ms and 15.8ms. Pearson correlation coefficients vary between 0.899 and 0.914. By contrast, QT interval estimation on these datasets with a standard method for automated ECG analysis (NeuroKit2) yields MAEs between 22.29ms and 90.79ms, and Pearson correlation coefficients 0.345 and 0.620. These results demonstrate the utility of QTNet across distinct datasets and patient populations, thereby highlighting the potential utility of DL models for ubiquitous QT tracking.Clinical Relevance- QTNet can be applied to inpatient or ambulatory Lead-I ECG signals to track QT intervals. The method facilitates ambulatory monitoring of patients at risk of QT prolongation.

AI-Assisted QT Measurements for Highly Automated Drug Safety Studies

Rate-corrected QT interval (QTc) prolongation has been suggested as a biomarker for the risk of drug-induced torsades de pointes, and is therefore monitored during clinical trials for the assessment of drug safety. Manual QT measurements by expert ECG analysts are expensive, laborious and prone to errors. Wavelet-based delineators and other automatic methods do not generalize well to different T wave morphologies and may require laborious tuning. Our study investigates the robustness of convolutional neural networks (CNNs) for QT measurement. We trained 3 CNN-based deep learning models on a private ECG database with human expert-annotated QT intervals. Among these models, we propose a U-Net model, which is widely used for segmentation tasks, to build a novel clinically useful QT estimator that includes QT delineation for better interpretability. We tested the 3 models on four external databases, amongst which a clinical trial investigating four drugs. Our results show that the deep learning models are in stronger agreement with the experts than the state-of-the-art wavelet-based algorithm. Indeed, the deep learning models yielded up to 71% of accurate QT measurements (absolute difference between manual and automatic QT below 15 ms) whereas the wavelet-based algorithm only allowed 52% of QT accuracy. For the 2 studies of drugs with small to no QT prolonging effect, a mean absolute difference of 6 ms (std = 5 ms) was obtained between the manual and deep learning methods. For the other 2 drugs with more significant effect on the volunteers, a mean difference of up to 17 ms (std = 17 ms) was obtained. The proposed models are therefore promising for automated QT measurements during clinical trials. They can analyze various ECG morphologies from a diversity of individuals although some QT-prolonged ECGs can be challenging. The U-Net model is particularly interesting for our application as it facilitates expert review of automatic QT intervals, which is still required by regulatory bodies, by providing QRS onset and T offset positions that are consistent with the estimated QT intervals.

New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science

The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.

Cardiovascular safety pharmacology of ivermectin assessed using the isoflurane-anesthetized beagle dogs: ICH S7B follow-up study

Antiparasitic ivermectin has been reported to induce cardiovascular adverse events, including orthostatic hypotension, tachycardia and cardiopulmonary arrest, of which the underlying pathophysiology remains unknown. Since its drug repurposing as an antiviral agent is underway at higher doses than those for antiparasitic, we evaluated the cardiovascular safety pharmacology of ivermectin using isoflurane-anesthetized beagle dogs (n=4). Ivermectin in doses of 0.1 followed by 1 mg/kg was intravenously infused over 10 min with an interval of 20 min, attaining peak plasma concentrations of 0.94 ± 0.04 and 8.82 ± 1.25 μg/mL, which were 29-31 and 276-288 times higher than those observed after its antiparasitic oral dose of 12 mg/body, respectively. The latter peak concentration was > 2 times greater than those inhibiting proliferation of dengue virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and hepatitis B virus in vitro. Ivermectin decreased heart rate without altering mean blood pressure, suggesting that ivermectin does not cause hypotension or tachycardia directly. Ivermectin hardly altered atrioventricular nodal or intraventricular conduction, indicating a lack of inhibitory action on Ca2+ or Na+ channel in vivo. Ivermectin prolonged QT interval/QTcV in a dose-related manner and tended to slow the repolarization speed in a reverse frequency-dependent manner, supporting previously described its IKr inhibition, which would explain Tpeak-Tend prolongation and heart-rate reduction in this study. Meanwhile, ivermectin did not significantly prolong J-Tpeakc or terminal repolarization period, indicating torsadogenic potential of ivermectin leading to the onset of cardiopulmonary arrest would be small. Thus, ivermectin has a broad range of cardiovascular safety profiles, which will help facilitate its drug repurposing.

