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Rationale While meta-analyses of clinical trials found that lurasidone and partial dopamine agonists (brexpiprazole and aripiprazole) were the antipsychotics less likely to cause QTc prolongation, and sertindole, amisulpride, and ziprasidone were the most frequently associated with this adverse drug reaction; no real-world studies have investigated this risk between the different antipsychotics. Objectives and methods Using data recorded from 1967 to 2019 in VigiBase®, the World Health Organization’s Global Individual Case Safety Reports database, we performed disproportionality analysis to investigate the risk of reporting QT prolongation between 20 antipsychotics. Results Sertindole had the highest risk of reporting QT prolongation, followed by ziprasidone and amisulpride. Lurasidone was associated with the lowest risk. First-generation antipsychotics were associated with a greater QT prolongation reporting risk (ROR, 1.21; 95%CI, 1.10–1.33) than second-generation antipsychotics. A positive correlation was found between the risk of reporting QT prolongation and affinity for hERG channel ( R 2  = 0.14, slope = Pearson coefficient = 0.41, p value = 0.1945). Conclusions This large study in a real-world setting suggests that sertindole and ziprasidone were the antipsychotics drugs associated with the highest risk of QT prolongation reporting. Our results suggest that lurasidone is less associated with QT interval prolongation reports. Our study also suggests that antipsychotics with the higher hERG affinity are more associated with to QT prolongations reports.

In the human heart, the rapid delayed rectifier K+ current (IKr) contributes significantly to ventricular action potential (AP) repolarization and to set the duration of the QT interval of the surface electrocardiogram (ECG). The pore‐forming (α) subunit of the IKr channel is encoded by KCNH2 or human ether‐à‐go‐go‐related gene 1 (hERG1). Impairment of hERG function through either gene mutation (congenital) or pharmacological blockade by diverse drugs in clinical use (acquired) can cause a prolongation of the AP duration (APD) reflected onto the surface ECG as a prolonged QT interval or Long QT Syndrome (LQTS). LQTS can increase the risk of triggered activity of ventricular cardiomyocytes and associated life‐threatening arrhythmia. Current treatments all focus on reducing the incidence of arrhythmia or terminating it after its onset but there is to date no prophylactic treatment for the pharmacological management of LQTS. A new class of hERG modulators (agonists) have been suggested through direct interaction with the hERG channel to shorten the action potential duration (APD) and/or increase the postrepolarisation refractoriness period (PRRP) of ventricular cardiomyocytes protecting thereby against triggered activity and associated arrhythmia. Although promising drug candidates, there remain major obstacles to their clinical development. The aim of this review is to summarize the latest advances as well as the limitations of this proposed pharmacotherapy. Pharmacological activation of the hERG potassium channel for the management of the Long QT Syndrome (LQTS) is a promising strategy. There are however still major drawbacks to its pre‐clinical development. This review will highlight the benefits as well as limitations of the proposed strategy for the management of LQTS.

The unique in-frame exon 6 of the HERG gene as a potential target for antisense oligonucleotide-mediated exon skipping therapy.The unique in-frame exon 6 of the HERG gene as a potential target for antisense oligonucleotide-mediated exon skipping therapy.

Mathematical models of ion channels, which constitute indispensable components of action potential models, are commonly constructed by fitting to whole-cell patch-clamp data. In a previous study, we fitted cell-specific models to hERG1a (Kv11.1) recordings simultaneously measured using an automated high-throughput system, and studied cell-cell variability by inspecting the resulting model parameters. However, the origin of the observed variability was not identified. Here, we study the source of variability by constructing a model that describes not just ion current dynamics, but the entire voltage-clamp experiment. The experimental artefact components of the model include: series resistance, membrane and pipette capacitance, voltage offsets, imperfect compensations made by the amplifier for these phenomena, and leak current. In this model, variability in the observations can be explained by either cell properties, measurement artefacts, or both. Remarkably, by assuming that variability arises exclusively from measurement artefacts, it is possible to explain a larger amount of the observed variability than when assuming cell-specific ion current kinetics. This assumption also leads to a smaller number of model parameters. This result suggests that most of the observed variability in patch-clamp data measured under the same conditions is caused by experimental artefacts, and hence can be compensated for in post-processing by using our model for the patch-clamp experiment. This study has implications for the question of the extent to which cell-cell variability in ion channel kinetics exists, and opens up routes for better correction of artefacts in patch-clamp data. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

