Modernized Classification of Cardiac Antiarrhythmic Drugs.

BACKGROUND: Among his major cardiac electrophysiological contributions, Miles Vaughan Williams (1918-2016) provided a classification of antiarrhythmic drugs that remains central to their clinical use. METHODS: We survey implications of subsequent discoveries concerning sarcolemmal, sarcoplasmic reticular, and cytosolic biomolecules, developing an expanded but pragmatic classification that encompasses approved and potential antiarrhythmic drugs on this centenary of his birth. RESULTS: We first consider the range of pharmacological targets, tracking these through to cellular electrophysiological effects. We retain the original Vaughan Williams Classes I through IV but subcategorize these divisions in light of more recent developments, including the existence of Na+ current components (for Class I), advances in autonomic (often G protein-mediated) signaling (for Class II), K+ channel subspecies (for Class III), and novel molecular targets related to Ca2+ homeostasis (for Class IV). We introduce new classes based on additional targets, including channels involved in automaticity, mechanically sensitive ion channels, connexins controlling electrotonic cell coupling, and molecules underlying longer-term signaling processes affecting structural remodeling. Inclusion of this widened range of targets and their physiological sequelae provides a framework for a modernized classification of established antiarrhythmic drugs based on their pharmacological targets. The revised classification allows for the existence of multiple drug targets/actions and for adverse, sometimes actually proarrhythmic, effects. The new scheme also aids classification of novel drugs under investigation. CONCLUSIONS: We emerge with a modernized classification preserving the simplicity of the original Vaughan Williams framework while aiding our understanding and clinical management of cardiac arrhythmic events and facilitating future developments in this area.

Compilation and physicochemical classification analysis of a diverse hERG inhibition database.

A large and chemically diverse hERG inhibition data set comprised of 6690 compounds was constructed on the basis of ChEMBL bioactivity database and original publications dealing with experimental determination of hERG activities using patch-clamp and competitive displacement assays. The collected data were converted to binary format at 10 µM activity threshold and subjected to gradient boosting machine classification analysis using a minimal set of physicochemical and topological descriptors. The tested parameters involved lipophilicity (log P), ionization (pK a ), polar surface area, aromaticity, molecular size and flexibility. The employed approach allowed classifying the compounds with an overall 75-80 % accuracy, even though it only accounted for non-specific interactions between hERG and ligand molecules. The observed descriptor-response profiles were consistent with common knowledge about hERG ligand binding site, but also revealed several important quantitative trends, as well as slight inter-assay variability in hERG inhibition data. The results suggest that even weakly basic groups (pK a  < 6) might substantially contribute to hERG inhibition potential, whereas the role of lipophilicity depends on the compound's ionization state, and the influence of log P decreases in the order of bases > zwitterions > neutrals > acids. Given its robust performance and clear physicochemical interpretation, the proposed model may provide valuable information to direct drug discovery efforts towards compounds with reduced risk of hERG-related cardiotoxicity.

The specific slow afterhyperpolarization inhibitor UCL2077 is a subtype-selective blocker of the epilepsy associated KCNQ channels.

Mutations in members of the KCNQ channel family underlie multiple diseases affecting the nervous and cardiovascular systems. Despite their clinical relevance, research into these channels is limited by the lack of subtype-selective inhibitors, making it difficult to differentiate the physiological function of each family member in vivo. We have proposed that KCNQ channels might partially underlie the calcium-activated slow afterhyperpolarization (sAHP), a neuronal conductance whose molecular components are uncertain. Here, we investigated whether 3-(triphenylmethylaminomethyl)pyridine (UCL2077), identified previously as an inhibitor of the sAHP in neurons, acts on members of the KCNQ family expressed in heterologous cells. We found that 3 µM UCL2077 strongly inhibits KCNQ1 and KCNQ2 channels and weakly blocks KCNQ4 channels in a voltage-independent manner. In contrast, UCL2077 potentiates KCNQ5 channels at more positive membrane potentials, with little effect at negative membrane potentials. We found that the effect of UCL2077 on KCNQ3 is bimodal: currents are enhanced at negative membrane potentials and inhibited at positive potentials. We found that UCL2077 facilitates KCNQ3 currents by inducing a leftward shift in the KCNQ3 voltage-dependence, a shift dependent on tryptophan 265. Finally, we show that UCL2077 has intermediate effects on KCNQ2/3 heteromeric channels compared with KCNQ2 and KCNQ3 homomers. Together, our data demonstrate that UCL2077 acts on KCNQ channels in a subtype-selective manner. This feature should make UCL2077 a useful tool for distinguishing KCNQ1 and KCNQ2 from less-sensitive KCNQ family members in neurons and cardiac cells in future studies.

