Assessing hERG pore models as templates for drug docking using published experimental constraints: the inactivated state in the context of drug block

How could simulations elucidate Nav1.5 channel blockers mechanism?

Tao and Corry used metadynamics, an enhanced sampling method to identify and classify Nav channel blockers.

Stereoselective block of the hERG potassium channel by the Class Ia antiarrhythmic drug disopyramide

Potassium channels encoded by human Ether-à-go-go-Related Gene (hERG) are inhibited by diverse cardiac and non-cardiac drugs. Disopyramide is a chiral Class Ia antiarrhythmic that inhibits hERG at clinical concentrations. This study evaluated effects of disopyramide enantiomers on hERG current (IhERG) from hERG expressing HEK 293 cells at 37 °C. S(+) and R(-) disopyramide inhibited wild-type (WT) IhERG with IC50 values of 3.9 µM and 12.9 µM respectively. The attenuated-inactivation mutant N588K had little effect on the action of S(+) disopyramide but the IC50 for the R(-) enantiomer was ~ 15-fold that for S(+) disopyramide. The enhanced inactivation mutant N588E only slightly increased the potency of R(-) disopyramide. S6 mutation Y652A reduced S(+) disopyramide potency more than that of R(-) disopyramide (respective IC50 values ~ 49-fold and 11-fold their WT controls). The F656A mutation also exerted a stronger effect on S(+) than R(-) disopyramide, albeit with less IC50 elevation. A WT-Y652A tandem dimer exhibited a sensitivity to the enantiomers that was intermediate between that of WT and Y652A, suggesting Y652 groups on adjacent subunits contribute to the binding. Moving the Y (normally at site 652) one residue in the N- terminal (up) direction in N588K hERG markedly increased the blocking potency of R(-) disopyramide. Molecular dynamics simulations using a hERG pore model produced different binding modes for S(+) and R(-) disopyramide consistent with the experimental observations. In conclusion, S(+) disopyramide interacts more strongly with S6 aromatic binding residues on hERG than does R(-) disopyramide, whilst optimal binding of the latter is more reliant on intact inactivation.

Inhibition of the hERG Potassium Channel by a Methanesulphonate-Free E-4031 Analogue

hERG (human Ether-à-go-go Related Gene)-encoded potassium channels underlie the cardiac rapid delayed rectifier (IKr) potassium current, which is a major target for antiarrhythmic agents and diverse non-cardiac drugs linked to the drug-induced form of long QT syndrome. E-4031 is a high potency hERG channel inhibitor from the methanesulphonanilide drug family. This study utilized a methanesulphonate-lacking E-4031 analogue, "E-4031-17", to evaluate the role of the methanesulphonamide group in E-4031 inhibition of hERG. Whole-cell patch-clamp measurements of the hERG current (IhERG) were made at physiological temperature from HEK 293 cells expressing wild-type (WT) and mutant hERG constructs. For E-4031, WT IhERG was inhibited by a half-maximal inhibitory concentration (IC50) of 15.8 nM, whilst the comparable value for E-4031-17 was 40.3 nM. Both compounds exhibited voltage- and time-dependent inhibition, but they differed in their response to successive applications of a long (10 s) depolarisation protocol, consistent with greater dissociation of E-4031-17 than the parent compound between applied commands. Voltage-dependent inactivation was left-ward voltage shifted for E-4031 but not for E-4031-17; however, inhibition by both compounds was strongly reduced by attenuated-inactivation mutations. Mutations of S6 and S5 aromatic residues (F656V, Y652A, F557L) greatly attenuated actions of both drugs. The S624A mutation also reduced IhERG inhibition by both molecules. Overall, these results demonstrate that the lack of a methanesulphonate in E-4031-17 is not an impediment to high potency inhibition of IhERG.

Calculations of the binding free energies of the Comprehensive in vitro Proarrhythmia Assay (CiPA) reference drugs to cardiac ion channels

The evaluation of the inhibitory activities of drugs on multiple cardiac ion channels is required for the accurate assessment of proarrhythmic risks. Moreover, the in silico prediction of such inhibitory activities of drugs on cardiac channels can improve the efficiency of the drug-development process. Here, we performed molecular docking simulations to predict the complex structures of 25 reference drugs that were proposed by the Comprehensive in vitro Proarrhythmia Assay consortium using two cardiac ion channels, the human ether-a-go-go-related gene (hERG) potassium channel and human NaV1.5 (hNaV1.5) sodium channel, with experimentally available structures. The absolute binding free energy (ΔGbind) values of the predicted structures were calculated by a molecular dynamics-based method and compared with the experimental half-maximal inhibitory concentration (IC50) data. Furthermore, the regression analysis between the calculated values and negative of the common logarithm of the experimental IC50 values (pIC50) revealed that the calculated values of four and ten drugs deviated significantly from the regression lines of the hERG and hNaV1.5 channels, respectively. We reconsidered the docking poses and protonation states of the drugs based on the experimental data and recalculated their ΔGbind values. Finally, the calculated ΔGbind values of 24 and 19 drugs correlated with their experimental pIC50 values (coefficients of determination=0.791 and 0.613 for the hERG and hNaV1.5 channels, respectively). Thus, the regression analysis between the calculated ΔGbind and experimental IC50 data ensured the realization of an increased number of reliable complex structures.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models

The human ether-a-go-go (hERG) potassium channel (Kv11.1) plays a critical role in mediating cardiac action potential. The blockade of this ion channel can potentially lead fatal disorder and/or long QT syndrome. Many drugs have been withdrawn because of their serious hERG-cardiotoxicity. It is crucial to assess the hERG blockade activity in the early stage of drug discovery. We are particularly interested in the hERG-cardiotoxicity of compounds collected in the DrugBank database considering that many DrugBank compounds have been approved for therapeutic treatments or have high potential to become drugs. Machine learning-based in silico tools offer a rapid and economical platform to virtually screen DrugBank compounds. We design accurate and robust classifiers for blockers/non-blockers and then build regressors to quantitatively analyze the binding potency of the DrugBank compounds on the hERG channel. Molecular sequences are embedded with two natural language processing (NLP) methods, namely, autoencoder and transformer. Complementary three-dimensional (3D) molecular structures are embedded with two advanced mathematical approaches, i.e., topological Laplacians and algebraic graphs. With our state-of-the-art tools, we reveal that 227 out of the 8641 DrugBank compounds are potential hERG blockers, suggesting serious drug safety problems. Our predictions provide guidance for the further experimental interrogation of DrugBank compounds' hERG-cardiotoxicity.

Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach

Background: Antiarrhythmic drugs are the first-line treatment for atrial fibrillation (AF), but their effect is highly dependent on the characteristics of the patient. Moreover, anatomical variability, and specifically atrial size, have also a strong influence on AF recurrence. Objective: We performed a proof-of-concept study using artificial intelligence (AI) that enabled us to identify proarrhythmic profiles based on pattern identification from in silico simulations. Methods: A population of models consisting of 127 electrophysiological profiles with a variation of nine electrophysiological variables (G _Na , I _NaK , G _K1, G _CaL , G _Kur , I _KCa , [Na] _ext , and [K] _ext and diffusion) was simulated using the Koivumaki atrial model on square planes corresponding to a normal (16 cm²) and dilated (22.5 cm²) atrium. The simple pore channel equation was used for drug implementation including three drugs (isoproterenol, flecainide, and verapamil). We analyzed the effect of every ionic channel combination to evaluate arrhythmia induction. A Random Forest algorithm was trained using the population of models and AF inducibility as input and output, respectively. The algorithm was trained with 80% of the data (N = 832) and 20% of the data was used for testing with a k-fold cross-validation (k = 5). Results: We found two electrophysiological patterns derived from the AI algorithm that was associated with proarrhythmic behavior in most of the profiles, where G _K1 was identified as the most important current for classifying the proarrhythmicity of a given profile. Additionally, we found different effects of the drugs depending on the electrophysiological profile and a higher tendency of the dilated tissue to fibrillate (Small tissue: 80 profiles vs Dilated tissue: 87 profiles). Conclusion: Artificial intelligence algorithms appear as a novel tool for electrophysiological pattern identification and analysis of the effect of antiarrhythmic drugs on a heterogeneous population of patients with AF.

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 (IhERG) were made from HEK293 cells expressing wild-type (WT) and mutant hERG channels. WT IhERG1a was inhibited by phenanthrene with an IC50 of 17.6 ± 1.7 µM, whilst IhERG1a/1b exhibited an IC50 of 1.8 ± 0.3 µM. WT IhERG 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 IhERG block. In contrast, the F557L (S5) and M651A (S6) mutations impaired the ability of phenanthrene to inhibit IhERG, 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.

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.

Molecular dynamics of hERG channel: insights into understanding the binding of small molecules for detuning cardiotoxicity

Evaluation of cardiotoxicity potential of new chemical entities (NCEs) has lately become one of the stringent filters in the drug discovery and development process. Cardiotoxicity is caused mainly by the inhibition of human ether-a-go-go related gene (hERG) channel protein. Inhibition of the hERG channel leads to a life-threatening condition known as cardiac arrhythmia. Knowledge of the structural behaviour of the hERG would aid greatly in the design of new drug molecules that do not interact with the protein and add to the safety index. In this study, a computational model for the active-state of hERG was developed. This model was equilibrated by performing the molecular dynamics simulations for 100 ns followed by clustering and selection of a representative structure based on the largest populated cluster. To study the changes in the protein structure on inhibition, three inhibitory ligands, namely, dofetilide, cisapride and terfenadine were docked, followed by molecular dynamics simulations of 200 ns for the apo and each ligand-bound structure. It was observed that docking and simulation studies of the hERG model exhibited noticeable conformational changes in the protein upon ligand-binding. A significant change in the kink of the S6-transmembrane helix was observed. Inter-chain distances between the crucial residues Y652 and F656 (present below the ion-selectivity filter), their side-chain orientation and hydrogen bonding indicated a probable collapse of the pore. These changes may infer the initiation in transition of hERG from an open to an inactive state. Hence, these findings would help in designing compounds devoid of hERG inhibition with reduced cardiotoxicity.Communicated by Ramaswamy H. Sarma.

Cryo-EM Structure of K+-Bound hERG Channel Complexed with the Blocker Astemizole

The hERG channel is a voltage-gated potassium channel involved in cardiac repolarization. Off-target hERG inhibition by drugs has become a critical issue in the pharmaceutical industry. The three-dimensional structure of the hERG channel was recently reported at 3.8-Å resolution using cryogenic electron microscopy (cryo-EM). However, the drug inhibition mechanism remains unclear because of the scarce structural information regarding the drug- and potassium-bound hERG channels. In this study, we obtained the cryo-EM density map of potassium-bound hERG channel complexed with astemizole, a well-known hERG inhibitor that increases risk of potentially fatal arrhythmia, at 3.5-Å resolution. The structure suggested that astemizole inhibits potassium conduction by binding directly below the selectivity filter. Furthermore, we propose a possible binding model of astemizole to the hERG channel and provide insights into the unusual sensitivity of hERG to several drugs.