The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

INTRODUCTION

QTc prolongation is key in diagnosing long QT syndrome (LQTS), however 25%-50% with congenital LQTS (cLQTS) demonstrate a normal resting QTc. T wave morphology (TWM) can distinguish cLQTS subtypes but its role in acquired LQTS (aLQTS) is unclear.

METHODS

Electronic databases were searched using the terms "LQTS," "long QT syndrome," "QTc prolongation," "prolonged QT," and "T wave," "T wave morphology," "T wave pattern," "T wave biomarkers." Whole text articles assessing TWM, independent of QTc, were included.

RESULTS

Seventeen studies met criteria. TWM measurements included T-wave amplitude, duration, magnitude, Tpeak-Tend, QTpeak, left and right slope, center of gravity (COG), sigmoidal and polynomial classifiers, repolarizing integral, morphology combination score (MCS) and principal component analysis (PCA); and vectorcardiographic biomarkers. cLQTS were distinguished from controls by sigmoidal and polynomial classifiers, MCS, QTpeak, Tpeak-Tend, left slope; and COG x axis. MCS detected aLQTS more significantly than QTc. Flatness, asymmetry and notching, J-Tpeak; and Tpeak-Tend correlated with QTc in aLQTS. Multichannel block in aLQTS was identified by early repolarization (ERD30% ) and late repolarization (LRD30% ), with ERD reflecting hERG-specific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching, and inversion in leads II and V5 , left slope in lead V6 ; and COG last 25% in lead I. T wave right slope in lead I and T-roundness achieved this in aLQTS.

CONCLUSION

Numerous TWM biomarkers which supplement QTc assessment were identified. Their diagnostic capabilities include differentiation of genotypes, identification of concealed LQTS, differentiating aLQTS from cLQTS; and determining multichannel versus hERG channel blockade.

Prediction of drug pro-arrhythmic cardiotoxicity using a semi-physiologically based pharmacokinetic model linked to cardiac ionic currents inhibition

Drug-induced torsades de pointes (TdP) risks are responsible for the withdrawal of many drugs from the market. Nowadays, assessments of drug-induced TdP risks are mainly based on maximum effective free therapeutic plasma concentration (EFTPCmax) and cardiac ionic current inhibitions using the human ventricular myocytes model (Tor-ORd model). Myocytes are targets of drug-induced TdP. The TdP risks may be directly linked to myocyte drug concentrations. We aimed to develop a semi-physiologically based pharmacokinetic (Semi-PBPK) model linked to cardiac ionic current inhibition (pharmacodynamics, PD) (Semi-PBPK-PD) to simultaneously predict myocyte drug concentrations and their TdP risks in humans. Alterations in action potential duration (ΔAPD90) were simulated using the Tor-ORd model and ionic current inhibition parameters based on myocyte or plasma drug concentrations. The predicted ΔAPD90 values were translated into in vivo alterations in QT interval(ΔQTc) induced by moxifloxacin, dofetilide, or sotalol. Myocyte drug concentrations of moxifloxacin, dofetilide, and sotalol gave better predictions of ΔQTc than plasma. Following validating the developed semi-PBPK-PD model, TdP risks of 37 drugs were assessed using ΔAPD90 and early afterdepolarization occurrence, which were estimated based on 10 × EFTPCmax and 10 × EFTMCmax (maximum effective free therapeutic myocyte concentration). 10 × EFTMCmax gave more sensitive and accurate predictions of pro-arrhythmic cardiotoxicity and the predicted TdP risks were also closer to clinic practice than 10 × EFTPCmax. In conclusion, pharmacokinetics and TdP risks of 37 drugs were successfully predicted using the semi-PBPK-PD model. Myocyte drug concentrations gave better predictions of ΔQTc and TdP risks than plasma.