Inhibitors of potassium channels KV1.3 and IK-1 as immunosuppressants.

New structural classes of K(V)1.3 and IK-1 ion channel blockers have been identified based on a virtual high throughput screening approach using a homology model of KcsA. These compounds display inhibitory effects on T-cell and/or keratinocyte proliferation and immunosuppressant activity within a DTH animal model.

Tuning out of hERG.

A number of drug withdrawals in recent years have been related to cardiovascular toxicity associated with undesirable blockade of the hERG potassium channel. A promiscuous target, hERG has been demonstrated to interact with pharmaceuticals of widely varying structure. Computational and statistical modeling efforts encompassing homology modeling, pharmacophore and quantitative structure-activity relationship models, and also various classification methods, are aimed at defining the molecular features that confer hERG inhibitory activity and understanding the structure-activity relationships that govern hERG-drug interactions. The organization-wide adoption of hERG models is driven by their ability to produce specific and testable structural hypotheses that lead to compounds devoid of hERG liability.

A binary QSAR model for classification of hERG potassium channel blockers.

Acquired long QT syndrome causes severe cardiac side effects and represents a major problem in clinical studies of drug candidates. One of the reasons for development of arrhythmias related to long QT is inhibition of the human ether-a-go-go-related-gene (hERG) potassium channel. Therefore, early prediction of hERG K(+) channel affinity of drug candidates is becoming increasingly important in the drug discovery process. Binary QSAR models with threshold values at IC(50)=1 and of 10 microM, respectively, were generated using two different sets of descriptors. One set comprising 32 P_VSA descriptors and the other one utilizing a set of descriptors identified out of a large set via a feature selection algorithm. For the full dataset, the power for classification of hERG blockers was 82-88%, which meets prior classification models. Considering the fact that 2D descriptors are fast and easy to calculate, these binary QSAR models are versatile tools for use in virtual screening protocols.

Support vector machines classification of hERG liabilities based on atom types.

Drug-induced long QT syndrome (LQTS) can cause critical cardiovascular side effects and has accounted for the withdrawal of several drugs from the market. Blockade of the potassium ion channel encoded by the human ether-a-go-go-related gene (hERG) has been identified as a major contributor to drug-induced LQTS. Experimental measurement of hERG activity for each compound in development is costly and time-consuming, thus it is beneficial to develop a predictive hERG model. Here, we present a hERG classification model formulated using support vector machines (SVM) as machine learning method and using atom types as molecular descriptors. The training set used in this study was composed of 977 corporate compounds with hERG activities measured under the same conditions. The impact of soft margin and kernel function on the performance of the SVM models was examined. The robustness of SVM was evaluated by comparing the predictive power of the models built with 90%, 50%, and 10% of the training set data. The final SVM model was able to correctly classify 94% of an external testing set containing 66 drug molecules. The most important atom types with respect to discriminative power were extracted and analyzed.

Effects of taurine on aortic rings isolated from fructose-fed insulin resistance Sprague-Dawley rat are changed.

PURPOSE: To observe and compare the effect of taurine on contractions of aortic rings isolated from normal (NC) and insulin resistance (IR) Sprague-Dawley rats, and to explore its underlying mechanism(s). METHODS: The IR animal model was made by feeding rats with high fructose diet for 8 weeks. Aortic rings were isolated and suspended in a tissue bath, and tensions were recorded isometrically. The effects of taurine on provoked contractions of the rings were assessed in absence or presence of different potassium channel or NO-synthase inhibitors. RESULTS: Taurine (20-80 mM) concentration-dependently relaxed precontractions induced by KCl (30 mM) and phenylephrine (1 microM) in NC rings, but enhanced the precontractions in IR rings. Denudation of the endothelium and pretreatment with N(G)-nitro-L-arginine methylester ester (0.1 mM) reversed the contraction enhancement of taurine to relaxation in IR rings. Tetraethylammonium (10 mM) nearly abolished taurine-induced relaxation of NC rings, and augmented taurine-induced contraction enhancement in IR rings. Iberiotoxin (100 nM) only augmented the contraction enhancement in IR rings. 4-Aminopyridine (1 mM), glibenclamide (10 microM) and indomethacin (10 muM) had no influence on the effect of taurine in both NC and IR rings. CONCLUSION: Taurine enhances contractions in IR aortic rings but relaxes the contractions in normal rat aortic ring; the enhancement is endothelium-dependent and the relaxation is endothelium-independent. TEA-sensitive K(+) channel may be involved in these actions; BK(Ca) channel dysfunction and endothelium-derived substances may be related to the contraction enhancement induced by taurine in IR aorta.

