A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity

Structural analysis of hERG channel blockers and the implications for drug design

The repolarizing current (I_kr) produced by the hERG potassium channel forms a major component of the cardiac action potential and blocking this current by small molecule drugs can lead to life-threatening cardiotoxicity. Understanding the mechanisms of drug-mediated hERG inhibition is essential to develop a second generation of safe drugs, with minimal cardiotoxic effects. Although various computational tools and drug design guidelines have been developed to avoid binding of drugs to the hERG pore domain, there are many other aspects that are still open for investigation. This includes the use computational modelling to study the implications of hERG mutations on hERG structure and trafficking, the interactions of hERG with hERG chaperone proteins and with membrane-soluble molecules, the mechanisms of drugs that inhibit hERG trafficking and drugs that rescue hERG mutations. The plethora of available experimental data regarding all these aspects can guide the construction of much needed robust computational structural models to study these mechanisms for the rational design of safe drugs.

Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives

Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.

Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach

Psychostimulant drugs, such as cocaine, inhibit dopamine reuptake via blockading the dopamine transporter (DAT), which is the primary mechanism underpinning their abuse. Atypical DAT inhibitors are dissimilar to cocaine and can block cocaine- or methamphetamine-induced behaviors, supporting their development as part of a treatment regimen for psychostimulant use disorders. When developing these atypical DAT inhibitors as medications, it is necessary to avoid off-target binding that can produce unwanted side effects or toxicities. In particular, the blockade of a potassium channel, human ether-a-go-go (hERG), can lead to potentially lethal ventricular tachycardia. In this study, we established a counter screening platform for DAT and against hERG binding by combining machine learning-based quantitative structure-activity relationship (QSAR) modeling, experimental validation, and molecular modeling and simulations. Our results show that the available data are adequate to establish robust QSAR models, as validated by chemical synthesis and pharmacological evaluation of a validation set of DAT inhibitors. Furthermore, the QSAR models based on subsets of the data according to experimental approaches used have predictive power as well, which opens the door to target specific functional states of a protein. Complementarily, our molecular modeling and simulations identified the structural elements responsible for a pair of DAT inhibitors having opposite binding affinity trends at DAT and hERG, which can be leveraged for rational optimization of lead atypical DAT inhibitors with desired pharmacological properties.

Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches

Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of the data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success rate of AI-powered drug discovery. Here, we simulated the federated learning process with different property and activity datasets from different sources, among which overlapping molecules with high or low biases exist in the recorded values. Beyond the benefit of gaining more data, we also demonstrated that federated training has a regularization effect superior to centralized training on the pooled datasets with high biases. Moreover, different network architectures for clients and aggregation algorithms for coordinators have been compared on the performance of federated learning, where personalized federated learning shows promising results. Our work demonstrates the applicability of federated learning in predicting drug-related properties and highlights its promising role in addressing the small and biased data dilemma in drug discovery.

Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations

Drug-induced cardiotoxicity is a potentially lethal and yet one of the most common side effects with the drugs in clinical use. Most of the drug-induced cardiotoxicity is associated with an off-target pharmacological blockade of K+ currents carried out by the cardiac Human-Ether-a-go-go-Related (hERG1) potassium channel. There is a compulsory preclinical stage safety assessment for the hERG1 blockade for all classes of drugs, which adds substantially to the cost of drug development. The availability of a high-resolution cryogenic electron microscopy (cryo-EM) structure for the channel in its open/depolarized state solved in 2017 enabled the application of molecular modeling for rapid assessment of drug blockade by molecular docking and simulation techniques. More importantly, if successful, in silico methods may allow a path to lead-compound salvaging by mapping out key block determinants. Here, we report the blind application of the site identification by the ligand competitive saturation (SILCS) protocol to map out druggable/regulatory hotspots in the hERG1 channel available for blockers and activators. The SILCS simulations use small solutes representative of common functional groups to sample the chemical space for the entire protein and its environment using all-atom simulations. The resulting chemical maps, FragMaps, explicitly account for receptor flexibility, protein-fragment interactions, and fragment desolvation penalty allowing for rapid ranking of potential ligands as blockers or nonblockers of hERG1. To illustrate the power of the approach, SILCS was applied to a test set of 55 blockers with diverse chemical scaffolds and pIC50 values measured under uniform conditions. The original SILCS model was based on the all-atom modeling of the hERG1 channel in an explicit lipid bilayer and was further augmented with a Bayesian-optimization/machine-learning (BML) stage employing an independent literature-derived training set of 163 molecules. BML approach was used to determine weighting factors for the FragMaps contributions to the scoring function. pIC50 predictions from the combined SILCS/BML approach to the 55 blockers showed a Pearson correlation (PC) coefficient of >0.535 relative to the experimental data. SILCS/BML model was shown to yield substantially improved performance as compared to commonly used rigid and flexible molecular docking methods for a well-established cohort of hERG1 blockers, where no correlation with experimental data was recorded. SILCS/BML results also suggest that a proper weighting of protonation states of common blockers present at physiological pH is essential for accurate predictions of blocker potency. The precalculated and optimized SILCS FragMaps can now be used for the rapid screening of small molecules for their cardiotoxic potential as well as for exploring alternative binding pockets in the hERG1 channel with applications to the rational design of activators.

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.

Myocardial slices come to age: an intermediate complexity in vitro cardiac model for translational research

Although past decades have witnessed significant reductions in mortality of heart failure together with advances in our understanding of its cellular, molecular, and whole-heart features, a lot of basic cardiac research still fails to translate into clinical practice. In this review we examine myocardial slices, a novel model in the translational arena. Myocardial slices are living ultra-thin sections of heart tissue. Slices maintain the myocardium's native function (contractility, electrophysiology) and structure (multicellularity, extracellular matrix) and can be prepared from animal and human tissue. The discussion begins with the history and current advances in the model, the different interlaboratory methods of preparation and their potential impact on results. We then contextualize slices' advantages and limitations by comparing it with other cardiac models. Recently, sophisticated methods have enabled slices to be cultured chronically in vitro while preserving the functional and structural phenotype. This is more timely now than ever where chronic physiologically relevant in vitro platforms for assessment of therapeutic strategies are urgently needed. We interrogate the technological developments that have permitted this, their limitations, and future directions. Finally, we look into the general obstacles faced by the translational field, and how implementation of research systems utilizing slices could help in resolving these.

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.

Human ether-à-go-go-related potassium channel: exploring SAR to improve drug design

hERG is best known as a primary anti-target, the inhibition of which is responsible for serious side effects. A renewed interest in hERG as a desired target, especially in oncology, was sparked because of its role in cellular proliferation and apoptosis. In this study, we survey the most recent advances regarding hERG by focusing on SAR in the attempt to elucidate, at a molecular level, off-target and on-target actions of potential hERG binders, which are highly promiscuous and largely varying in structure. Understanding the rationale behind hERG interactions and the molecular determinants of hERG activity is a real challenge and comprehension of this is of the utmost importance to prioritize compounds in early stages of drug discovery and to minimize cardiotoxicity attrition in preclinical and clinical studies.

Intracellular metabolism and potential cardiotoxicity of a β-D-2'-C-methyl-2,6-diaminopurine ribonucleoside phosphoramidate that inhibits hepatitis C virus replication

β-D-2'-C-Methyl-2,6-diaminopurine ribonucleoside (2'-C-Me-DAPN) phosphoramidate prodrug (DAPN-PD) is a selective hepatitis C virus inhibitor that is metabolized intracellularly into two active metabolites: 2'-C-Methyl-DAPN triphosphate (2'-C-Me-DAPN-TP) and 2'-C-methyl-guanosine 5'-triphosphate (2'-C-Me-GTP). BMS-986094 and IDX-184 are also bioconverted to 2'-C-Me-GTP. A phase IIb clinical trial with BMS-986094 was abruptly halted due to adverse cardiac and renal effects. Herein, we developed an efficient large scale synthesis of DAPN-PD and determined intracellular pharmacology of DAPN-PD in comparison with BMS-986094 and IDX-184, versus Huh-7, HepG2 and interspecies primary hepatocytes and human cardiomyocytes. Imaging data of drug treated human cardiomyocytes was found to be most useful in determining toxicity potential as no obvious beating rate change was observed for IDX-184 up to 50 µM up at 48 h. However, with BMS-986094 and DAPN-PD at 10 µM changes to both beat rate and rhythm were noted.