Contractility assessment of human iPSC-derived cardiomyocytes by using a motion vector system and measuring cell impedance

Predicting drug-induced cardiotoxicity during the non-clinical stage is important to avoid severe consequences in the clinical trials of new drugs. Human iPSC-derived cardiomyocytes (hiPSC-CMs) hold great promise for cardiac safety assessments in drug development. To date, multi-electrode array system (MEA) has been a widely used as a tool for the assessment of proarrhythmic risk with hiPSC-CMs. Recently, new methodologies have been proposed to assess in vitro contractility, such as the force and velocity of cell contraction, using hiPSC-CMs. Herein, we focused on an imaging-based motion vector system (MV) and an electric cell-substrate impedance sensing system (IMP). We compared the output signals of hiPSC-CMs from MV and IMP in detail and observed a clear correlation between the parameters. In addition, we assessed the effects of isoproterenol and verapamil on hiPSC-CM contraction and identified a correlation in the contractile change of parameters obtained with MV and IMP. These results suggest that both assay systems could be used to monitor hiPSC-CM contraction dynamics.

Predicting human cardiac QT alterations and pro-arrhythmic effects of compounds with a 3D beating heart-on-chip platform

Determining the potential cardiotoxicity and pro-arrhythmic effects of drug candidates remains one of the most relevant issues in the drug development pipeline (DDP). New methods enabling to perform more representative preclinical in vitro studies by exploiting induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) are under investigation to increase the translational power of the outcomes. Here we present a pharmacological campaign conducted to evaluate the drug-induced QT alterations and arrhythmic events on uHeart, a 3D miniaturized in vitro model of human myocardium encompassing iPSC-CM and dermal fibroblasts embedded in fibrin. uHeart was mechanically trained resulting in synchronously beating cardiac microtissues in 1 week, characterized by a clear field potential (FP) signal that was recorded by means of an integrated electrical system. A drug screening protocol compliant with the new International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines was established and uHeart was employed for testing the effect of 11 compounds acting on single or multiple cardiac ion channels and well-known to elicit QT prolongation or arrhythmic events in clinics. The alterations of uHeart's electrophysiological parameters such as the beating period, the FP duration, the FP amplitude, and the detection of arrhythmic events prior and after drug administration at incremental doses were effectively analyzed through a custom-developed algorithm. Results demonstrated the ability of uHeart to successfully anticipate clinical outcome and to predict the QT prolongation with a sensitivity of 83.3%, a specificity of 100% and an accuracy of 91.6%. Cardiotoxic concentrations of drugs were notably detected in the range of the clinical highest blood drug concentration (Cmax), qualifying uHeart as a fit-to-purpose preclinical tool for cardiotoxicity studies.

An in silico-in vitro pipeline for drug cardiotoxicity screening identifies ionic pro-arrhythmia mechanisms

BACKGROUND AND PURPOSE

Before advancing to clinical trials, new drugs are screened for their pro-arrhythmic potential using a method that is overly conservative and provides limited mechanistic insight. The shortcomings of this approach can lead to the mis-classification of beneficial drugs as pro-arrhythmic.

EXPERIMENTAL APPROACH

An in silico-in vitro pipeline was developed to circumvent these shortcomings. A computational human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model was used as part of a genetic algorithm to design experiments, specifically electrophysiological voltage clamp (VC) protocols, to identify which of several cardiac ion channels were blocked during in vitro drug studies. Such VC data, along with dynamically clamped action potentials (AP), were acquired from iPSC-CMs before and after treatment with a control solution or a low- (verapamil), intermediate- (cisapride or quinine) or high-risk (quinidine) drug.