Construction of an integrated database for hERG blocking small molecules.

The inhibition of the hERG potassium channel is closely related to the prolonged QT interval, and thus assessing this risk could greatly facilitate the development of therapeutic compounds and the withdrawal of hazardous marketed drugs. The recent increase in SAR information about hERG inhibitors in public databases has led to many successful applications of machine learning techniques to predict hERG inhibition. However, most of these reports constructed their prediction models based on only one SAR database because the differences in the data format and ontology hindered the integration of the databases. In this study, we curated the hERG-related data in ChEMBL, PubChem, GOSTAR, and hERGCentral, and integrated them into the largest database about hERG inhibition by small molecules. Assessment of structural diversity using Murcko frameworks revealed that the integrated database contains more than twice as many chemical scaffolds for hERG inhibitors than any of the individual databases, and covers 18.2% of the Murcko framework-based chemical space occupied by the compounds in ChEMBL. The database provides the most comprehensive information about hERG inhibitors and will be useful to design safer compounds for drug discovery. The database is freely available at http://drugdesign.riken.jp/hERGdb/.

Predictive in silico modeling for hERG channel blockers.

hERG-mediated sudden death as a side effect of non-antiarrhythmic drugs has been receiving increased regulatory attention. Perhaps owing to the unique shape of the ligand-binding site and its hydrophobic character, the hERG channel has been shown to interact with pharmaceuticals of widely varying structure. Several in silico approaches have attempted to predict hERG channel blockade. Some of these approaches are aimed primarily at filtering out potential hERG blockers in the context of virtual libraries, others involve understanding structure-activity relationships governing hERG-drug interactions. This review summarizes the most recent efforts in this emerging field.

HERG-Lite: a novel comprehensive high-throughput screen for drug-induced hERG risk.

INTRODUCTION: Direct block of I(Kr) by non-antiarrhythmic drugs (NARDs) is a major cause of QT prolongation and torsades de pointes (TdP), and has made the hERG potassium channel a major target of drug safety programs in cardiotoxicity. Block of hERG currents is not the only way that drugs can adversely impact the repolarizing current I(Kr), however. We have shown recently that two drugs in clinical use do not block hERG but produce long QT syndrome (LQTS) and TdP by inhibiting trafficking of hERG to the cell surface. To address the need for an inexpensive, rapid, and comprehensive assay to predict both types of hERG risk early in the drug development process, we have developed a novel antibody-based chemiluminescent assay called HERG-Lite. METHODS: HERG-Lite monitors the expression of hERG at the cell surface in two different stable mammalian cell lines. One cell line acts as a biosensor for drugs that inhibit hERG trafficking, while the other predicts hERG blockers based on their ability to act as pharmacological chaperones. In this study, we have validated the HERG-Lite assay using a panel of 100 drugs: 50 hERG blockers and 50 nonblockers. RESULTS: HERG-Lite correctly predicted hERG risk for all 100 test compounds with no false positives or negatives. All 50 hERG blockers were detected as drugs with hERG risk in the HERG-Lite assay, and fell into two classes: B (for blocker) and C (for complex; block and trafficking inhibition). DISCUSSION: HERG-Lite is the most comprehensive assay available for predicting drug-induced hERG risk. It accurately predicts both channel blockers and trafficking inhibitors in a rapid, cost-effective manner and is a valuable non-clinical assay for drug safety testing.

A large number of novel Ergtoxin-like genes and ERG K+-channels blocking peptides from scorpions of the genus Centruroides.