Discovery of novel modulators targeting human TRPC5: Docking-based virtual screening, molecular dynamics simulation and binding affinity predication

Canonical transient receptor potential channel 5 (TRPC5) plays a key role in the regulation of central nervous system, cardiovascular system, kidney disease, cancer, and could be also involved in liver function, arthritis, diabetes-associated complications and so on. However, evidence of TRPC5 function on cellular or organismic levels is sparse. There is still a need for identifying novel and efficient TRPC5 channel modulators to study TRPC5 function. In this study, based on the hTRPC5 structure obtained by homology modeling and the predicted binding site, we have performed virtual screening of 212,736 compounds from the specs database（http://www.specs.net) to find potential hTRPC5 modulators. Lipinski and Veber rules, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) and PAINS (Pan Assay Interference structures) filters were used to screen the large database. Further, multi-software combination docking, cluster analysis and interaction analysis were used to select 20 potential active candidates with novel skeleton. 4 Hits, bearing appreciable binding affinity with hTRPC5 were selected for 40ns all-atom molecular dynamics (MD) simulations under explicit water conditions. The MD simulation results suggested that the 4 Hits binding induces a slight structural change and stabilizes the hTRPC5 structure. In addition, decomposition free energy demonstrated that residues TRP434, LEU437, MET438, ALA441, ILE484, ILE487, LEU488, LEU491, LEU515, ILE517, LEU518, LEU521, PHE531, THR607, VAL610, ILE611, VAL615 played the critical role on system stability. 4 Hits, as potential modulators of hTRPC5, may be potential leads to develop effective therapeutics hTRPC5-associated diseases.

A structure-based computational workflow to predict liability and binding modes of small molecules to hERG

Off-target interactions of drugs with the human ether-à-go-go related gene 1 (hERG1) channel have been associated with severe cardiotoxic conditions leading to the withdrawal of many drugs from the market over the last decades. Consequently, predicting drug-induced hERG-liability is now a prerequisite in any drug discovery campaign. Understanding the atomic level interactions of drug with the channel is essential to guide the efficient development of safe drugs. Here we utilize the recent cryo-EM structure of the hERG channel and describe an integrated computational workflow to characterize different drug-hERG interactions. The workflow employs various structure-based approaches and provides qualitative and quantitative insights into drug binding to hERG. Our protocol accurately differentiated the strong blockers from weak and revealed three potential anchoring sites in hERG. Drugs engaging in all these sites tend to have high affinity towards hERG. Our results were cross-validated using a fluorescence polarization kit binding assay and with electrophysiology measurements on the wild-type (WT-hERG) and on the two hERG mutants (Y652A-hERG and F656A-hERG), using the patch clamp technique on HEK293 cells. Finally, our analyses show that drugs binding to hERG disrupt and hijack certain native—structural networks in the channel, thereby, gaining more affinity towards hERG.

Anticancer potential of new 3-nitroaryl-6-(N-methyl)piperazin-1,2,4-triazolo[3,4-a]phthalazines targeting voltage-gated K+ channel: Copper-catalyzed one-pot synthesis from 4-chloro-1-phthalazinyl-arylhydrazones

A series of six 3-aryl-6-(N-methylpiperazin)-1,2,4-triazolo[3,4-a]phthalazines were prepared through a facile and efficient one-pot copper-catalyzed procedure from 4-chloro-1-phthalazinyl-arylhydrazones with relatively good yields (62-83%). The one-pot copper-catalytic procedure consists of two simultaneous reactions: (i) a direct intramolecular dehydrogentaive cyclization between ylidenic carbon and adjacent pyrazine nitrogen to form 1,2,4-triazolo ring and, (ii) a direct N-amination on carbon-chlorine bond. Then, an in vitro anticancer evaluation was performed for the synthesized compounds against five selected human cancer cells (A549, MCF-7, SKBr3, PC-3 and HeLa). The nitro-derivatives were significantly more active against cancer strains than against the rest of tested compounds. Specifically, compound 8d was identified as the most promising anticancer agent with significant biological responses and low relative toxicities on human dermis fibroblast. The cytotoxic effect of compound 8d was more significant on PC3, MCF-7 and SKBr3 cancer cells with low-micromolar IC₅₀ value ranging from 0.11 to 0.59 μM, superior to Adriamycin drug. Mechanistic experimental and theoretical studies demonstrated that compounds 8d act as a K⁺ channel inhibitor in cancer models. Further molecular docking studies suggest that the EGFR Tyrosine Kinase enzyme may be a potential target for the most active 3-aryl-6-(N-methylpiperazin)-1,2,4-triazolo[3,4-a]phthalazines.

An Update on the Structure of hERG

The human voltage-sensitive K⁺ channel hERG plays a fundamental role in cardiac action potential repolarization, effectively controlling the QT interval of the electrocardiogram. Inherited loss- or gain-of-function mutations in hERG can result in dangerous “long” (LQTS) or “short” QT syndromes (SQTS), respectively, and the anomalous susceptibility of hERG to block by a diverse range of drugs underlies an acquired LQTS. A recent open channel cryo-EM structure of hERG should greatly advance understanding of the molecular basis of hERG channelopathies and drug-induced LQTS. Here we describe an update of recent research that addresses the nature of the particular gated state of hERG captured in the new structure, and the insight afforded by the structure into the molecular basis for high affinity drug block of hERG, the binding of hERG activators and the molecular basis of hERG's peculiar gating properties. Interpretation of the pharmacology of natural SQTS mutants in the context of the structure is a promising approach to understanding the molecular basis of hERG inactivation, and the structure suggests how voltage-dependent changes in the membrane domain may be transmitted to an extracellular “turret” to effect inactivation through aromatic side chain motifs that are conserved throughout the KCNH family of channels.