Molecular Docking Guided Grid-Independent Descriptor Analysis to Probe the Impact of Water Molecules on Conformational Changes of hERG Inhibitors in Drug Trapping Phenomenon

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (I_Kr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal arrhythmias. Several marketed drugs have been withdrawn for this reason. Therefore, there is considerable interest in developing better tests for predicting drugs which can block the hERG channel. The drug-binding pocket in hERG channels, which lies below the selectivity filter, normally contains K⁺ ions and water molecules. In this study, we test the hypothesis that these water molecules impact drug binding to hERG. We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. The ligand–protein interaction fingerprints (PLIF) scheme was used to summarize and compare the interactions. All models delineated similar 3D hERG binding features, however, small deviations of about ~0.4 Å were observed between important hotspots of molecular interaction fields (MIFs) between solvated and non-solvated hERG models. These small changes in conformations do not affect the performance and predictive power of the model to any significant extent. The model that exhibits the best statistical values was attained with a cryo_EM structure of the hERG channel in open state without water. This model also showed the best R² of 0.58 and 0.51 for the internal and external validation test sets respectively. Our results suggest that the inclusion of water molecules during the docking process has little effect on conformations and this conformational change does not impact the predictive ability of the 3D QSAR models.

Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting

In this paper, we explore the impact of combining different in silico prediction approaches and data sources on the predictive performance of the resulting system. We use inhibition of the hERG ion channel target as the endpoint for this study as it constitutes a key safety concern in drug development and a potential cause of attrition. We will show that combining data sources can improve the relevance of the training set in regard of the target chemical space, leading to improved performance. Similarly we will demonstrate that combining multiple statistical models together, and with expert systems, can lead to positive synergistic effects when taking into account the confidence in the predictions of the merged systems. The best combinations analyzed display a good hERG predictivity. Finally, this work demonstrates the suitability of the SOHN methodology for building models in the context of receptor based endpoints like hERG inhibition when using the appropriate pharmacophoric descriptors.

Cyclosporine A binding to COX-2 reveals a novel signaling pathway that activates the IRE1α unfolded protein response sensor

Cyclosporine, a widely used immunosuppressant in organ transplantation and in treatment of various autoimmune diseases, activates the unfolded protein response (UPR), an ER stress coping response. In this study we discovered a new and unanticipated cyclosporine-dependent signaling pathway, with cyclosporine triggering direct activation of the UPR. COX-2 binds to and activates IRE1α, leading to IRE1α splicing of XBP1 mRNA. Molecular interaction and modeling analyses identified a novel interaction site for cyclosporine with COX-2 which caused enhancement of COX-2 enzymatic activity required for activation of the IRE1α branch of the UPR. Cyclosporine-dependent activation of COX-2 and IRE1α in mice indicated that cyclosporine-COX-2-IRE1α signaling pathway was functional in vivo. These findings identify COX-2 as a new IRE1α binding partner and regulator of the IRE1α branch of the UPR pathway, and establishes the mechanism underlying cytotoxicity associated with chronic cyclosporine exposure.

Effects of protein-protein interactions and ligand binding on the ion permeation in KCNQ1 potassium channel

