Pharmacophore modeling for hERG channel facilitation

In Silico Assisted Identification, Synthesis, and In Vitro Pharmacological Characterization of Potent and Selective Blockers of the Epilepsy-Associated KCNT1 Channel

Gain-of-function (GoF) variants in KCNT1 channels cause severe, drug-resistant forms of epilepsy. Quinidine is a known KCNT1 blocker, but its clinical use is limited due to severe drawbacks. To identify novel KCNT1 blockers, a homology model of human KCNT1 was built and used to screen an in-house library of compounds. Among the 20 molecules selected, five (CPK4, 13, 16, 18, and 20) showed strong KCNT1-blocking ability in an in vitro fluorescence-based assay. Patch-clamp experiments confirmed a higher KCNT1-blocking potency of these compounds when compared to quinidine, and their selectivity for KCNT1 over hERG and Kv7.2 channels. Among identified molecules, CPK20 displayed the highest metabolic stability; this compound also blocked KCNT2 currents, although with a lower potency, and counteracted GoF effects prompted by 2 recurrent epilepsy-causing KCNT1 variants (G288S and A934T). The present results provide solid rational basis for future design of novel compounds to counteract KCNT1-related neurological disorders.

Structural modeling of hERG channel-drug interactions using Rosetta

The human ether-a-go-go-related gene (hERG) not only encodes a potassium-selective voltage-gated ion channel essential for normal electrical activity in the heart but is also a major drug anti-target. Genetic hERG mutations and blockage of the channel pore by drugs can cause long QT syndrome, which predisposes individuals to potentially deadly arrhythmias. However, not all hERG-blocking drugs are proarrhythmic, and their differential affinities to discrete channel conformational states have been suggested to contribute to arrhythmogenicity. We used Rosetta electron density refinement and homology modeling to build structural models of open-state hERG channel wild-type and mutant variants (Y652A, F656A, and Y652A/F656 A) and a closed-state wild-type channel based on cryo-electron microscopy structures of hERG and EAG1 channels. These models were used as protein targets for molecular docking of charged and neutral forms of amiodarone, nifekalant, dofetilide, d/l-sotalol, flecainide, and moxifloxacin. We selected these drugs based on their different arrhythmogenic potentials and abilities to facilitate hERG current. Our docking studies and clustering provided atomistic structural insights into state-dependent drug-channel interactions that play a key role in differentiating safe and harmful hERG blockers and can explain hERG channel facilitation through drug interactions with its open-state hydrophobic pockets.

Facilitation of hERG Activation by Its Blocker: A Mechanism to Reduce Drug-Induced Proarrhythmic Risk

Modulation of the human Ether-à-go-go-Related Gene (hERG) channel, a crucial voltage-gated potassium channel in the repolarization of action potentials in ventricular myocytes of the heart, has significant implications on cardiac electrophysiology and can be either antiarrhythmic or proarrhythmic. For example, hERG channel blockade is a leading cause of long QT syndrome and potentially life-threatening arrhythmias, such as torsades de pointes. Conversely, hERG channel blockade is the mechanism of action of Class III antiarrhythmic agents in terminating ventricular tachycardia and fibrillation. In recent years, it has been recognized that less proarrhythmic hERG blockers with clinical potential or Class III antiarrhythmic agents exhibit, in addition to their hERG-blocking activity, a second action that facilitates the voltage-dependent activation of the hERG channel. This facilitation is believed to reduce the proarrhythmic potential by supporting the final repolarizing of action potentials. This review covers the pharmacological characteristics of hERG blockers/facilitators, the molecular mechanisms underlying facilitation, and their clinical significance, as well as unresolved issues and requirements for research in the fields of ion channel pharmacology and drug-induced arrhythmias.

hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties

Human ether-a-go-go-related gene (hERG) potassium channel is well-known contributor to drug-induced cardiotoxicity and therefore an extremely important target when performing safety assessments of drug candidates. Ligand-based approaches in connection with quantitative structure active relationships (QSAR) analyses have been developed to predict hERG toxicity. Availability of the recent published cryogenic electron microscopy (cryo-EM) structure for the hERG channel opened the prospect for using structure-based simulation and docking approaches for hERG drug liability predictions. In recent time, the idea of combining structure- and ligand-based approaches for modeling hERG drug liability has gained momentum offering improvements in predictability when compared to ligand-based QSAR practices alone. The present article demonstrates uniting the structure-based SILCS (site-identification by ligand competitive saturation) approach in conjunction with physicochemical properties to develop predictive models for hERG blockade. This combination leads to improved model predictability based on Pearson's R and percent correct (represents rank-ordering of ligands) metric for different validation sets of hERG blockers involving diverse chemical scaffold and wide range of pIC50 values. The inclusion of the SILCS structure-based approach allows determination of the hERG region to which compounds bind and the contribution of different chemical moieties in the compounds to blockade, thereby facilitating the rational ligand design to minimize hERG liability.

Pore opening, not voltage sensor movement, underpins the voltage-dependence of facilitation by a hERG blocker

A drug that blocks the cardiac myocyte voltage-gated K+ channels encoded by the human Ether-à-go-go-Related Gene (hERG) carries a potential risk of long QT syndrome and life-threatening cardiac arrhythmia, including Torsade de Points Interestingly, certain hERG blockers can also facilitate hERG activation to increase hERG currents, which may reduce proarrhythmic potential. However, the molecular mechanism involved in the facilitation effect of hERG blockers remains unclear. The hallmark feature of the facilitation effect by hERG blockers is that a depolarizing preconditioning pulse shifts voltage-dependence of hERG activation to more negative voltages. Here we utilize a D540K hERG mutant to study the mechanism of the facilitation effect. D540K hERG is activated by not only depolarization but also hyperpolarization. This unusual gating property enables tests of the mechanism by which voltage induces facilitation of hERG by blockers. With D540K hERG, we find that nifekalant, a hERG blocker and Class III antiarrhythmic agent, blocks and facilitates not only current activation by depolarization but also current activation by hyperpolarization, suggesting a shared gating process upon depolarization and hyperpolarization. Moreover, in response to hyperpolarizing conditioning pulses, nifekalant facilitates D540K hERG currents but not wild-type currents. Our results indicate that induction of facilitation is coupled to pore opening, not voltage per se We propose that gated access to the hERG central cavity underlies the voltage-dependence of induction of facilitation. This study identifies hERG channel pore gate opening as the conformational change facilitated by nifekalant, a clinically important antiarrhythmic agent. Significance Statement Nifekalant is a clinically important antiarrhythmic agent and a hERG blocker which can also facilitate voltage-dependent activation of hERG channels after a preconditioning pulse. Here we show that the mechanism of action of the preconditioning pulse is to open a conductance gate to enable drug access to a facilitation site. Moreover, we find that facilitation increases hERG currents by altering pore dynamics, rather than acting through voltage sensors.

Cisapride induced hypoglycemia via the KCNH6 potassium channel

Mutations in KCNH6 has been proved to cause hypoinsulinemia and diabetes in human and mice. Cisapride is a stomach-intestinal motility drug used to treat gastrointestinal dysfunction. Cisapride has been reported to be a potential inhibitor of the KCNH family, but it remained unclear whether cisapride inhibited KCNH6. Here, we discovered the role of cisapride on glucose metabolism, focusing on the KCNH6 potassium channel protein. Cisapride reduced blood glucose level and increased serum insulin secretion in wild-type (WT) mice fed standard normal chow/a high-fat diet or in db/db mice, especially when combined with tolbutamide. This effect was much stronger after 4 weeks of intraperitoneal injection. Whole-cell patch-clamp showed that cisapride inhibited KCNH6 currents in transfected HEK293 cells in a concentration-dependent manner. Cisapride induced an increased insulin secretion through the disruption of intracellular calcium homeostasis in a rat pancreatic β-cell line, INS-1E. Further experiments revealed that cisapride did not decrease blood glucose or increase serum insulin in KCNH6 β-cell knockout (Kcnh6-β-KO) mice when compared with WT mice. Cisapride also ameliorated glucose-stimulated insulin secretion (GSIS) in response to high glucose in WT but not Kcnh6-β-KO mice. Thus, our data reveal a novel way for the effect of KCNH6 in cisapride-induced hypoglycemia.