Revealing Molecular Determinants of hERG Blocker and Activator Binding

The Kv11.1 potassium channel, encoded by the human ether-a-go-go-related gene (hERG), plays an essential role in the cardiac action potential. hERG blockade by small molecules can induce "torsade de pointes" arrhythmias and sudden death; as such, it is an important off-target to avoid during drug discovery. Recently, a cryo-EM structure of the open channel state of hERG was reported, opening the door to in silico docking analyses and interpretation of hERG structure-activity relationships, with a view to avoiding blocking activity. Despite this, docking directly to this cryo-EM structure has been reported to yield binding modes that are unable to explain known mutagenesis data. In this work, we use molecular dynamics simulations to sample a range of channel conformations and run ensemble docking campaigns at the known hERG binding site below the selectivity filter, composed of the central cavity and the four deep hydrophobic pockets. We identify a hERG conformational state allowing discrimination of blockers vs nonblockers from docking; furthermore, the binding pocket agrees with mutagenesis data, and blocker binding modes fit the hERG blocker pharmacophore. We then use the same protocol to identify a binding pocket in the hERG channel pore for hERG activators, again agreeing with the reported mutagenesis. Our approach may be useful in drug discovery campaigns to prioritize candidate compounds based on hERG liability via virtual docking screens.

Calculation of absolute binding free energies between the hERG channel and structurally diverse drugs

The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel that plays an essential role in the repolarization of action potentials in cardiac muscle. However, various drugs can block the ion current by binding to the hERG channel, resulting in potentially lethal cardiac arrhythmia. Accordingly, in silico studies are necessary to clarify the mechanisms of how these drugs bind to the hERG channel. Here, we used the experimental structure of the hERG channel, determined by cryo-electron microscopy, to perform docking simulations to predict the complex structures that occur between the hERG channel and structurally diverse drugs. The absolute binding free energies for the models were calculated using the MP-CAFEE method; calculated values were well correlated with experimental ones. By applying the regression equation obtained here, the affinity of a drug for the hERG channel can be accurately predicted from the calculated value of the absolute binding free energy.

The molecular determinants of R-roscovitine block of hERG channels

Human ether-à-go-go-related gene (Kv11.1, or hERG) is a potassium channel that conducts the delayed rectifier potassium current (I_Kr) during the repolarization phase of cardiac action potentials. hERG channels have a larger pore than other K⁺channels and can trap many unintended drugs, often resulting in acquired LQTS (aLQTS). R-roscovitine is a cyclin-dependent kinase (CDK) inhibitor that induces apoptosis in colorectal, breast, prostate, multiple myeloma, other cancer cell lines, and tumor xenografts, in micromolar concentrations. It is well tolerated in phase II clinical trials. R-roscovitine inhibits open hERG channels but does not become trapped in the pore. Two-electrode voltage clamp recordings from Xenopus oocytes expressing wild-type (WT) or hERG pore mutant channels (T623A, S624A, Y652A, F656A) demonstrated that compared to WT hERG, T623A, Y652A, and F656A inhibition by 200 μM R-roscovitine was ~ 48%, 29%, and 73% weaker, respectively. In contrast, S624A hERG was inhibited more potently than WT hERG, with a ~ 34% stronger inhibition. These findings were further supported by the IC₅₀ values, which were increased for T623A, Y652A and F656A (by ~5.5, 2.75, and 42 fold respectively) and reduced 1.3 fold for the S624A mutant. Our data suggest that while T623, Y652, and F656 are critical for R-roscovitine-mediated inhibition, S624 may not be. Docking studies further support our findings. Thus, R-roscovitine’s relatively unique features, coupled with its tolerance in clinical trials, could guide future drug screens.

Potent hERG channel inhibition by sarizotan, an investigative treatment for Rett Syndrome

Rett Syndrome (RTT) is an X-linked neurodevelopmental disorder associated with respiratory abnormalities and, in up to ~40% of patients, with prolongation of the cardiac QT_c interval. QT_c prolongation calls for cautious use of drugs with a propensity to inhibit hERG channels. The STARS trial has been undertaken to investigate the efficacy of sarizotan, a 5-HT_1A receptor agonist, at correcting RTT respiratory abnormalities. The present study investigated whether sarizotan inhibits hERG potassium channels and prolongs ventricular repolarization. Whole-cell patch-clamp measurements were made at 37 °C from hERG-expressing HEK293 cells. Docking analysis was conducted using a recent cryo-EM structure of hERG. Sarizotan was a potent inhibitor of hERG current (I_hERG; IC₅₀ of 183 nM) and of native ventricular I_Kr from guinea-pig ventricular myocytes. 100 nM and 1 μM sarizotan prolonged ventricular action potential (AP) duration (APD₉₀) by 14.1 ± 3.3% (n = 6) and 29.8 ± 3.1% (n = 5) respectively and promoted AP triangulation. High affinity I_hERG inhibition by sarizotan was contingent upon channel gating and intact inactivation. Mutagenesis experiments and docking analysis implicated F557, S624 and Y652 residues in sarizotan binding, with weaker contribution from F656. In conclusion, sarizotan inhibits I_Kr/I_hERG, accessing key binding residues on channel gating. This action and consequent ventricular AP prolongation occur at concentrations relevant to those proposed to treat breathing dysrhythmia in RTT. Sarizotan should only be used in RTT patients with careful evaluation of risk factors for QT_c prolongation.

Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities

The hERG (human ether-a-go-go-related gene) encoded potassium ion (K⁺) channel plays a major role in cardiac repolarization. Drug-induced blockade of hERG has been a major cause of potentially lethal ventricular tachycardia termed Torsades de Pointes (TdPs). Therefore, we presented a pharmacoinformatics strategy using combined ligand and structure based models for the prediction of hERG inhibition potential (IC₅₀) of new chemical entities (NCEs) during early stages of drug design and development. Integrated GRid-INdependent Descriptor (GRIND) models, and lipophilic efficiency (LipE), ligand efficiency (LE) guided template selection for the structure based pharmacophore models have been used for virtual screening and subsequent hERG activity (pIC₅₀) prediction of identified hits. Finally selected two hits were experimentally evaluated for hERG inhibition potential (pIC₅₀) using whole cell patch clamp assay. Overall, our results demonstrate a difference of less than ±1.6 log unit between experimentally determined and predicted hERG inhibition potential (IC₅₀) of the selected hits. This revealed predictive ability and robustness of our models and could help in correctly rank the potency order (lower μM to higher nM range) against hERG.

The current limits in virtual screening and property prediction

Beyond finding inhibitors that show high binding affinity to the respective target, there is the challenge of optimizing their properties with respect to metabolic and toxicological issues, as well as further off-target effects. To reduce the experimental effort of synthesizing and testing actual substances in corresponding assays, virtual screening has become an indispensable toolbox in preclinical development. The scope of application covers the prediction of molecular properties including solubility, metabolic liability and binding to antitargets, such as the hERG channel. Furthermore, prediction of binding sites and drugable targets are emerging aspects of virtual screening. Issues involved with the currently applied computational models including machine learning algorithms are outlined, such as limitations to the accuracy of prediction and overfitting.

Structural implications of hERG K+ channel block by a high-affinity minimally structured blocker

Cardiac potassium channels encoded by human ether-à-go-go–related gene (hERG) are major targets for structurally diverse drugs associated with acquired long QT syndrome. This study characterized hERG channel inhibition by a minimally structured high-affinity hERG inhibitor, Cavalli-2, composed of three phenyl groups linked by polymethylene spacers around a central amino group, chosen to probe the spatial arrangement of side chain groups in the high-affinity drug-binding site of the hERG pore. hERG current (I_hERG) recorded at physiological temperature from HEK293 cells was inhibited with an IC₅₀ of 35.6 nm with time and voltage dependence characteristic of blockade contingent upon channel gating. Potency of Cavalli-2 action was markedly reduced for attenuated inactivation mutants located near (S620T; 54-fold) and remote from (N588K; 15-fold) the channel pore. The S6 Y652A and F656A mutations decreased inhibitory potency 17- and 75-fold, respectively, whereas T623A and S624A at the base of the selectivity filter also decreased potency (16- and 7-fold, respectively). The S5 helix F557L mutation decreased potency 10-fold, and both F557L and Y652A mutations eliminated voltage dependence of inhibition. Computational docking using the recent cryo-EM structure of an open channel hERG construct could only partially recapitulate experimental data, and the high dependence of Cavalli-2 block on Phe-656 is not readily explainable in that structure. A small clockwise rotation of the inner (S6) helix of the hERG pore from its configuration in the cryo-EM structure may be required to optimize Phe-656 side chain orientations compatible with high-affinity block.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC₅₀/Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca²⁺-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca²⁺/late Na⁺ currents and Na⁺/Ca²⁺-exchanger, reduced Na⁺/K⁺-pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na⁺ and Ca²⁺ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca²⁺-transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

The effects of deoxyelephantopin on the cardiac delayed rectifier potassium channel current (IKr) and human ether-a-go-go-related gene (hERG) expression

Elephantopus scaber Linn and its major bioactive component, deoxyelephantopin are known for their medicinal properties and are often reported to have various cytotoxic and antitumor activities. This plant is widely used as folk medicine for a plethora of indications although its safety profile remains unknown. Human ether-a-go-go-related gene (hERG) encodes the cardiac I_Kr current which is a determinant of the duration of ventricular action potentials and QT interval. The hERG potassium channel is an important antitarget in cardiotoxicity evaluation. This study investigated the effects of deoxyelephantopin on the current, mRNA and protein expression of hERG channel in hERG-transfected HEK293 cells. The hERG tail currents following depolarization pulses were insignificantly affected by deoxyelephantopin in the transfected cell line. Current reduction was less than 40% as compared with baseline at the highest concentration of 50 μM. The results were consistent with the molecular docking simulation and hERG surface protein expression. Interestingly, it does not affect the hERG expression at both transcriptional and translational level at most concentrations, although higher concentration at 10 μM caused protein accumulation. In conclusion, deoxyelephantopin is unlikely a clinically significant hERG channel and I_kr blocker.

A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins

Membrane proteins constitute a large portion of the human proteome and perform a variety of important functions as membrane receptors, transport proteins, enzymes, signaling proteins, and more. Computational studies of membrane proteins are usually much more complicated than those of globular proteins. Here, we propose a new continuum model for Poisson-Boltzmann calculations of membrane channel proteins. Major improvements over the existing continuum slab model are as follows: (1) The location and thickness of the slab model are fine-tuned based on explicit-solvent MD simulations. (2) The highly different accessibilities in the membrane and water regions are addressed with a two-step, two-probe grid-labeling procedure. (3) The water pores/channels are automatically identified. The new continuum membrane model is optimized (by adjusting the membrane probe, as well as the slab thickness and center) to best reproduce the distributions of buried water molecules in the membrane region as sampled in explicit water simulations. Our optimization also shows that the widely adopted water probe of 1.4 Å for globular proteins is a very reasonable default value for membrane protein simulations. It gives the best compromise in reproducing the explicit water distributions in membrane channel proteins, at least in the water accessible pore/channel regions. Finally, we validate the new membrane model by carrying out binding affinity calculations for a potassium channel, and we observe good agreement with the experimental results.