The voltage-gated KCNQ1 potassium ion channel interacts with the type I transmembrane protein minK (KCNE1) to generate the slow delayed rectifier (I_Ks) current in the heart. Mutations in these transmembrane proteins have been linked with several heart-related issues, including long QT syndromes (LQTS), congenital atrial fibrillation, and short QT syndrome. Off-target interactions of several drugs with that of KCNQ1/KCNE1 ion channel complex have been known to cause fatal cardiac irregularities. Thus, KCNQ1/KCNE1 remains an important avenue for drug-design and discovery research. In this work, we present the structural and mechanistic details of potassium ion permeation through an open KCNQ1 structural model using the combined molecular dynamics and steered molecular dynamics simulations. We discuss the processes and key residues involved in the permeation of a potassium ion through the KCNQ1 ion channel, and how the ion permeation is affected by (i) the KCNQ1-KCNE1 interactions and (ii) the binding of chromanol 293B ligand and its derivatives into the complex. The results reveal that interactions between KCNQ1 with KCNE1 causes a pore constriction in the former, which in-turn forms small energetic barriers in the ion-permeation pathway. These findings correlate with the previous experimental reports that interactions of KCNE1 dramatically slows the activation of KCNQ1. Upon ligand-binding onto the complex, the energy-barriers along ion permeation path are more pronounced, as expected, therefore, requiring higher force in our steered-MD simulations. Nevertheless, pulling the ion when a weak blocker is bound to the channel does not necessitate high force in SMD. This indicates that our SMD simulations have been able to discern between strong and week blockers and reveal their influence on potassium ion permeation. The findings presented here will have some implications in understanding the potential off-target interactions of the drugs with the KCNQ1/KCNE1 channel that lead to cardiotoxic effects.

Computational membrane biophysics: From ion channel interactions with drugs to cellular function

The rapid development of experimental and computational techniques has changed fundamentally our understanding of cellular-membrane transport. The advent of powerful computers and refined force-fields for proteins, ions, and lipids has expanded the applicability of Molecular Dynamics (MD) simulations. A myriad of cellular responses is modulated through the binding of endogenous and exogenous ligands (e.g. neurotransmitters and drugs, respectively) to ion channels. Deciphering the thermodynamics and kinetics of the ligand binding processes to these membrane proteins is at the heart of modern drug development. The ever-increasing computational power has already provided insightful data on the thermodynamics and kinetics of drug-target interactions, free energies of solvation, and partitioning into lipid bilayers for drugs. This review aims to provide a brief summary about modeling approaches to map out crucial binding pathways with intermediate conformations and free-energy surfaces for drug-ion channel binding mechanisms that are responsible for multiple effects on cellular functions. We will discuss post-processing analysis of simulation-generated data, which are then transformed to kinetic models to better understand the molecular underpinning of the experimental observables under the influence of drugs or mutations in ion channels. This review highlights crucial mathematical frameworks and perspectives on bridging different well-established computational techniques to connect the dynamics and timescales from all-atom MD and free energy simulations of ion channels to the physiology of action potentials in cellular models. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

A comprehensive structural model for the human KCNQ1/KCNE1 ion channel

The voltage-gated KCNQ1/KCNE1 potassium ion channel complex, forms the slow delayed rectifier (I_Ks) current in the heart, which plays an important role in heart signaling. The importance of KCNQ1/KCNE1 channel's function is further implicated by the linkage between loss-of-function and gain-of-function mutations in KCNQ1 or KCNE1, and long QT syndromes, congenital atrial fibrillation, and short QT syndrome. Also, KCNQ1/KCNE1 channels are an off-target for many non-cardiovascular drugs, leading to fatal cardiac irregularities. One solution to address and study the mentioned aspects of KCNQ1/KNCE1 channel would be the structural studies using a validated and accurate model. Along the same line in this study, we have used several top-notch modeling approaches to build a structural model for the open state of KCNQ1 protein, which is both accurate and compatible with available experimental data. Next, we included the KCNE1 protein components using data-driven protein-protein docking simulations, encompassing a 4:2 stoichiometry to complete the picture of the channel complex formed by these two proteins. All the protein systems generated through these processes were refined by long Molecular Dynamics simulations. The refined models were analyzed extensively to infer data about the interaction of KCNQ1 channel with its accessory KCNE1 beta subunits.