Twenty-three novel sequences similar to Ergtoxin (ErgTx) were obtained by direct sequencing of peptides or deduced from gene cloned using cDNAs of venomous glands of Centruroides (C.) elegans, C. exilicauda, C. gracilis, C. limpidus limpidus, C. noxius and C. sculpturatus. These peptides have from 42 to 47 amino acid residues cross-linked by four disulfide bridges. They share sequence similarities (60-98% compared with ErgTx1) and were shown to block ERG K(+)-channels of F-11 clone (N18TG-2xrat DRG) cultured cells. An unrooted phylogenetic tree analysis of these peptides showed that they conform at least five different subfamilies, of which three are novel subfamilies.

Pharmacological modulation of I(Ks): potential for antiarrhythmic therapy.

The slowly (I(Ks)) and rapidly (I(Kr)) activating delayed rectifier K(+) currents play important roles in cardiac ventricular repolarization. Compared with I(Kr), however, I(Ks) has important distinguishing characteristics, including beta-adrenergic receptor stimulation and accumulation at rapid rates that may impart significant therapeutic relevance. Therefore, development of selective I(Ks) inhibitors has been pursued as a strategy for providing potentially safer and more effective Class III antiarrhythmic agents and pharmacological tools for elucidating the normal physiological and potential pathological role of I(Ks) in cardiac repolarization. We have identified a series of 3-Acylamino-1,4 benzodiazepines that are very potent and selective inhibitors of I(Ks). A representative compound, L-768,673 (1) (IC(50)~8nM), has been extensively characterized for its pharmacologic activity. L-768,673 concentration-dependently prolongs action potential duration in a frequency-independent manner in vitro, but decreases transmural dispersion of refractoriness, a risk factor for arrhythmia induction. In conscious dogs, L 768,673 administered IV (0.3-100 micro g/kg) and PO (0.03-1 mg/kg) elicits consistent but limited (5-15%) QT(c) prolongation, and increases ventricular refractory period more at fast than at slow pacing rates, indicating a "forward" rate-dependence in vivo. In an anesthetized canine model of anterior myocardial infarction, I(Ks) blockers suppress the development of ischemic ventricular fibrillation at intravenous doses that minimally prolong the QT interval. I(Ks) blockers display an interesting and intriguing profile of effects on cardiac electrophysiologic parameters that differ in remarkable ways from other selective Class III agents such as I(Kr) blockers. It remains to be determined if these properties can be exploited clinically to provide more effective and safer treatment of cardiac arrhythmias.

Theoretical possibilities for the development of novel antiarrhythmic drugs.

One possible mechanism of action of the available K-channel blocking agents used to treat arrhythmias is to selectively inhibit the HERG plus MIRP channels, which carry the rapid delayed rectifier outward potassium current (I(Kr)). These antiarrhythmics, like sotalol, dofetilide and ibutilide, have been classified as Class III antiarrhythmics. However, in addition to their beneficial effect, they substantially lengthen ventricular repolarization in a reverse-rate dependent manner. This latter effect, in certain situations, can result in life-threatening polymorphic ventricular tachycardia (torsades de pointes). Selective blockers (chromanol 293B, HMR-1556, L-735,821) of the KvLQT1 plus minK channel, which carriy the slow delayed rectifier potassium current (I(Ks)), were also considered to treat arrhythmias, including atrial fibrillation (AF). However, I(Ks) activates slowly and at a more positive voltage than the plateau of the action potential, therefore it remains uncertain how inhibition of this current would result in a therapeutically meaningful repolarization lengthening. The transient outward potassium current (I(to)), which flows through the Kv 4.3 and Kv 4.2 channels, is relatively large in the atrial cells, which suggests that inhibition of this current may cause substantial prolongation of repolarization predominantly in the atria. Although it was reported that some antiarrhythmic drugs (quinidine, disopyramide, flecainide, propafenone, tedisamil) inhibit I(to), no specific blockers for I(to) are currently available. Similarly, no specific inhibitors for the Kir 2.1, 2.2, 2.3 channels, which carry the inward rectifier potassium current (I(kl)), have been developed making difficult to judge the possible beneficial effects of such drugs in both ventricular arrhythmias and AF. Recently, a specific potassium channel (Kv 1.5 channel) has been described in human atrium, which carries the ultrarapid, delayed rectifier potassium current (I(Kur)). The presence of this current has not been observed in the ventricular muscle, which raises the possibility that by specific inhibition of this channel, atrial repolarization can be lengthened without similar effect in the ventricle. Therefore, AF could be terminated and torsades de pointes arrhythmia avoided. Several compounds were reported to inhibit I(Kur)(flecainide, tedisamil, perhexiline, quinidine, ambasilide, AVE 0118), but none of them can be considered as specific for Kv 1.5 channels. Similarly to Kv 1.5 channels, acetylcholine activated potassium channels carry repolarizing current (I(KAch)) in the atria and not in the ventricle during normal vagal tone and after parasympathetic activation. Specific blockers of I(KAch) can, therefore, also be a possible candidate to treat AF without imposing proarrhythmic risk on the ventricle. At present several compounds (amiodarone, dronedarone, aprindine, pirmenol, SD 3212) were shown to inhibit I(KAch) but none of them proved to be selective. Further research is needed to develop specific K-channel blockers, such as I(Kur)and I(KAch) inhibitors, and to establish their possible therapeutic value.