Insights into channel dysfunction from modelling and molecular dynamics simulations

Developments in structural biology mean that the number of different ion channel structures has increased significantly in recent years. Structures of ion channels enable us to rationalize how mutations may lead to channelopathies. However, determining the structures of ion channels is still not trivial, especially as they necessarily exist in many distinct functional states. Therefore, the use of computational modelling can provide complementary information that can refine working hypotheses of both wild type and mutant ion channels. The simplest but still powerful tool is homology modelling. Many structures are available now that can provide suitable templates for many different types of ion channels, allowing a full three-dimensional interpretation of mutational effects. These structural models, and indeed the structures themselves obtained by X-ray crystallography, and more recently cryo-electron microscopy, can be subjected to molecular dynamics simulations, either as a tool to help explore the conformational dynamics in detail or simply as a means to refine the models further. Here we review how these approaches have been used to improve our understanding of how diseases might be linked to specific mutations in ion channel proteins. This article is part of the Special Issue entitled 'Channelopathies.'

Computational Tool for Fast in silico Evaluation of hERG K+ Channel Affinity

The development of a novel comprehensive approach for the prediction of hERG activity is herein presented. Software Phase has been used to derive a 3D-QSAR model, employing as alignment rule a common pharmacophore built on a subset of 22 highly active compounds (threshold Ki: 50 nM) against hERG K⁺ channel. Five features comprised the pharmacophore: two aromatic rings (R₁ and R₂), one hydrogen-bond acceptor (A), one hydrophobic site (H), and one positive ionizable function (P). The sequential 3D-QSAR model developed with a set of 421 compounds (randomly divided in training and test set) yielded a test set (Q²) = 0.802 and proved to be predictive with respect to an external test set of 309 compounds that were not used to generate the model (rext_ts2 = 0.860). Furthermore, the model was submitted to an in silico validation for assessing the reliability of the approach, by applying a decoys set, evaluating the Güner and Henry score (GH) and the Enrichment Factor (EF), and by using the ROC curve analysis. The outcome demonstrated the high predictive power of the inclusive 3D-QSAR model developed for the hERG K⁺ channel blockers, confirming the fundamental validity of the chosen approach for obtaining a fast proprietary cardiotoxicity predictive tool to be employed for rationally designing compounds with reduced hERG K⁺ channel activity at the early steps of the drug discovery trajectory.

Investigation of PDE5/PDE6 and PDE5/PDE11 selective potent tadalafil-like PDE5 inhibitors using combination of molecular modeling approaches, molecular fingerprint-based virtual screening protocols and structure-based pharmacophore development

The essential biological function of phosphodiesterase (PDE) type enzymes is to regulate the cytoplasmic levels of intracellular second messengers, 3′,5′-cyclic guanosine monophosphate (cGMP) and/or 3′,5′-cyclic adenosine monophosphate (cAMP). PDE targets have 11 isoenzymes. Of these enzymes, PDE5 has attracted a special attention over the years after its recognition as being the target enzyme in treating erectile dysfunction. Due to the amino acid sequence and the secondary structural similarity of PDE6 and PDE11 with the catalytic domain of PDE5, first-generation PDE5 inhibitors (i.e. sildenafil and vardenafil) are also competitive inhibitors of PDE6 and PDE11. Since the major challenge of designing novel PDE5 inhibitors is to decrease their cross-reactivity with PDE6 and PDE11, in this study, we attempt to identify potent tadalafil-like PDE5 inhibitors that have PDE5/PDE6 and PDE5/PDE11 selectivity. For this aim, the similarity-based virtual screening protocol is applied for the “clean drug-like subset of ZINC database” that contains more than 20 million small compounds. Moreover, molecular dynamics (MD) simulations of selected hits complexed with PDE5 and off-targets were performed in order to get insights for structural and dynamical behaviors of the selected molecules as selective PDE5 inhibitors. Since tadalafil blocks hERG1 K channels in concentration dependent manner, the cardiotoxicity prediction of the hit molecules was also tested. Results of this study can be useful for designing of novel, safe and selective PDE5 inhibitors.

3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the "true" external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6ppm×6ppm×1Å in the C-C region, 6ppm×30ppm×1Å in the C-N region, and 30ppm×30ppm×1Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model - a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.

Role of the pH in state-dependent blockade of hERG currents

Mutations that reduce inactivation of the voltage-gated Kv11.1 potassium channel (hERG) reduce binding for a number of blockers. State specific block of the inactivated state of hERG block may increase risks of drug-induced Torsade de pointes. In this study, molecular simulations of dofetilide binding to the previously developed and experimentally validated models of the hERG channel in open and open-inactivated states were combined with voltage-clamp experiments to unravel the mechanism(s) of state-dependent blockade. The computations of the free energy profiles associated with the drug block to its binding pocket in the intra-cavitary site display startling differences in the open and open-inactivated states of the channel. It was also found that drug ionization may play a crucial role in preferential targeting to the open-inactivated state of the pore domain. pH-dependent hERG blockade by dofetilie was studied with patch-clamp recordings. The results show that low pH increases the extent and speed of drug-induced block. Both experimental and computational findings indicate that binding to the open-inactivated state is of key importance to our understanding of the dofetilide’s mode of action.