Modeling the human Nav1.5 sodium channel: structural and mechanistic insights of ion permeation and drug blockade

Abnormalities in the human Nav1.5 (hNav1.5) voltage-gated sodium ion channel (VGSC) are associated with a wide range of cardiac problems and diseases in humans. Current structural models of hNav1.5 are still far from complete and, consequently, their ability to study atomistic interactions of this channel is very limited. Here, we report a comprehensive atomistic model of the hNav1.5 ion channel, constructed using homology modeling technique and refined through long molecular dynamics simulations (680 ns) in the lipid membrane bilayer. Our model was comprehensively validated by using reported mutagenesis data, comparisons with previous models, and binding to a panel of known hNav1.5 blockers. The relatively long classical MD simulation was sufficient to observe a natural sodium permeation event across the channel's selectivity filters to reach the channel's central cavity, together with the identification of a unique role of the lysine residue. Electrostatic potential calculations revealed the existence of two potential binding sites for the sodium ion at the outer selectivity filters. To obtain further mechanistic insight into the permeation event from the central cavity to the intracellular region of the channel, we further employed "state-of-the-art" steered molecular dynamics (SMD) simulations. Our SMD simulations revealed two different pathways through which a sodium ion can be expelled from the channel. Further, the SMD simulations identified the key residues that are likely to control these processes. Finally, we discuss the potential binding modes of a panel of known hNav1.5 blockers to our structural model of hNav1.5. We believe that the data presented here will enhance our understanding of the structure-property relationships of the hNav1.5 ion channel and the underlying molecular mechanisms in sodium ion permeation and drug interactions. The results presented here could be useful for designing safer drugs that do not block the hNav1.5 channel.

A multiscale computational modelling approach predicts mechanisms of female sex risk in the setting of arousal-induced arrhythmias

KEY POINTS

This study represents a first step toward predicting mechanisms of sex-based arrhythmias that may lead to important developments in risk stratification and may inform future drug design and screening. We undertook simulations to reveal the conditions (i.e. pacing, drugs, sympathetic stimulation) required for triggering and sustaining reentrant arrhythmias. Using the recently solved cryo-EM structure for the Eag-family channel as a template, we revealed potential interactions of oestrogen with the pore loop hERG mutation (G604S). Molecular models suggest that oestrogen and dofetilide blockade can concur simultaneously in the hERG channel pore.

ABSTRACT

Female sex is a risk factor for inherited and acquired long-QT associated torsade de pointes (TdP) arrhythmias, and sympathetic discharge is a major factor in triggering TdP in female long-QT syndrome patients. We used a combined experimental and computational approach to predict 'the perfect storm' of hormone concentration, I_Kr block and sympathetic stimulation that induces arrhythmia in females with inherited and acquired long-QT. More specifically, we developed mathematical models of acquired and inherited long-QT syndrome in male and female ventricular human myocytes by combining effects of a hormone and a hERG blocker, dofetilide, or hERG mutations. These 'male' and 'female' model myocytes and tissues then were used to predict how various sex-based differences underlie arrhythmia risk in the setting of acute sympathetic nervous system discharge. The model predicted increased risk for arrhythmia in females when acute sympathetic nervous system discharge was applied in the settings of both inherited and acquired long-QT syndrome. Females were predicted to have protection from arrhythmia induction when progesterone is high. Males were protected by the presence of testosterone. Structural modelling points towards two plausible and distinct mechanisms of oestrogen action enhancing torsadogenic effects: oestradiol interaction with hERG mutations in the pore loop containing G604 or with common TdP-related blockers in the intra-cavity binding site. Our study presents findings that constitute the first evidence linking structure to function mechanisms underlying female dominance of arousal-induced arrhythmias.