Purpose The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play a significant role during action potential repolarization of cardiomyocytes. Blockade of the hERG channel by off-target drugs can lead to long QT syndrome, significantly increasing the risk of proarrhythmic cardiotoxicity. Traditional hERG screening methods are effort-demanding and time-consuming. Thus, it is essential to develop computational methods to utilize the existing knowledge for faster and more accurate in silico screening. Although with wide use of deep learning/machine learning algorithms, existing computational models often rely on manually defined atomic features to represent atom nodes, which may overlook critical underlying information. Thus, we want to provide a new method to learn the atom representation automatically. Methods We first developed an automated atom embedding model using deep neural networks (DNNs), trained with 118,312 compounds collected from the ZINC database. We then trained a Graph neural networks (GNNs) model with 7909 ChEMBL compounds as the classifying part. The integration of our atom embedding model and GNN models formed a classifier that could effectively distinguish between hERG inhibitors and non-inhibitors. Results Our atom embedding model achieved 0.93 accuracy in representing structures. Our best GNN model achieved an accuracy of 0.84 and outcompeted traditional machine-learning models, as well as published AI-driven models, in external testing. Conclusions These results highlight the potential of our automated atom embedding model as a standard for generating robust molecular representations. Its integration with advanced GNN algorithms offers promising assistance for screening hERG inhibitors and accelerating drug discovery and repurposing.

The bulb of Fritillaria species called “Bei Mu” is a well-known traditional Chinese medicine. We have reported some potential off-target effects of “Bei Mu” due to peimine’s blockade of hERG (human Ether-a-go-go-Related Gene) channels. This research investigated the modulatory effects of three major alkaloid analogs of “Bei Mu” and their cooperative effects on hERG channels using manual whole-cell patch-clamp techniques. Results showed that peiminine and sipeimine blocked hERG currents with IC50s of 36.8 ± 2.5 μM and 47.6 ± 9.8 μM, which were close to that of peimine (26.1 ± 3.5 μM). Peiminine-induced blockade increased with increasing depolarizing strengths, durations, and frequencies, which suggested a preferential binding to open or inactivated states. The reduced blockade by the less inactivating S631A mutation supported peiminine‘s inactivation preference. Molecular docking and dynamics simulations confirmed the hERG-blocking activities of the three alkaloids and provided further insight into potential mechanisms. We also discovered antagonistic effects of the three alkaloids at nearly all concentrations tested, which might help reduce potential cardiotoxicities. To our knowledge, this is the first study to investigate combination effects of chemicals from one herb on hERG channels. In conclusion, peiminine and sipeimine can block hERG channels in a way similar to peimine, but antagonistic effects exist among them.

Ischemic heart disease is a significant public health problem with high mortality and morbidity. Extensive scientific investigations from basic sciences to clinics revealed multilevel alterations from metabolic imbalance, altered electrophysiology, and defective Ca2+/Na+ homeostasis leading to lethal arrhythmias. Despite the recent identification of numerous molecular targets with potential therapeutic interest, a pragmatic observation on the current pharmacological R&D output confirms the lack of new therapeutic offers to patients. By contrast, from recent trials, molecules initially developed for other fields of application have shown cardiovascular benefits, as illustrated with some anti-diabetic agents, regardless of the presence or absence of diabetes, emphasizing the clear advantage of “old” drug repositioning. Ranolazine is approved as an antianginal agent and has a favorable overall safety profile. This drug, developed initially as a metabolic modulator, was also identified as an inhibitor of the cardiac late Na+ current, although it also blocks other ionic currents, including the hERG/Ikr K+ current. The latter actions have been involved in this drug’s antiarrhythmic effects, both on supraventricular and ventricular arrhythmias (VA). However, despite initial enthusiasm and promising development in the cardiovascular field, ranolazine is only authorized as a second-line treatment in patients with chronic angina pectoris, notwithstanding its antiarrhythmic properties. A plausible reason for this is the apparent difficulty in linking the clinical benefits to the multiple molecular actions of this drug. Here, we review ranolazine’s experimental and clinical knowledge on cardiac metabolism and arrhythmias. We also highlight advances in understanding novel effects on neurons, the vascular system, skeletal muscles, blood sugar control, and cancer, which may open the way to reposition this “old” drug alone or in combination with other medications.