KEY RESULTS

Significant AP prolongation (a pro-arrhythmia marker) was seen in response to quinidine and quinine. The VC protocol identified block of IKr (a source of arrhythmias) by all strong IKr blockers, including cisapride, quinidine and quinine. The protocol also detected block of ICaL by verapamil and Ito by quinidine. Further demonstrating the power of the approach, the VC data uncovered a previously unidentified If block by quinine, which was confirmed with experiments using a HEK-293 expression system and automated patch-clamp.

CONCLUSION AND IMPLICATIONS

We developed an in silico-in vitro pipeline that simultaneously identifies pro-arrhythmia risk and mechanism of ion channel-blocking drugs. The approach offers a new tool for evaluating cardiotoxicity during preclinical drug screening.

Use of high throughput ion channel profiling and statistical modeling to predict off-target arrhythmia risk - One pharma's experience and perspective

INTRODUCTION

The use of high throughput patch clamp profiling to determine mixed ion channel-mediated arrhythmia risk was assessed using profiling data generated using proprietary internal and clinical reference compounds. We define the reproducibility of the platform and highlight inherent platform issues. The data generated was used to develop predictive models for cardiac arrhythmia risk, specifically Torsades de Pointes (TdP).

METHODS

A retrospective analysis was performed using profiling data generated over a 3-year period, including patch clamp data from hERG, Ca_v1.2, and Na_v1.5 (peak/late), together with hERG binding.

RESULTS

Assay reproducibility was robust over the 3-year period examined. High throughput hERG patch IC₅₀ values correlated well with GLP-hERG data (Pearson = 0.87). A disconnect between hERG binding and patch was observed for ∼10% compounds and trended with passive cellular permeability. hERG and Ca_v1.2 potency did not correlate for proprietary compounds, with more potent hERG compounds showing selectivity versus Ca_v1.2. For clinical compounds where hERG and Ca_v1.2 activity was more balanced, an analysis of TdP risk versus hERG/Ca_v1.2 ratio demonstrated low TdP probability when the hERG/Ca_v1.2 potency ratios were < 1. Modeling of clinical compound data revealed a lack of impact of the Na_v1.5 (late) current in predicting TdP. Moreover, models using hERG binding data (ROC AUC = 0.876) showed an improved ability to predict TdP risk versus hERG patch clamp (ROC AUC = 0.787).

DISCUSSION

The data highlight the value of high throughput patch clamp data in the prediction of TdP risk, as well as some potential limitations with this approach.

IKs inhibitor JNJ303 prolongs the QT interval and perpetuates arrhythmia when combined with enhanced inotropy in the CAVB dog

INTRODUCTION

Impaired I_Ks induced by drugs or due to a KCNQ1 mutation, diagnosed as long QT syndrome type 1 (LQT1) prolongs the QT interval and predisposes the heart to Torsade de Pointes (TdP) arrhythmias. The anesthetized chronic AV block (CAVB) dog is inducible for TdP after remodeling and I_Kr inhibitor dofetilide. We tested the proarrhythmic effect of I_Ks inhibition in the CAVB dog, and the proarrhythmic role of increased contractility herein.

METHODS

Dofetilide-inducible animals were included to test the proarrhythmic effect of 1) I_Ks inhibition by JNJ303 (0.63 mg/kg/10min i.v.; n = 4), 2) I_Ks inhibition combined with enhanced inotropy (ouabain, 0.045 mg/kg/1min i.v.; n = 6), and 3) the washout period of the anesthetic regime (n = 10).

RESULTS

JNJ303 prolonged the QTc interval (from 477 ± 53 ms to 565 ± 14 ms, P < 0.02) resembling standardized dofetilide-induced QTc prolongation. Single ectopic beats (n = 4) and ventricular tachycardia (VT) (n = 3) were present, increasing the arrhythmia score (AS) from 1.0 ± 0 to 7.1 ± 6.5. JNJ303 combined with ouabain increased contractile parameters (LVdP/dt_max from 1725 ± 273 to 4147 ± 611 mmHg/s, P < 0.01). Moreover, TdP arrhythmias were induced in 4/6 dogs and AS increased from 1.0 ± 0 to 20.2 ± 19.0 after JNJ303 and ouabain (P < 0.05). Finally, TdP arrhythmias were induced in 4/10 dogs during the anesthesia washout period and the AS increased from 1.1 ± 0.3 to 9.2 ± 11.2.