In silico design of novel hERG-neutral sildenafil-like PDE5 inhibitors

Cyclic nucleotide phosphodiesterase enzymes (PDEs) have functions in regulating the levels of intracellular second messengers, 3', 5'-cyclic adenosine monophosphate (cAMP) and 3', 5'-cyclic guanosine monophosphate (cGMP), via hydrolysis and decomposing mechanisms in cells. They take essential roles in modulating various cellular activities such as memory and smooth muscle functions. PDE type 5 (PDE5) inhibitors enhance the vasodilatory effects of cGMP in the corpus cavernosum and they are used to treat erectile dysfunction. Patch clamp experiments showed that the IC₅₀ values of the human ether-à-go-go-related gene (hERG1) potassium (K) ion channel blocking affinity of PDE5 inhibitors sildenafil, vardenafil, and tadalafil as 33, 12, and 100 μM, respectively. hERG1 channel is responsible for the regulation of the action potential of human ventricular myocyte by contributing the rapid component of delayed rectifier K⁺ current (I_Kr) component of the cardiac action potential. In this work, interaction patterns and binding affinity predictions of selected PDE5 inhibitors against the hERG1 channel are studied. It is attempted to develop PDE5 inhibitor analogs with lower binding affinity to hERG1 ion channel while keeping their pharmacological activity against their principal target PDE5 using in silico methods. Based on detailed analyses of docking poses and predicted interaction energies, novel analogs of PDE5 inhibitors with lower predicted binding affinity to hERG1 channels without loosing their principal target activity were proposed. Moreover, molecular dynamics (MD) simulations and post-processing MD analyses (i.e. Molecular Mechanics/Generalized Born Surface Area calculations) were performed. Detailed analysis of molecular simulations helped us to better understand the PDE5 inhibitor-target binding interactions in the atomic level. Results of this study can be useful for designing of novel and safe PDE5 inhibitors with enhanced activity and other tailored properties.

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

Interactions between amiodarone and the hERG potassium channel pore determined with mutagenesis and in silico docking

The antiarrhythmic drug amiodarone delays cardiac repolarisation through inhibition of hERG-encoded potassium channels responsible for the rapid delayed rectifier potassium current (I_Kr). This study aimed to elucidate molecular determinants of amiodarone binding to the hERG channel. Whole-cell patch-clamp recordings were made at 37 °C of ionic current (I_hERG) carried by wild-type (WT) or mutant hERG channels expressed in HEK293 cells. Alanine mutagenesis and ligand docking were used to investigate the roles of pore cavity amino-acid residues in amiodarone binding. Amiodarone inhibited WT outward I_hERG tails with a half-maximal inhibitory concentration (IC₅₀) of ∼45 nM, whilst inward I_hERG tails in a high K⁺ external solution ([K⁺]_e) of 94 mM were blocked with an IC₅₀ of 117.8 nM. Amiodarone’s inhibitory action was contingent upon channel gating. Alanine-mutagenesis identified multiple residues directly or indirectly involved in amiodarone binding. The IC₅₀ for the S6 aromatic Y652A mutation was increased to ∼20-fold that of WT I_hERG, similar to the pore helical mutant S624A (∼22-fold WT control). The IC₅₀ for F656A mutant I_hERG was ∼17-fold its corresponding WT control. Computational docking using a MthK-based hERG model differentiated residues likely to interact directly with drug and those whose Ala mutation may affect drug block allosterically. The requirements for amiodarone block of aromatic residues F656 and Y652 within the hERG pore cavity are smaller than for other high affinity I_hERG inhibitors, with relative importance to amiodarone binding of the residues investigated being S624A ∼ Y652A > F656A > V659A > G648A > T623A.

New potential binding determinant for hERG channel inhibitors

Human ether-à-go-go related gene (hERG) 1 channels conduct the rapid delayed rectifier K⁺ current (I_Kr) and are essential for the repolarization of the cardiac action potential. hERG1 inhibition by structurally diverse drugs may lead to life threatening arrhythmia. Putative binding determinants of hERG1 channel blockers include T623, S624 and V625 on the pore helix, and residues G648, Y652 and F656, located on segment S6. We and others have previously hypothesized that additional binding determinants may be located on helix S5, which is in close contact with the S6 segments. In order to test this hypothesis, we performed a detailed investigation combining ionic current measurements with two-microelectrode voltage clamp and molecular modeling techniques. We identified a novel aromatic high affinity binding determinant for blockers located in helix S5, F557, which is equally potent as Y652. Modeling supports a direct interaction with the outer pore helix.

Drug trapping in hERG K+ channels: (not) a matter of drug size?

Inhibition of hERG K+ channels by structurally diverse drugs prolongs the ventricular action potential and increases the risk of torsade de pointes arrhythmias and sudden cardiac death. The capture of drugs behind closed channel gates, so-called drug trapping, is suggested to harbor an increased pro-arrhythmic risk. In this study, the trapping mechanisms of a trapped hERG blocker propafenone and a bulky derivative (MW: 647.24 g mol-1) were studied by making use of electrophysiological measurements in combination with molecular dynamics simulations. Our study suggests that the hERG cavity is able to accommodate very bulky compounds without disturbing gate closure.

Molecular basis of hERG potassium channel blockade by the class Ic antiarrhythmic flecainide

The class Ic antiarrhythmic drug flecainide inhibits KCNH2-encoded "hERG" potassium channels at clinically relevant concentrations. The aim of this study was to elucidate the underlying molecular basis of this action. Patch clamp recordings of hERG current (IhERG) were made from hERG expressing cells at 37°C. Wild-type (WT) IhERG was inhibited with an IC50 of 1.49μM and this was not significantly altered by reversing the direction of K(+) flux or raising external [K(+)]. The use of charged and uncharged flecainide analogues showed that the charged form of the drug accesses the channel from the cell interior to produce block. Promotion of WT IhERG inactivation slowed recovery from inhibition, whilst the N588K and S631A attenuated-inactivation mutants exhibited IC50 values 4-5 fold that of WT IhERG. The use of pore-helix/selectivity filter (T623A, S624A V625A) and S6 helix (G648A, Y652A, F656A) mutations showed <10-fold shifts in IC50 for all but V625A and F656A, which respectively exhibited IC50s 27-fold and 142-fold their WT controls. Docking simulations using a MthK-based homology model suggested an allosteric effect of V625A, since in low energy conformations flecainide lay too low in the pore to interact directly with that residue. On the other hand, the molecule could readily form π-π stacking interactions with aromatic residues and particularly with F656. We conclude that flecainide accesses the hERG channel from the cell interior on channel gating, binding low in the inner cavity, with the S6 F656 residue acting as a principal binding determinant.