To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been the resolution of discrete conformational states of transmembrane ion channel proteins. An example is K V 11.1 (hERG), comprising the primary cardiac repolarizing current, I kr . hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. While prior studies have applied AlphaFold to predict alternative protein conformations, we show that the inclusion of carefully chosen structural templates can guide these predictions toward distinct functional states. This targeted modeling approach is validated through comparisons with experimental data, including proposed state-dependent structural features, drug interactions from molecular docking, and ion conduction properties from molecular dynamics simulations. Remarkably, AlphaFold not only predicts inactivation mechanisms of the hERG channel that prevent ion conduction but also uncovers novel molecular features explaining enhanced drug binding observed during inactivation, offering a deeper understanding of hERG channel function and pharmacology. Furthermore, leveraging AlphaFold-derived states enhances computational screening by significantly improving agreement with experimental drug affinities, an important advance for hERG as a key drug safety target where traditional single-state models miss critical state-dependent effects. By mapping protein residue interaction networks across closed, open, and inactivated states, we identified critical residues driving state transitions validated by prior mutagenesis studies. This innovative methodology sets a new benchmark for integrating deep learning-based protein structure prediction with experimental validation. It also offers a broadly applicable approach using AlphaFold to predict discrete protein conformations, reconcile disparate data, and uncover novel structure–function relationships, ultimately advancing drug safety screening and enabling the design of safer therapeutics.

The voltage-gated potassium channel, K V 11.1, encoded by the human Ether-à-go-go -Related Gene (hERG), is expressed in cardiac myocytes, where it is crucial for the membrane repolarization of the action potential. Gating of the hERG channel is characterized by rapid, voltage-dependent, C-type inactivation, which blocks ion conduction and is suggested to involve constriction of the selectivity filter. Mutations S620T and S641A/T within the selectivity filter region of hERG have been shown to alter the voltage dependence of channel inactivation. Because hERG channel blockade is implicated in drug-induced arrhythmias associated with both the open and inactivated states, we used Rosetta to simulate the effects of hERG S620T and S641A/T mutations to elucidate conformational changes associated with hERG channel inactivation and differences in drug binding between the two states. Rosetta modeling of the S641A fast-inactivating mutation revealed a lateral shift of the F627 side chain in the selectivity filter into the central channel axis along the ion conduction pathway and the formation of four lateral fenestrations in the pore. Rosetta modeling of the non-inactivating mutations S620T and S641T suggested a potential molecular mechanism preventing F627 side chain from shifting into the ion conduction pathway during the proposed inactivation process. Furthermore, we used Rosetta docking to explore the binding mechanism of highly selective and potent hERG blockers - dofetilide, terfenadine, and E4031. Our structural modeling correlates well with much, but not all, existing experimental evidence involving interactions of hERG blockers with key residues in hERG pore and reveals potential molecular mechanisms of ligand interactions with hERG in an inactivated state.

Cardiotoxicity, particularly drug-induced arrhythmias, poses a significant challenge in drug development, highlighting the importance of early-stage prediction of human ether-a-go-go-related gene (hERG) toxicity. hERG encodes the pore-forming subunit of the cardiac potassium channel. Traditional methods are both costly and time-intensive, necessitating the development of computational approaches. In this study, we introduce AttenhERG, a novel graph neural network framework designed to predict hERG channel blockers reliably and interpretably. AttenhERG demonstrates improved performance compared to existing methods with an AUROC of 0.835, showcasing its efficacy in accurately predicting hERG activity across diverse datasets. Additionally, uncertainty evaluation analysis reveals the model's reliability, enhancing its utility in drug discovery and safety assessment. Case studies illustrate the practical application of AttenhERG in optimizing compounds for hERG toxicity, highlighting its potential in rational drug design. Scientific contribution AttenhERG is a breakthrough framework that significantly improves the interpretability and accuracy of predicting hERG channel blockers. By integrating uncertainty estimation, AttenhERG demonstrates superior reliability compared to benchmark models. Two case studies, involving APH1A and NMT1 inhibitors, further emphasize AttenhERG's practical application in compound optimization.