Allosteric modulators of the hERG K+ channel

The utility of hERG channel inhibition data in the derivation of occupational exposure limits

Inhibition of the human ether-à-go-go (hERG) channel may lead to QT prolongation and fatal arrhythmia. While pharmaceutical drug candidates that exhibit potent hERG channel inhibition often fail early in development, many drugs with both cardiac and non-cardiac indications proceed to market. In this study, the relationship between in vitro hERG channel inhibition and published occupational exposure limit (OEL) was evaluated. A total of 23 cardiac drugs and 44 drugs with non-cardiac indications with published hERG channel IC₅₀ and published OELs were identified. There was an apparent relationship between hERG IC₅₀ potency and the OEL for cardiac and non-cardiac drugs. Twenty cardiac and non-cardiac drugs were identified that had a potent hERG IC₅₀ (≤25 μM) and a contrastingly large OEL value (≥100 μg/m³). OELs or hazard banding corresponding to ≤100 μg/m³ should be sufficiently protective of effects following occupational exposure to the majority of APIs with hERG IC₅₀ values ≤ 100 μM. It is important to consider hERG IC₅₀ values and possible cardiac effects when deriving OEL values for drugs, regardless of indication. These considerations may be particularly important early in the drug development process for establishing exposure control bands for drugs that do not yet have full clinical safety data.

The ERG1 K+ Channel and Its Role in Neuronal Health and Disease

The ERG1 potassium channel, encoded by KCNH2, has long been associated with cardiac electrical excitability. Yet, a growing body of work suggests that ERG1 mediates physiology throughout the human body, including the brain. ERG1 is a regulator of neuronal excitability, ERG1 variants are associated with neuronal diseases (e.g., epilepsy and schizophrenia), and ERG1 serves as a potential therapeutic target for neuronal pathophysiology. This review summarizes the current state-of-the-field regarding the ERG1 channel structure and function, ERG1’s relationship to the mammalian brain and highlights key questions that have yet to be answered.

Predicting hERG channel blockers with directed message passing neural networks

Compounds with human ether-à-go-go related gene (hERG) blockade activity may cause severe cardiotoxicity. Assessing the hERG liability in the early stages of the drug discovery process is important, and the in silico methods for predicting hERG channel blockers are actively pursued. In the present study, the directed message passing neural network (D-MPNN) was applied to construct classification models for identifying hERG blockers based on diverse datasets. Several descriptors and fingerprints were tested along with the D-MPNN model. Among all these combinations, D-MPNN with the moe206 descriptors generated from MOE (D-MPNN + moe206) showed significantly improved performances. The AUC-ROC values of the D-MPNN + moe206 model reached 0.956 ± 0.005 under random split and 0.922 ± 0.015 under scaffold split on Cai's hERG dataset, respectively. Moreover, the comparisons between our models and several recently reported machine learning models were made based on various datasets. Our results indicated that the D-MPNN + moe206 model is among the best classification models. Overall, the excellent performance of the DMPNN + moe206 model achieved in this study highlights its potential application in the discovery of novel and effective hERG blockers.

Compounds with human ether-à-go-go related gene (hERG) blockade activity may cause severe cardiotoxicity.

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.

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.

LUF7244, an allosteric modulator/activator of Kv 11.1 channels, counteracts dofetilide-induced torsades de pointes arrhythmia in the chronic atrioventricular block dog model

BACKGROUND AND PURPOSE

K_v 11.1 (hERG) channel blockade is an adverse effect of many drugs and lead compounds, associated with lethal cardiac arrhythmias. LUF7244 is a negative allosteric modulator/activator of K_v 11.1 channels that inhibits early afterdepolarizations in vitro. We tested LUF7244 for antiarrhythmic efficacy and potential proarrhythmia in a dog model.

EXPERIMENTAL APPROACH

LUF7244 was tested in vitro for (a) increasing human I_Kv11.1 and canine I_Kr and (b) decreasing dofetilide-induced action potential lengthening and early afterdepolarizations in cardiomyocytes derived from human induced pluripotent stem cells and canine isolated ventricular cardiomyocytes. In vivo, LUF7244 was given intravenously to anaesthetized dogs in sinus rhythm or with chronic atrioventricular block.