CONCLUSION

Mimicking LQT1 using I_Ks inhibitor JNJ303 prolongs the QTc interval and triggers ectopic beats and non-sustained VT in the CAVB dog. Induction of the more severe arrhythmic events (TdP) demands a combination of I_Ks inhibition with enhanced inotropy or ending the anesthetic regime.

The Challenges of Predicting Drug-Induced QTc Prolongation in Humans

The content of this article derives from a Health and Environmental Sciences Institute (HESI) consortium with a focus to improve cardiac safety during drug development. A detailed literature review was conducted to evaluate the concordance between nonclinical repolarization assays and the clinical thorough QT (TQT) study. Food and Drug Administration and HESI developed a joint database of nonclinical and clinical data, and a retrospective analysis of 150 anonymized drug candidates was reviewed to compare the performance of 3 standard nonclinical assays with clinical TQT study findings as well as investigate mechanism(s) potentially responsible for apparent discrepancies identified. The nonclinical assays were functional (IKr) current block (Human ether-a-go-go related gene), action potential duration, and corrected QT interval in animals (in vivo corrected QT). Although these nonclinical assays demonstrated good specificity for predicting negative clinical QT prolongation, they had relatively poor sensitivity for predicting positive clinical QT prolongation. After review, 28 discordant TQT-positive drugs were identified. This article provides an overview of direct and indirect mechanisms responsible for QT prolongation and theoretical reasons for lack of concordance between clinical TQT studies and nonclinical assays. We examine 6 specific and discordant TQT-positive drugs as case examples. These were derived from the unique HESI/Food and Drug Administration database. We would like to emphasize some reasons for discordant data including, insufficient or inadequate nonclinical data, effects of the drug on other cardiac ion channels, and indirect and/or nonelectrophysiological effects of drugs, including altered heart rate. We also outline best practices that were developed based upon our evaluation.

Effect of hydroxychloroquine on the cardiac ventricular repolarization: A randomized clinical trial

AIMS

Hydroxychloroquine has been suggested as possible treatment for severe acute respiratory syndrome-coronavirus-2. Studies reported an increased risk of QTcF-prolongation after treatment with hydroxychloroquine. The aim of this study was to analyse the concentration-dependent effects of hydroxychloroquine on the ventricular repolarization, including QTcF-duration and T-wave morphology.

METHODS

Twenty young (≤30 y) and 20 elderly (65-75 y) healthy male subjects were included. Subjects were randomized to receive either a total dose of 2400 mg hydroxychloroquine over 5 days, or placebo (ratio 1:1). Follow-up duration was 28 days. Electrocardiograms (ECGs) were recorded as triplicate at baseline and 4 postdose single recordings, followed by hydroxychloroquine concentration measurements. ECG intervals (RR, QRS, PR, QTcF, J-Tpc, Tp-Te) and T-wave morphology, measured with the morphology combination score, were analysed with a prespecified linear mixed effects concentration-effect model.

RESULTS

There were no significant associations between hydroxychloroquine concentrations and ECG characteristics, including RR-, QRS- and QTcF-interval (P = .09, .34, .25). Mean ΔΔQTcF-interval prolongation did not exceed 5 ms and the upper limit of the 90% confidence interval did not exceed 10 ms at the highest measured concentrations (200 ng/mL). There were no associations between hydroxychloroquine concentration and the T-wave morphology (P = .34 for morphology combination score). There was no significant effect of age group on ECG characteristics.

CONCLUSION

In this study, hydroxychloroquine did not affect ventricular repolarization, including the QTcF-interval and T-wave morphology, at plasma concentrations up to 200 ng/mL. Based on this analysis, hydroxychloroquine does not appear to increase the risk of QTcF-induced arrhythmias.