Kite-Shaped Molecules Block SARS-CoV-2 Cell Entry at a Post-Attachment Step

Anti-viral small molecules are currently lacking for treating coronavirus infection. The long development timescales for such drugs are a major problem, but could be shortened by repurposing existing drugs. We therefore screened a small library of FDA-approved compounds for potential severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antivirals using a pseudovirus system that allows a sensitive read-out of infectivity. A group of structurally-related compounds, showing moderate inhibitory activity with IC₅₀ values in the 2–5 μM range, were identified. Further studies demonstrated that these “kite-shaped” molecules were surprisingly specific for SARS-CoV-1 and SARS-CoV-2 and that they acted early in the entry steps of the viral infectious cycle, but did not affect virus attachment to the cells. Moreover, the compounds were able to prevent infection in both kidney- and lung-derived human cell lines. The structural homology of the hits allowed the production of a well-defined pharmacophore that was found to be highly accurate in predicting the anti-viral activity of the compounds in the screen. We discuss the prospects of repurposing these existing drugs for treating current and future coronavirus outbreaks.

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Drug-induced blockade of the human ether-à-go-go-related gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligand-based approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein–ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUC_MAX = 0.86 ± 0.01) and negative predictive values (NPV_MAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein–ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

Kite-shaped molecules block SARS-CoV-2 cell entry at a post-attachment step.. [DOI: 10.1101/2021.05.29.446272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Anti-viral small molecules are currently lacking for treating coronavirus infection. The long development timescales for such drugs are a major problem, but could be shortened by repurposing existing drugs. We therefore screened a small library of FDA-approved compounds for potential severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antivirals using a pseudovirus system that allows a sensitive read-out of infectivity. A group of structurally-related compounds, showing moderate inhibitory activity with IC50values in the 1-5µM range, were identified. Further studies demonstrated that these ‘kite-shaped’ molecules were surprisingly specific for SARS-CoV and SARS-CoV-2 and that they acted early in the entry steps of the viral infectious cycle, but did not affect virus attachment to the cells. Moreover the compounds were able to prevent infection in both kidney- and lung-derived human cell lines. The structural homology of the hits allowed the production of a well-defined pharmacophore that was found to be highly accurate in predicting the anti-viral activity of the compounds in the screen. We discuss the prospects of repurposing these existing drugs for treating current and future coronavirus outbreaks.

Inhibition of the hERG potassium channel by phenanthrene: a polycyclic aromatic hydrocarbon pollutant

The lipophilic polycyclic aromatic hydrocarbon (PAH) phenanthrene is relatively abundant in polluted air and water and can access and accumulate in human tissue. Phenanthrene has been reported to interact with cardiac ion channels in several fish species. This study was undertaken to investigate the ability of phenanthrene to interact with hERG (human Ether-à-go-go-Related Gene) encoded Kv11.1 K⁺ channels, which play a central role in human ventricular repolarization. Pharmacological inhibition of hERG can be proarrhythmic. Whole-cell patch clamp recordings of hERG current (I_hERG) were made from HEK293 cells expressing wild-type (WT) and mutant hERG channels. WT I_hERG1a was inhibited by phenanthrene with an IC₅₀ of 17.6 ± 1.7 µM, whilst I_hERG1a/1b exhibited an IC₅₀ of 1.8 ± 0.3 µM. WT I_hERG block showed marked voltage and time dependence, indicative of dependence of inhibition on channel gating. The inhibitory effect of phenanthrene was markedly impaired by the attenuated inactivation N588K mutation. Remarkably, mutations of S6 domain aromatic amino acids (Y652, F656) in the canonical drug binding site did not impair the inhibitory action of phenanthrene; the Y652A mutation augmented I_hERG block. In contrast, the F557L (S5) and M651A (S6) mutations impaired the ability of phenanthrene to inhibit I_hERG, as did the S624A mutation below the selectivity filter region. Computational docking using a cryo-EM derived hERG structure supported the mutagenesis data. Thus, phenanthrene acts as an inhibitor of the hERG K⁺ channel by directly interacting with the channel, binding to a distinct site in the channel pore domain.