Potassium channels in T lymphocytes: therapeutic targets for autoimmune disorders?

Human peripheral blood T lymphocytes possess two types of K(+) channels: the voltage-gated Kv1.3 and the calcium-activated IKCa1 channels. The use of peptidyl inhibitors of Kv1.3 and IKCa1 indicated that these channels are involved in the maintenance of membrane potential and that they play a crucial role in Ca(2+) signaling during T-cell activation. Thus, in vitro blockade of Kv1.3 and IKCa1 leads to inhibition of cytokine production and lymphocyte proliferation. These observations prompted several groups of investigators in academia and pharmaceutical companies to characterize the expression of Kv1.3 and IKCa1 in different subsets of human T lymphocytes and to evaluate their potential as novel targets for immunosuppression. Recent in vivo studies showed that chronically activated T lymphocytes involved in the pathogenesis of multiple sclerosis present unusually high expression of Kv1.3 channels and that the treatment with selective Kv1.3 inhibitors can either prevent or ameliorate the symptoms of the disease. In this model of multiple sclerosis, blockade of IKCa1 channels had no effect alone, but improved the response to Kv1.3 inhibitors. In addition, the expression of Kv1.3 and IKCa1 channels in human cells is very restricted, which makes them attractive targets for a more cell-specific and less harmful action than what is typically obtained with classical immunosuppressants. Studies using high-throughput toxin displacement, (86)Rb-efflux screening or membrane potential assays led to the identification of non-peptidyl small molecules with high affinity for Kv1.3 or IKCa1 channels. Analysis of structure-function relationships in Kv1.3 and IKCa1 channels helped define the binding sites for channel blockers, allowing the design of a new generation of small molecules with selectivity for either Kv1.3 or IKCa1, which could help the development of new drugs for safer treatment of auto-immune diseases.

Accelerated evolution and functional divergence of scorpion short-chain K+ channel toxins after speciation.

The α-KTx14 subfamily of scorpion toxins is a group of short-chain polypeptides affecting K(+) channels, including five known members which are restrictedly distributed in Mesobuthus martensii. Here, we describe seven new α-KTx14 peptides from M. martensii and its sibling species Mesobuthus eupeus, two of which (termed MarKTX-3 and MeuKTX-1) were chemically synthesized and refolded for structural and functional studies. Electrophysiological recordings of effects of these two peptides on an array of voltage-gated potassium channels revealed that MarKTX-3 was capable of inhibiting five mammalian K(v)1 isoforms (rK(v)1.1-rK(v)1.5) and the Drosophila Shaker channel with low potency whereas MeuKTX-1 lacks such activity. Circular dichroism spectroscopy analysis combined with homology modeling demonstrates that MarKTX-3 and MeuKTX-1 both adopt a similar cysteine-stabilized α-helical and ß-sheet fold. Evolutionary analysis indicates accelerated amino acid substitutions in the mature-peptide-encoding regions of orthologous α-KTx14 peptides after speciation, thereby providing evidences for adaptive evolution and functional divergence of this subfamily.

A potent potassium channel blocker from Mesobuthus eupeus scorpion venom.