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.

Getting to the heart of hERG K(+) channel gating

Potassium ion channels encoded by the human ether-a-go-go related gene (hERG) form the ion-conducting subunit of the rapid delayed rectifier potassium current (IKr ). Although hERG channels exhibit a widespread tissue distribution they play a particularly important role in the heart. There has been considerable interest in hERG K(+) channels for three main reasons. First, they have very unusual gating kinetics, most notably rapid and voltage-dependent inactivation coupled to slow deactivation, which has led to the suggestion that they may play a specific role in the suppression of arrhythmias. Second, mutations in hERG are the cause of 30-40% of cases of congenital long QT syndrome (LQTS), the commonest inherited primary arrhythmia syndrome. Third, hERG is the molecular target for the vast majority of drugs that cause drug-induced LQTS, the commonest cause of drug-induced arrhythmias and cardiac death. Drug-induced LQTS has now been reported for a large range of both cardiac and non-cardiac drugs, in which this side effect is entirely undesired. In recent years there have been comprehensive reviews published on hERG K(+) channels (Vandenberg et al. 2012) and we will not re-cover this ground. Rather, we focus on more recent work on the structural basis and dynamics of hERG gating with an emphasis on how the latest developments may facilitate translational research in the area of stratifying risk of arrhythmias.

hERG potassium channel blockade by the HCN channel inhibitor bradycardic agent ivabradine

Background

Ivabradine is a specific bradycardic agent used in coronary artery disease and heart failure, lowering heart rate through inhibition of sinoatrial nodal HCN‐channels. This study investigated the propensity of ivabradine to interact with KCNH2‐encoded human Ether‐à‐go‐go–Related Gene (hERG) potassium channels, which strongly influence ventricular repolarization and susceptibility to torsades de pointes arrhythmia.

Methods and Results

Patch clamp recordings of hERG current (I_hERG) were made from hERG expressing cells at 37°C. I_hERG was inhibited with an IC₅₀ of 2.07 μmol/L for the hERG 1a isoform and 3.31 μmol/L for coexpressed hERG 1a/1b. The voltage and time‐dependent characteristics of I_hERG block were consistent with preferential gated‐state‐dependent channel block. Inhibition was partially attenuated by the N588K inactivation‐mutant and the S624A pore‐helix mutant and was strongly reduced by the Y652A and F656A S6 helix mutants. In docking simulations to a MthK‐based homology model of hERG, the 2 aromatic rings of the drug could form multiple π‐π interactions with the aromatic side chains of both Y652 and F656. In monophasic action potential (MAP) recordings from guinea‐pig Langendorff‐perfused hearts, ivabradine delayed ventricular repolarization and produced a steepening of the MAPD₉₀ restitution curve.

Conclusions

Ivabradine prolongs ventricular repolarization and alters electrical restitution properties at concentrations relevant to the upper therapeutic range. In absolute terms ivabradine does not discriminate between hERG and HCN channels: it inhibits I_hERG with similar potency to that reported for native I_f and HCN channels, with S6 binding determinants resembling those observed for HCN4. These findings may have important implications both clinically and for future bradycardic drug design.

In silico prediction of hERG inhibition

The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure–activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).

Ranolazine inhibition of hERG potassium channels: drug-pore interactions and reduced potency against inactivation mutants

The antianginal drug ranolazine, which combines inhibitory actions on rapid and sustained sodium currents with inhibition of the hERG/I_Kr potassium channel, shows promise as an antiarrhythmic agent. This study investigated the structural basis of hERG block by ranolazine, with lidocaine used as a low potency, structurally similar comparator. Recordings of hERG current (I_hERG) were made from cell lines expressing wild-type (WT) or mutant hERG channels. Docking simulations were performed using homology models built on MthK and KvAP templates. In conventional voltage clamp, ranolazine inhibited I_hERG with an IC₅₀ of 8.03 μM; peak I_hERG during ventricular action potential clamp was inhibited ~ 62% at 10 μM. The IC₅₀ values for ranolazine inhibition of the S620T inactivation deficient and N588K attenuated inactivation mutants were respectively ~ 73-fold and ~ 15-fold that for WT I_hERG. Mutations near the bottom of the selectivity filter (V625A, S624A, T623A) exhibited IC₅₀s between ~ 8 and 19-fold that for WT I_hERG, whilst the Y652A and F656A S6 mutations had IC₅₀s ~ 22-fold and 53-fold WT controls. Low potency lidocaine was comparatively insensitive to both pore helix and S6 mutations, but was sensitive to direction of K⁺ flux and particularly to loss of inactivation, with an IC₅₀ for S620T-hERG ~ 49-fold that for WT I_hERG. Docking simulations indicated that the larger size of ranolazine gives it potential for a greater range of interactions with hERG pore side chains compared to lidocaine, in particular enabling interaction of its two aromatic groups with side chains of both Y652 and F656. The N588K mutation is responsible for the SQT1 variant of short QT syndrome and our data suggest that ranolazine is unlikely to be effective against I_Kr/hERG in SQT1 patients.

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hERG K⁺ channels regulate cardiac action potential repolarization.

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The molecular basis of hERG block by ranolazine and structurally related lidocaine was studied.

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S6 Y652A and F656A mutations affected greatly ranolazine but not lidocaine binding.

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T623 and S624 residues may directly interact with ranolazine but not lidocaine.

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N588K and S620T attenuated inactivation mutants had reduced sensitivity to both drugs.