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discovery-related studies.

Astemizole-based turn-on fluorescent probes for imaging hERG potassium channel

Based on the scaffold of astemizole, three novel turn-on fluorescent probes (N1-N3) for human ether-a-go-go-related gene (hERG) potassium channel were developed herein. These probes have reasonable fluorescence properties, acceptable cell toxicity, and potent inhibitory activity, all of which contribute to cell imaging at the nanomolar level. Overall, these probes have the potential for setting up a screening system for hERG channels.

Aggregation-Induced Emission: Lighting Up hERG Potassium Channel

Based on the scaffold of astemizole and E-4031, four AIE light-up probes (L1-L4) for Human Ether-a-go-go-Related Gene (hERG) potassium channel were developed herein using AIE fluorogen(TPE). These probes showing advantages such as low background interference, superior photostability, acceptable cell toxicity, and potent inhibitory activity, which could be used to image hERG channels at the nanomolar level. These AIE light-up probes hoped to provide guidelines for the design of more advanced AIE sensing and imaging hERG channels to a broad range of applications.

Facilitation of I Kr current by some hERG channel blockers suppresses early afterdepolarizations

Some hERG channel blockers are clinically safe, but others cause fatal cardiac arrhythmias. Furutani et al. show that safe blockers facilitate channel opening in ventricular myocytes and provide a repolarization reserve at precisely the voltages and times needed to suppress arrhythmias.

Drug-induced block of the cardiac rapid delayed rectifying potassium current (I_Kr), carried by the human ether-a-go-go-related gene (hERG) channel, is the most common cause of acquired long QT syndrome. Indeed, some, but not all, drugs that block hERG channels cause fatal cardiac arrhythmias. However, there is no clear method to distinguish between drugs that cause deadly arrhythmias and those that are clinically safe. Here we propose a mechanism that could explain why certain clinically used hERG blockers are less proarrhythmic than others. We demonstrate that several drugs that block hERG channels, but have favorable cardiac safety profiles, also evoke another effect; they facilitate the hERG current amplitude in response to low-voltage depolarization. To investigate how hERG facilitation impacts cardiac safety, we develop computational models of I_Kr block with and without this facilitation. We constrain the models using data from voltage clamp recordings of hERG block and facilitation by nifekalant, a safe class III antiarrhythmic agent. Human ventricular action potential simulations demonstrate the ability of nifekalant to suppress ectopic excitations, with or without facilitation. Without facilitation, excessive I_Kr block evokes early afterdepolarizations, which cause lethal arrhythmias. When facilitation is introduced, early afterdepolarizations are prevented at the same degree of block. Facilitation appears to prevent early afterdepolarizations by increasing I_Kr during the repolarization phase of action potentials. We empirically test this prediction in isolated rabbit ventricular myocytes and find that action potential prolongation with nifekalant is less likely to induce early afterdepolarization than action potential prolongation with dofetilide, a hERG channel blocker that does not induce facilitation. Our data suggest that hERG channel blockers that induce facilitation increase the repolarization reserve of cardiac myocytes, rendering them less likely to trigger lethal ventricular arrhythmias.

Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers

Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.

Nortriptyline, a tricyclic antidepressant, inhibits voltage-dependent K+ channels in coronary arterial smooth muscle cells

We demonstrated the effect of nortriptyline, a tricyclic antidepressant drug and serotonin reuptake inhibitor, on voltage-dependent K⁺ (Kv) channels in freshly isolated rabbit coronary arterial smooth muscle cells using a whole-cell patch clamp technique. Nortriptyline inhibited Kv currents in a concentration-dependent manner, with an apparent IC₅₀ value of 2.86±0.52 µM and a Hill coefficient of 0.77±0.1. Although application of nortriptyline did not change the activation curve, nortriptyline shifted the inactivation current toward a more negative potential. Application of train pulses (1 or 2 Hz) did not change the nortriptyline-induced Kv channel inhibition, suggesting that the effects of nortiprtyline were not use-dependent. Preincubation with the Kv1.5 and Kv2.1/2.2 inhibitors, DPO-1 and guangxitoxin did not affect nortriptyline inhibition of Kv channels. From these results, we concluded that nortriptyline inhibited Kv channels in a concentration-dependent and state-independent manner independently of serotonin reuptake.