KEY RESULTS

LUF7244 (0.5-10 μM) concentration dependently increased I_Kv11.1 by inhibiting inactivation. In vitro, LUF7244 (10 μM) had no effects on I_KIR2.1 , I_Nav1.5 , I_Ca-L , and I_Ks , doubled I_Kr , shortened human and canine action potential duration by approximately 50%, and inhibited dofetilide-induced early afterdepolarizations. LUF7244 (2.5 mg·kg^-1 ·15 min^-1 ) in dogs with sinus rhythm was not proarrhythmic and shortened, non-significantly, repolarization parameters (QTc: -6.8%). In dogs with chronic atrioventricular block, LUF7244 prevented dofetilide-induced torsades de pointes arrhythmias in 5/7 animals without normalization of the QTc. Peak LUF7244 plasma levels were 1.75 ± 0.80 during sinus rhythm and 2.34 ± 1.57 μM after chronic atrioventricular block.

CONCLUSIONS AND IMPLICATIONS

LUF7244 counteracted dofetilide-induced early afterdepolarizations in vitro and torsades de pointes in vivo. Allosteric modulators/activators of K_v 11.1 channels might neutralize adverse cardiac effects of existing drugs and newly developed compounds that display QTc lengthening.

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.

Natural products modulating the hERG channel: heartaches and hope

This review covers natural products modulating the hERG potassium channel. Risk assessment strategies, structural features of blockers, and the duality target/antitarget are discussed.

Covering: 1996–December 2016

The human Ether-à-go-go Related Gene (hERG) channel is a voltage-gated potassium channel playing an essential role in the normal electrical activity in the heart. It is involved in the repolarization and termination of action potentials in excitable cardiac cells. Mutations in the hERG gene and hERG channel blockage by small molecules are associated with increased risk of fatal arrhythmias. Several drugs have been withdrawn from the market due to hERG channel-related cardiotoxicity. Moreover, as a result of its notorious ligand promiscuity, this ion channel has emerged as an important antitarget in early drug discovery and development. Surprisingly, the hERG channel blocking profile of natural compounds present in frequently consumed botanicals (i.e. dietary supplements, spices, and herbal medicinal products) is not routinely assessed. This comprehensive review will address these issues and provide a critical compilation of hERG channel data for isolated natural products and extracts over the past two decades (1996–2016). In addition, the review will provide (i) a solid basis for the molecular understanding of the physiological functions of the hERG channel, (ii) the translational potential of in vitro/in vivo results to cardiotoxicity in humans, (iii) approaches for the identification of hERG channel blockers from natural sources, (iv) future perspectives for cardiac safety guidelines and their applications within phytopharmaceuticals and dietary supplements, and (v) novel applications of hERG channel modulation (e.g. as a drug target).

A new hERG allosteric modulator rescues genetic and drug-induced long-QT syndrome phenotypes in cardiomyocytes from isogenic pairs of patient induced pluripotent stem cells

Long-QT syndrome (LQTS) is an arrhythmogenic disorder characterised by prolongation of the QT interval in the electrocardiogram, which can lead to sudden cardiac death. Pharmacological treatments are far from optimal for congenital forms of LQTS, while the acquired form, often triggered by drugs that (sometimes inadvertently) target the cardiac hERG channel, is still a challenge in drug development because of cardiotoxicity. Current experimental models in vitro fall short in predicting proarrhythmic properties of new drugs in humans. Here, we leveraged a series of isogenically matched, diseased and genetically engineered, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from patients to test a novel hERG allosteric modulator for treating congenital LQTS, drug-induced LQTS or a combination of the two. By slowing IK r deactivation and positively shifting IK r inactivation, the small molecule LUF7346 effectively rescued all of these conditions, demonstrating in a human system that allosteric modulation of hERG may be useful as an approach to treat inherited and drug-induced LQTS Furthermore, our study provides experimental support of the value of isogenic pairs of patient hiPSC-CMs as platforms for testing drug sensitivities and performing safety pharmacology.