Kinase inhibitor-induced cardiotoxicity assessed in vitro with human pluripotent stem cell derived cardiomyocytes

Many small molecule kinase inhibitors (SMKIs), used predominately in cancer therapy, have been implicated in serious clinical cardiac adverse events, which means that traditional preclinical drug development assays were not sufficient for identifying these cardiac liabilities. To improve clinical cardiac safety predictions, the effects of SMKIs targeting many different signaling pathways were studied using human pluripotent stem cell derived cardiomyocytes (hPSC-CMs) in combined assays designed for the detection of both electrophysiological (proarrhythmic) and non-electrophysiological (non-proarrhythmic) drug-induced cardiotoxicity. Several microplate-based assays were used to quantitate cell death, apoptosis, mitochondrial damage, energy depletion, and oxidative stress as mechanism-based non-electrophysiological cardiomyocyte toxicities. Microelectrode arrays (MEA) were used to quantitate vitro arrhythmic events (iAEs), field potential duration (FPD) prolongation, and spike amplitude suppression (SAS) as electrophysiological effects. To enhance the clinical relevance, SMKIs induced cardiotoxicities were compared by converting dug concentrations into multiples of reported clinical maximum therapeutic plasma concentration, "FoldC_max", for each assay. The results support the conclusion that the combination of the hPSC-CMs based electrophysiological and non-electrophysiological assays have significantly more predictive value than either assay alone, and significantly more than the current FDA-recommended hERG assay. In addition, the combination of these assays provided mechanistic information relevant to cardiomyocyte toxicities, thus providing valuable information on potential drug-induced cardiotoxicities early in drug development prior to animal and clinical testing. We believe that this early information will be helpful to guide the development of safer and more cost-effective drugs.

Characterization of a high throughput human stem cell cardiomyocyte assay to predict drug-induced changes in clinical electrocardiogram parameters

Human induced pluripotent stem cell derived cardiomyocytes (hIPSC-CM's) play an increasingly important role in the safety profiling of candidate drugs. For such models to have utility a clear understanding of clinical translation is required. In the present study we examined the ability of our hIPSC-CM model to predict the clinically observed effects of a diverse set of compounds on several electrocardiogram endpoints, including changes in QT and QRS intervals. To achieve this, compounds were profiled in a novel high throughput voltage-sensitive dye platform. Measurements were taken acutely (30 min) and chronically (24 h) to ensure that responses from compounds with slow onset kinetics or that affected surface ion channel expression would be captured. In addition, to avoid issues associated with changes in free drug levels due to protein binding, assays were run in serum free conditions. Changes in hIPSC-CM threshold APD₉₀ values correlated with compound plasma exposures that produced a +10 ms change in clinical QTc (Pearson r² = 0.80). In addition, randomForest modeling showed high predictivity in defining TdP risk (AUROC value = 0.938). Risk associated with QRS prolongation correlated with an increase in action potential rise-time (AUROC value = 0.982). The in-depth understanding of the clinical translatability of our hIPSC-CM model positions this assay to play a key role in defining cardiac risk early in drug development. Moreover, the ability to perform longer term studies enables the detection of compounds that may not be highlighted by more acute assay formats, such as inhibitors of hERG trafficking.

Electrocardiogram-based index for the assessment of drug-induced hERG potassium channel block

INTRODUCTION

Drug-induced block of the hERG potassium channel could predispose to torsade de pointes, depending on occurrence of concomitant blocks of the calcium and/or sodium channels. Since the hERG potassium channel block affects cardiac repolarization, the aim of this study was to propose a new reliable index for non-invasive assessment of drug-induced hERG potassium channel block based on electrocardiographic T-wave features.