Scorpion venom-derived peptidyl toxins are valuable pharmacological tools for investigating the structure-function relationship of ion channels. Here, we report the purification, sequencing and functional characterization of a new K(+) channel blocker (MeuKTX) from the venom of the scorpion Mesobuthus eupeus. Effects of MeuKTX on ten cloned potassium channels in Xenopus oocytes were evaluated using two-electrode voltage-clamp recordings. MeuKTX is the orthologue of BmKTX (α-KTx3.6), a known Kv1.3 blocker from the scorpion Mesobuthus martensii, and classified as α-KTx3.13. MeuKTX potently blocks rKv1.1, rKv1.2 and hKv1.3 channels with 50% inhibitory concentration (IC(50)) of 203.15 ± 4.06 pM, 8.92 ± 2.3 nM and 171 ± 8.56 pM, respectively, but does not affect rKv1.4, rKv1.5, hKv3.1, rKv4.3, and hERG channels even at 2 µM concentration. At this high concentration, MeuKTX is also active on rKv1.6 and Shaker IR. Our results also demonstrate that MeuKTX and BmKTX have the same channel spectrum and similar pharmacological potency. Analysis of the structure-function relationships of α-KTx3 subfamily toxins allows us to recognize several key sites which may be useful for designing toxins with improved activity on hKv1.3, an attractive target for T-cell mediated autoimmune diseases.

Combining cluster analysis, feature selection and multiple support vector machine models for the identification of human ether-a-go-go related gene channel blocking compounds.

Blockade of the human ether-a-go-go related gene potassium channel is regarded as a major cause of drug toxicity and associated with severe cardiac side-effects. A variety of in silico models have been reported to aid in the identification of compounds blocking the human ether-a-go-go related gene channel. Herein, we present a classification approach for the detection of diverse human ether-a-go-go related gene blockers that combines cluster analysis of training data, feature selection and support vector machine learning. Compound learning sets are first divided into clusters of similar molecules. For each cluster, independent support vector machine models are generated utilizing preselected MACCS structural keys as descriptors. These models are combined to predict human ether-a-go-go related gene inhibition of our large compound data set with consistent experimental measurements (i.e. only patch clamp measurements on mammalian cell lines). Our combined support vector machine model achieves a prediction accuracy of 85% on this data set and performs better than alternative methods used for comparison. We also find that structural keys selected on the basis of statistical criteria are associated with molecular substructures implicated in human ether-a-go-go related gene channel binding.

Ligand structural aspects of hERG channel blockade.

Sudden death as a side effect of action of non-antiarrhythmic drugs is a major pharmacological safety concern facing the pharmaceutical industry and the health regulatory authorities. A number of drugs have been withdrawn from the market in recent years due to cardiovascular toxicity associated with undesirable blockade of hERG potassium channel. Pharmaceuticals of widely varying structure have been shown to interact with hERG. Defining the molecular features that confer hERG inhibitory activity has therefore become a focus of considerable computational and statistical modeling efforts. Some of the approaches are aimed primarily at filtering out potential hERG blockers in the context of virtual libraries, while others involve understanding structure-activity relationships governing hERG-drug interactions. The ability of models to produce structural hypotheses that can be tested by the project teams has become the key prerequisite driving their organization-wide adoption.

Classification models for HERG inhibitors by counter-propagation neural networks.

Counter-propagation neural networks were used to develop computational models for classification and prediction of human ether-a-go-go-related-gene (hERG) potassium channel blockers. The data set used includes 285 compounds taken from literature sources and two sets of 2D molecular descriptors, one is based on 32 P_VSA descriptors derived from moe and the other comprises 11 descriptors retrieved by a feature selection method. The counter-propagation neural networks with a 3-dimensional output layer combined with a set of 11 hERG relevant descriptors showed best performance, especially in classifying compounds in the middle-activity class (hERG IC(50) = 1-10 microm). The total accuracy values obtained for training and test sets are 0.93-0.95 and 0.83-0.85, respectively. In each activity class (low, medium, high), 'Goodness of Hit lists' GH scores archived range from 0.89 to 0.97 for the training set and from 0.74 to 0.87 for the test set. This model thus provides possible strategies for improving the performance of predicting and classifying compounds having hERG IC(50) in the range of 1-10 microm.