HD Physiology Project-Japanese efforts to promote multilevel integrative systems biology and physiome research

The HD Physiology Project is a Japanese research consortium that aimed to develop methods and a computational platform in which physiological and pathological information can be described in high-level definitions across multiple scales of time and size. During the 5 years of this project, an appropriate software platform for multilevel functional simulation was developed and a whole-heart model including pharmacokinetics for the assessment of the proarrhythmic risk of drugs was developed. In this article, we outline the description and scientific strategy of this project and present the achievements and influence on multilevel integrative systems biology and physiome research.

Torsades de pointes induced by concomitant use of chlorpheniramine and propranolol: An unusual presentation with no QT prolongation

Drug-induced torsades de pointes (TdP) is a rare but potentially fatal adverse effect of commonly prescribed medications including cardiac and noncardiac drugs. Importantly, many drugs have been reported to cause the characteristic Brugada syndrome-linked electrocardiography (ECG) abnormalities and/or (fatal) ventricular tachyarrhythmias. Chlorpheniramine and propranolol have the arrhythmogenic effects reported previously. A review of literature revealed a large number of case reports of chlorpheniramine or propranolol use resulting in QTc prolongation, TdP, or both. However, we wish to report the case of a patient who was treated with a combination of chlorpheniramine and propranolol, whose ECG showed no QT prolongation but who suffered from cardiac arrest due to TdP.

Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools

The goal of this study was to design, experimentally validate, and apply a virtual screening workflow to identify novel hERG channel blockers. The hERG channel is an important antitarget in drug development since cardiotoxic risks remain as a major cause of attrition. A ligand-based pharmacophore model collection was developed and theoretically validated. The seven most complementary and suitable models were used for virtual screening of in-house and commercially available compound libraries. From the hit lists, 50 compounds were selected for experimental validation through bioactivity assessment using patch clamp techniques. Twenty compounds inhibited hERG channels expressed in HEK 293 cells with IC50 values ranging from 0.13 to 2.77 μM, attesting to the suitability of the models as cardiotoxicity prediction tools in a preclinical stage.

Progress in computational toxicology

INTRODUCTION

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed.

METHODS

A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods.

RESULTS

The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data.

DISCUSSION

Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

Effects of the antitussive drug cloperastine on ventricular repolarization in halothane-anesthetized guinea pigs

Cloperastine is an antitussive drug, which can be received as an over-the-counter cold medicine. The chemical structure of cloperastine is quite similar to that of the antihistamine drug diphenhydramine, which is reported to inhibit hERG K⁺ channels and clinically induce long QT syndrome after overdose. To analyze its proarrhythmic potential, we compared effects of cloperastine and diphenhydramine on the hERG K⁺ channels expressed in HEK293 cells. We further assessed their effects on the halothane-anesthetized guinea-pig heart under the monitoring of monophasic action potential (MAP) of the ventricle. Cloperastine inhibited the hERG K⁺ currents in a concentration-dependent manner with an IC₅₀ value of 0.027 μM, whose potency was 100 times greater than that of diphenhydramine (IC₅₀; 2.7 μM). In the anesthetized guinea pigs, cloperastine at a therapeutic dose of 1 mg/kg prolonged the QT interval and MAP duration without affecting PR interval or QRS width. Diphenhydramine at a therapeutic dose of 10 mg/kg prolonged the QT interval and MAP duration together with increase in PR interval and QRS width. The present results suggest that cloperastine may be categorized as a QT-prolonging drug that possibly induces arrhythmia at overdoses like diphenhydramine does.