METHODS

ERD_30% (early repolarization duration) and T_S/A (down-going T-wave slope to T-wave amplitude ratio) features were measured in 22 healthy subjects who received, in different days, doses of dofetilide, ranolazine, verapamil and quinidine (all being hERG potassium channel blockers and the latter three being also blockers of calcium and/or sodium channels) while undergoing continuous electrocardiographic acquisition from which ERD_30% and T_S/A were evaluated in fifteen time points during the 24 h following drug administration ("ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects" database by Physionet). A total of 1320 pairs of ERD_30% and T_S/A measurements, divided in training (50%) and testing (50%) datasets, were obtained. Drug-induced hERG potassium channel block was modelled by the regression equation B_ECG(%) = a·ERD_30% + b·T_S/A+ c·ERD_30%·T_S/A + d; B_ECG(%) values were compared to plasma-based measurements, B_REF(%).

RESULTS

Regression coefficients values, obtained on the training dataset, were: a = -561.0 s^-1, b = -9.7 s, c = 77.2 and d = 138.9. In the testing dataset, correlation coefficient between B_ECG(%) and B_REF(%) was 0.67 (p < 10^-81); estimation error was -11.5 ± 16.7%.

CONCLUSION

B_ECG(%) is a reliable non-invasive index for the assessment of drug-induced hERG potassium channel block, independently from concomitant blocks of other ions.

Symmetric projection attractor reconstruction: Embedding in higher dimensions

Symmetric Projection Attractor Reconstruction (SPAR) provides an intuitive visualization and simple quantification of the morphology and variability of approximately periodic signals. The original method takes a three-dimensional delay coordinate embedding of a signal and subsequently projects this phase space reconstruction to a two-dimensional image with threefold symmetry, providing a bounded visualization of the waveform. We present an extension of the original work to apply delay coordinate embedding in any dimension N≥3 while still deriving a two-dimensional output with some rotational symmetry property that provides a meaningful visualization of the higher dimensional attractor. A generalized result is developed for taking N≥3 delay coordinates from a continuous periodic signal, where we determine invariant subspaces of the phase space that provide a two-dimensional projection with the required rotational symmetry. The result in each subspace is shown to be equivalent to following each pair of coefficients of the trigonometric interpolating polynomial of N evenly spaced points as the signal is translated horizontally. Bounds on the mean and the frequency response of our new coordinates are derived. We demonstrate how this aids our understanding of the attractor properties and its relationship to the underlying waveform. Our generalized result is then extended to real, approximately periodic signals, where we demonstrate that the higher dimensional SPAR method provides information on subtle changes in different parts of the waveform morphology.

Reliable identification of cardiac conduction abnormalities in drug discovery using automated patch clamp II: Best practices for Nav1.5 peak current in a high throughput screening environment

INTRODUCTION

For reliable identification of cardiac safety risk, compounds should be screened for activity on cardiac ion channels in addition to hERG, including Na_V1.5 and Ca_V1.2. We identified different parameters that might affect IC₅₀s of compounds on Na_V1.5 peak and late currents recorded using automated patch clamp (APC) and suggest outlines for best practices.

METHODS

APC instruments SyncroPatch 384 and Patchliner were used to record Na_V1.5 peak and late current. Up to 24 CiPA compounds were used to investigate effects of voltage protocol, holding potential (-80 mV or - 95 mV) and temperature (23 ± 1 °C or 36 ± 1 °C) on IC₅₀ values on hNa_V1.5 overexpressed in HEK or CHO cells either as frozen cells or running cultures.

RESULTS

The IC₅₀s of 18 compounds on the Na_V1.5 peak current recorded on the SyncroPatch 384 using the CiPA step-ramp protocol correlated well with the literature. The use of frozen or cultured cells did not affect IC₅₀s but voltage protocol and holding potential did cause differences in IC₅₀ values. Temperature can affect V_half of inactivation and also compound potency. A compound incubation time of 5-6 min was sufficient for most compounds, however slow acting compounds such as terfenadine required longer to reach maximum effect.