First universal pharmacophore model for hERG1 K+ channel activators: acthER

The intra-cavitary drug blockade of hERG1 channel has been extensively studied, both experimentally and theoretically. Structurally diverse ligands inadvertently block the hERG1 K⁺ channel currents lead to drug induced Long QT Syndrome (LQTS). Accordingly, designing either hERG1 channel openers or current activators, with the potential to target other binding pockets of the channel, has been introduced as a viable approach in modern anti-arrhythmia drug development. However, reports and investigations on the molecular mechanisms underlying activators binding to the hERG1 channel remain sparse and the overall molecular design principles are largely unknown. Most of the hERG1 activators were discovered during mandatory screening for hERG1 blockade. To fill this apparent deficit, the first universal pharmacophore model for hERG1 K⁺ channel activators was developed using PHASE. 3D structures of 18 hERG1 K⁺ channel activators and their corresponding measured binding affinity values were used in the development of pharmacophore models. These compounds spanned a range of structurally different chemotypes with moderate variation in binding affinity. A five sites AAHRR (A, hydrogen-bond accepting, H, hydrophobic, R, aromatic) pharmacophore model has shown reasonable high statistical results compared to the other developed more than 1000 hypotheses. This model was used to construct steric and electrostatic contour maps. The predictive power of the model was tested with 3 external test set compounds as true unknowns. Finally, the pharmacophore model was combined with the previously developed receptor-based model of hERG1 K⁺ channel to develop and screen novel activators. The results are quite striking and it suggests a greater future role for pharmacophore modeling and virtual drug screening simulations in deciphering complex patterns of molecular mechanisms of hERG1 channel openers at the target sites. The developed model is available upon request and it may serve as basis for the synthesis of novel therapeutic hERG1 activators.

Applications of computer-aided approaches in the development of hepatitis C antiviral agents

INTRODUCTION

Hepatitis C virus (HCV) is a global health problem that causes several chronic life-threatening liver diseases. The numbers of people affected by HCV are rising annually. Since 2011, the FDA has approved several anti-HCV drugs; while many other promising HCV drugs are currently in late clinical trials. Areas covered: This review discusses the applications of different computational approaches in HCV drug design. Expert opinion: Molecular docking and virtual screening approaches have emerged as a low-cost tool to screen large databases and identify potential small-molecule hits against HCV targets. Ligand-based approaches are useful for filtering-out compounds with rich physicochemical properties to inhibit HCV targets. Molecular dynamics (MD) remains a useful tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in HCV. Despite their varied roles, the application of in-silico approaches in HCV drug design is still in its infancy. A more mature application should aim at modelling the whole HCV replicon in its active form and help to identify new effective druggable sites within the replicon system. With more technological advancements, the roles of computer-aided methods are only going to increase several folds in the development of next-generation HCV drugs.

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.

From the Cover: Investigative Nonclinical Cardiovascular Safety and Toxicology Studies with BMS-986094, an NS5b RNA-Dependent RNA Polymerase Inhibitor

BMS-986094, a 2'-C-methylguanosine prodrug that was in development for treatment of chronic hepatitis C infection was withdrawn from Phase 2 clinical trials because of unexpected cardiac and renal adverse events. Investigative nonclinical studies were conducted to extend the understanding of these findings using more comprehensive endpoints. BMS-986094 was given orally to female CD-1 mice (25 and 150 mg/kg/d) for 2 weeks (53/group) and to cynomolgus monkeys (15 and 30 mg/kg/d) for up to 6 weeks (2-3/sex/group for cardiovascular safety, and 5/sex/group for toxicology). Endpoints included toxicokinetics; echocardiography, telemetric hemodynamics and electrocardiography, and tissue injury biomarkers (monkey); and light and ultrastructural pathology of heart, kidney, and skeletal muscle (mouse/monkey). Dose-related and time-dependent findings included: severe toxicity in mice at 150 mg/kg/d and monkeys at 30 mg/kg/d; decreased left ventricular (LV) ejection fraction, fractional shortening, stroke volume, and dP/dt; LV dilatation, increased QTc interval, and T-wave flattening/inversion (monkeys at ≥ 15 mg/kg/d); cardiomyocyte degeneration (mice at 150 mg/kg/d and monkeys at ≥ 15 mg/kg/d) with myofilament lysis/myofbril disassembly; time-dependent proteinuria and increased urine β-2 microglobulin, calbindin, clusterin; kidney pallor macroscopically; and tubular dilatation (monkeys); tubular regeneration (mice 150 mg/kg/d); and acute proximal tubule degeneration ultrastructurally (mice/monkeys); and skeletal muscle degeneration with increased urine myoglobin and serum sTnI. These studies identified changes not described previously in studies of BMS-986094 including premonitory cardiovascular functional changes as well as additional biomarkers for muscle and renal toxicities. Although the mechanism of potential toxicities observed in BMS-986094 studies was not established, there was no evidence for direct mitochondrial toxicity.