Allosteric Modulation of Kv11.1 (hERG) Channels Protects Against Drug-Induced Ventricular Arrhythmias

BACKGROUND

Ventricular arrhythmias as a result of unintentional blockade of the Kv11.1 (hERG [human ether-à-go-go-related gene]) channel are a major safety concern in drug development. In past years, several highly prescribed drugs have been withdrawn for their ability to cause such proarrhythmia. Here, we investigated whether the proarrhythmic risk of existing drugs could be reduced by Kv11.1 allosteric modulators.

METHODS AND RESULTS

Using [(3)H]dofetilide-binding assays with membranes of human Kv11.1-expressing human embryonic kidney 293 cells, 2 existing compounds (VU0405601 and ML-T531) and a newly synthesized compound (LUF7244) were found to be negative allosteric modulators of dofetilide binding to the Kv11.1 channel, with LUF7244 showing the strongest effect at 10 μmol/L. The Kv11.1 affinities of typical blockers (ie, dofetilide, astemizole, sertindole, and cisapride) were significantly decreased by LUF7244. Treatment of confluent neonatal rat ventricular myocyte (NRVM) monolayers with astemizole or sertindole caused heterogeneous prolongation of action potential duration and a high incidence of early afterdepolarizations on 1-Hz electric point stimulation, occasionally leading to unstable, self-terminating tachyarrhythmias. Pretreatment of NRVMs with LUF7244 prevented these proarrhythmic effects. NRVM monolayers treated with LUF7244 alone displayed electrophysiological properties indistinguishable from those of untreated NRVM cultures. Prolonged exposure of NRVMs to LUF7244 or LUF7244 plus astemizole did not affect their viability, excitability, and contractility as assessed by molecular, immunological, and electrophysiological assays.

CONCLUSIONS

Allosteric modulation of the Kv11.1 channel efficiently suppresses drug-induced ventricular arrhythmias in vitro by preventing potentially arrhythmogenic changes in action potential characteristics, raising the possibility to resume the clinical use of unintended Kv11.1 blockers via pharmacological combination therapy.

Synthesis and biological evaluation of negative allosteric modulators of the Kv11.1(hERG) channel

We synthesized and evaluated a series of compounds for their allosteric modulation at the Kv11.1 (hERG) channel. Most compounds were negative allosteric modulators of [(3)H]dofetilide binding to the channel, in particular 7f, 7h-j and 7p. Compounds 7f and 7p were the most potent negative allosteric modulators amongst all ligands, significantly increasing the dissociation rate of dofetilide in the radioligand kinetic binding assay, while remarkably reducing the affinities of dofetilide and astemizole in a competitive displacement assay. Additionally, both 7f and 7p displayed peculiar displacement characteristics with Hill coefficients significantly distinct from unity as shown by e.g., dofetilide, further indicative of their allosteric effects on dofetilide binding. Our findings in this investigation yielded several promising negative allosteric modulators for future functional and clinical research with respect to their antiarrhythmic propensities, either alone or in combination with known Kv11.1 blockers.

Computational investigations of hERG channel blockers: New insights and current predictive models

Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel blockers is an essential part of the drug development and drug safety process in pharmaceutical industries or academic drug discovery centers, as they may lead to drug-induced QT prolongation, arrhythmia and Torsade de Pointes. Recent reports also suggest starting to address such issues at the hit selection stage. In order to prioritize molecules during the early drug discovery phase and to reduce the risk of drug attrition due to cardiotoxicity during pre-clinical and clinical stages, computational approaches have been developed to predict the potential hERG blockage of new drug candidates. In this review, we will describe the current in silico methods developed and applied to predict and to understand the mechanism of actions of hERG blockers, including ligand-based and structure-based approaches. We then discuss ongoing research on other ion channels and hERG polymorphism susceptible to be involved in LQTS and how systemic approaches can help in the drug safety decision.

Sensitivity of pharmacokinetic-pharmacodynamic analysis for detecting small magnitudes of QTc prolongation in preclinical safety testing

INTRODUCTION

Preclinical concentration-effect (pharmacokinetic-pharmacodynamic, PKPD) modeling has successfully quantified QT effects of several drugs known for significant QT prolongation. This study investigated its sensitivity for detecting small magnitudes of QT-prolongation in a typical preclinical cardiovascular (CV) safety study in the conscious telemetered dog (crossover study in 4-8 animals receiving a vehicle and three dose levels). Results were compared with conventional statistical analysis (analysis of covariance, ANCOVA).