DISCUSSION

We conclude that holding potential, voltage protocol and temperature can affect IC₅₀ values and recommend the use of the CiPA step-ramp protocol at physiological temperature to record Na_V1.5 peak and late currents for cardiac safety. Further recommendations include: a minimum compound incubation time of 5 min, a replicate number of 4 and the use of positive and negative controls for reliable IC₅₀s.

Human-induced pluripotent stem cell-derived cardiomyocytes, 3D cardiac structures, and heart-on-a-chip as tools for drug research

Development of new drugs is of high interest for the field of cardiac and cardiovascular diseases, which are a dominant cause of death worldwide. Before being allowed to be used and distributed, every new potentially therapeutic compound must be strictly validated during preclinical and clinical trials. The preclinical studies usually involve the in vitro and in vivo evaluation. Due to the increasing reporting of discrepancy in drug effects in animal and humans and the requirement to reduce the number of animals used in research, improvement of in vitro models based on human cells is indispensable. Primary cardiac cells are difficult to access and maintain in cell culture for extensive experiments; therefore, the human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) became an excellent alternative. This technology enables a production of high number of patient- and disease-specific cardiomyocytes and other cardiac cell types for a large-scale research. The drug effects can be extensively evaluated in the context of electrophysiological responses with a use of well-established tools, such as multielectrode array (MEA), patch clamp, or calcium ion oscillation measurements. Cardiotoxicity, which is a common reason for withdrawing drugs from marketing or rejection at final stages of clinical trials, can be easily verified with a use of hiPSC-CM model providing a prediction of human-specific responses and higher safety of clinical trials involving patient cohort. Abovementioned studies can be performed using two-dimensional cell culture providing a high-throughput and relatively lower costs. On the other hand, more complex structures, such as engineered heart tissue, organoids, or spheroids, frequently applied as co-culture systems, represent more physiological conditions and higher maturation rate of hiPSC-derived cells. Furthermore, heart-on-a-chip technology has recently become an increasingly popular tool, as it implements controllable culture conditions, application of various stimulations and continuous parameters read-out. This paper is an overview of possible use of cardiomyocytes and other cardiac cell types derived from hiPSC as in vitro models of heart in drug research area prepared on the basis of latest scientific reports and providing thorough discussion regarding their advantages and limitations.

Differentiating multichannel block on the guinea pig ECG: Use of Tpeak-Tend and J-Tpeak

INTRODUCTION

The anaesthetised guinea pig is a well characterised assay for early assessment of drug effects on ventricular repolarisation and risk of Torsade de Pointes (TdP). We assessed whether a selective hERG blocker with known TdP risk could be differentiated from lower risk, balanced ion channel blockers in the guinea pig, using corrected QT (QTc) interval alongside novel electrocardiogram (ECG) biomarkers J-T_peakc and T_peak-T_end. Effects were compared with previous clinical investigations at similar plasma concentrations and with another index of TdP risk, the electromechanical window (EMW).

METHODS

Twenty-two Dunkin Hartley guinea pigs anaesthetised with sodium pentobarbitone were instrumented for haemodynamic measurement and ECG recording. Three ascending doses of vehicle (n = 6), dofetilide (2, 6 or 20 μg/kg; n = 7), ranolazine (2, 6 or 20 mg/kg; n = 5) or verapamil (0.1, 0.3 or 1.0 mg/kg; n = 4) were administered intravenously.

RESULTS

As reported in previous clinical studies, dofetilide induced dose-dependent increases in QTc interval, with increases in both J-T_peakC or T_peak-T_end, while verapamil caused no significant increase in QTc interval, J-T_peakC or T_peak-T_end. Ranolazine caused dose-dependent increases in QTc interval and corrected J-T_peakc, but had no effect on T_peak-T_end, which is in contrast to the effects reported in humans at similar concentrations. Only dofetilide caused a clear, dose-related decrease in the EMW.

DISCUSSION

These findings suggest that measurements of J-T_peakc and T_peak-T_end in addition to QT interval, may help differentiate pure hERG channel blockers with high risk of TdP from lower risk, multichannel blockers.