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.

New design of nucleotide excision repair (NER) inhibitors for combination cancer therapy

Many cancer chemotherapy agents act by targeting the DNA of cancer cells, causing substantial damage within their genome and causing them to undergo apoptosis. An effective DNA repair pathway in cancer cells can act in a reverse way by removing these drug-induced DNA lesions, allowing cancer cells to survive, grow and proliferate. In this context, DNA repair inhibitors opened a new avenue in cancer treatment, by blocking the DNA repair mechanisms from removing the chemotherapy-mediated DNA damage. In particular, the nucleotide excision repair (NER) involves more than thirty protein-protein interactions and removes DNA adducts caused by platinum-based chemotherapy. The excision repair cross-complementation group 1 (ERCC1)-xeroderma pigmentosum, complementation group A (XPA) protein (XPA-ERCC1) complex seems to be one of the most promising targets in this pathway. ERCC1 is over expressed in cancer cells and the only known cellular function so far for XPA is to recruit ERCC1 to the damaged point. Here, we build upon our recent advances in identifying inhibitors for this interaction and continue our efforts to rationally design more effective and potent regulators for the NER pathway. We employed in silico drug design techniques to: (1) identify compounds similar to the recently discovered inhibitors, but more effective at inhibiting the XPA-ERCC1 interactions, and (2) identify different scaffolds to develop novel lead compounds. Two known inhibitor structures have been used as starting points for two ligand/structure-hybrid virtual screening approaches. The findings described here form a milestone in discovering novel inhibitors for the NER pathway aiming at improving the efficacy of current platinum-based therapy, by modulating the XPA-ERCC1 interaction.

HIV, highly active antiretroviral therapy and the heart: a cellular to epidemiological review

The advent of potent highly active antiretroviral therapy (HAART) for persons infected with HIV-1 has led to a "new" chronic disease with complications including cardiovascular disease (CVD). CVD is a significant cause of morbidity and mortality in persons with HIV infection. In addition to traditional risk factors such as smoking, hypertension, insulin resistance and dyslipidaemia, infection with HIV is an independent risk factor for CVD. This review summarizes: (1) the vascular and nonvascular cardiac manifestations of HIV infection; (2) cardiometabolic effects of HAART; (3) atherosclerotic cardiovascular disease (ASCVD) risk assessment, prevention and treatment in persons with HIV-1 infection.

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.

Anesthetic drug midazolam inhibits cardiac human ether-à-go-go-related gene channels: mode of action

Midazolam is a short-acting benzodiazepine that is in wide clinical use as an anxiolytic, sedative, hypnotic, and anticonvulsant. Midazolam has been shown to inhibit ion channels, including calcium and potassium channels. So far, the effects of midazolam on cardiac human ether-à-go-go-related gene (hERG) channels have not been analyzed. The inhibitory effects of midazolam on heterologously expressed hERG channels were analyzed in Xenopus oocytes using the double-electrode voltage clamp technique. We found that midazolam inhibits hERG channels in a concentration-dependent manner, yielding an IC₅₀ of 170 μM in Xenopus oocytes. When analyzed in a HEK 293 cell line using the patch-clamp technique, the IC₅₀ was 13.6 μM. Midazolam resulted in a small negative shift of the activation curve of hERG channels. However, steady-state inactivation was not significantly affected. We further show that inhibition is state-dependent, occurring within the open and inactivated but not in the closed state. There was no frequency dependence of block. Using the hERG pore mutants F656A and Y652A we provide evidence that midazolam uses a classical binding site within the channel pore. Analyzing the subacute effects of midazolam on hERG channel trafficking, we further found that midazolam does not affect channel surface expression. Taken together, we show that the anesthetic midazolam is a low-affinity inhibitor of cardiac hERG channels without additional effects on channel surface expression. These data add to the current understanding of the pharmacological profile of the anesthetic midazolam.