METHODS

A PKPD model predicting individual QTc was first developed from vehicle arms of 28 typical CV studies and one positive control study (sotalol). The model quantified between-animal, inter-occasion and within-animal variability and described QTc over 24h as a function of circadian variation and drug concentration. This "true" model was used to repeatedly (n = 500) simulate studies with typical drug-induced QTc prolongation (∆QTc) of 1 to 12 ms at high-dose peak concentrations. Simulated studies were re-analyzed by both PKPD analysis (with varying complexity) and ANCOVA. Sensitivity (power) was calculated as the percentage of studies in which a significant (α = 0.05) drug effect was found. One simulation scenario did not include a concentration-effect relationship and served to investigate false-positive rates. Exposure-effect relationships were derived from both PKPD analysis (linear concentration-effect) and ANCOVA (linear trend test for dose) and compared.

RESULTS

PKPD analysis/ANCOVA had a sensitivity of 80% to detect the effects of 7/13 ms (n = 4), 5/10 ms (n = 6) and 4.5/8 ms (n = 8), respectively. The false-positive rate was much higher using ANCOVA (40%) compared to PKPD analysis (1%). Typical drug effects were more precisely predicted using estimated concentration-effect slopes (± 1.5-2.8 ms) than dose-effect slopes (± 3.3-3.7 ms).

DISCUSSION

Preclinical PKPD analysis can increase the confidence in the quantification of small QTc effects and potentially allow reducing the number of animals while maintaining the required study sensitivity. This underscores the value of PKPD modeling in preclinical safety testing.

Kv 11.1 (hERG)-induced cardiotoxicity: a molecular insight from a binding kinetics study of prototypical Kv 11.1 (hERG) inhibitors

BACKGROUND AND PURPOSE

Drug-induced arrhythmia due to blockade of the Kv 11.1 channel (also known as the hERG K(+) channel) is a frequent side effect. Previous studies have primarily focused on equilibrium parameters, i.e. affinity or potency, of drug candidates at the channel. The aim of this study was to determine the kinetics of the interaction with the channel for a number of known Kv 11.1 blockers and to explore a possible correlation with the affinity or physicochemical properties of these compounds.

EXPERIMENTAL APPROACH

The affinity and kinetic parameters of 15 prototypical Kv 11.1 inhibitors were evaluated in a number of [(3) H]-dofetilide binding assays. The lipophilicity (logKW - C8 ) and membrane partitioning (logKW - IAM ) of these compounds were determined by means of HPLC analysis.

KEY RESULTS

A novel [(3) H]-dofetilide competition association assay was set up and validated, which allowed us to determine the binding kinetics of the Kv 11.1 blockers used in this study. Interestingly, the compounds' affinities (Ki values) were correlated to their association rates rather than dissociation rates. Overall lipophilicity or membrane partitioning of the compounds were not correlated to their affinity or rate constants for the channel.

CONCLUSIONS AND IMPLICATIONS

A compound's affinity for the Kv 11.1 channel is determined by its rate of association with the channel, while overall lipophilicity and membrane affinity are not. In more general terms, our findings provide novel insights into the mechanism of action for a compound's activity at the Kv 11.1 channel. This may help to elucidate how Kv 11.1-induced cardiotoxicity is governed and how it can be circumvented in the future.

International Union of Basic and Clinical Pharmacology. XC. multisite pharmacology: recommendations for the nomenclature of receptor allosterism and allosteric ligands

Allosteric interactions play vital roles in metabolic processes and signal transduction and, more recently, have become the focus of numerous pharmacological studies because of the potential for discovering more target-selective chemical probes and therapeutic agents. In addition to classic early studies on enzymes, there are now examples of small molecule allosteric modulators for all superfamilies of receptors encoded by the genome, including ligand- and voltage-gated ion channels, G protein-coupled receptors, nuclear hormone receptors, and receptor tyrosine kinases. As a consequence, a vast array of pharmacologic behaviors has been ascribed to allosteric ligands that can vary in a target-, ligand-, and cell-/tissue-dependent manner. The current article presents an overview of allostery as applied to receptor families and approaches for detecting and validating allosteric interactions and gives recommendations for the nomenclature of allosteric ligands and their properties.