Characterization of HERG potassium channel inhibition using CoMSiA 3D QSAR and homology modeling approaches

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.

Multitargeting in cardioprotection: An example of biaromatic compounds

A multitarget drug design approach is actively developing in modern medicinal chemistry and pharmacology, especially with regard to multifactorial diseases such as cardiovascular diseases, cancer, and neurodegenerative diseases. A detailed study of many well-known drugs developed within the single-target approach also often reveals additional mechanisms of their real pharmacological action. One of the multitarget drug design approaches can be the identification of the basic pharmacophore models corresponding to a wide range of the required target ligands. Among such models in the group of cardioprotectors is the linked biaromatic system. This review develops the concept of a "basic pharmacophore" using the biaromatic pharmacophore of cardioprotectors as an example. It presents an analysis of possible biological targets for compounds corresponding to the biaromatic pharmacophore and an analysis of the spectrum of biological targets for the five most known and most studied cardioprotective drugs corresponding to this model, and their involvement in the biological effects of these drugs.

Machine-learning technique, QSAR and molecular dynamics for hERG-drug interactions

One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-dependent hERG K + channel, which is well-known for its crucial involvement in cardiac action potential repolarization. Torsades de Pointes, QT prolongation, and sudden death are all caused by hERG (the human Ether-à-go-go-Related Gene) inhibition. There is great interest in creating predictive computational (in silico) tools to identify and weed out potential hERG blockers early in the drug discovery process because testing for hERG liability and the traditional experimental screening are complicated, expensive and time-consuming. This study used 2D descriptors of a large curated dataset of 6766 compounds and machine learning approaches to build robust descriptor-based QSAR and predictive classification models for KCNH2 liability. Decision Tree, Random Forest, Logistic Regression, Ada Boosting, kNN, SVM, Naïve Bayes, neural network and stochastic gradient classification classifier algorithms were used to build classification models. If a compound's IC50 value was between 10 μM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew's correlation coefficient formula and F1score were applied to compare and track the developed models' performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG-gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG-ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns.Communicated by Ramaswamy H. Sarma.

Structural Alterations of the "Address" Moiety of NAN Leading to the Discovery of a Novel Opioid Receptor Modulator with Reduced hERG Toxicity

The search for selective opioid ligands with desired pharmacological potency and improved safety profile has always been an area of interest. Our previous effort yielded a potent opioid modulator, NAN, a 6α-N-7'-indolyl-substituted naltrexamine derivative, which exhibited promising pharmacological activities both in vitro and in vivo. However, significant human ether-a-go-go-related gene (hERG) liability limited its further development. Therefore, a systematic structural modification on NAN was conducted in order to alleviate hERG toxicity while preserving pharmacological properties, which led to the discovery of 2'-methylindolyl derivative compound 21. Compared to NAN, compound 21 manifested overall improved pharmacological profiles. Follow-up hERG channel inhibition evaluation revealed a seven-fold decreased potency of compound 21 compared to NAN. Furthermore, several fundamental drug-like property evaluations suggested a reasonable ADME profile of 21. Collectively, compound 21 appeared to be a promising opioid modulator for further development as a novel therapeutic agent toward opioid use disorder treatments.

Aromatic or Hetero-aromatic Directly Attached Tri and Tetrasubstituted Methanes: New Chemical Entities as Anti-Infectives

Tri and Tetra-substituted Methanes (TRSMs) are a significant structural motif in many approved drugs and prodrugs. There is increasing use of TRSM units in medicinal chemistry, and many derivatives are specifically designed to make drug-target interactions through new chemical space around TRSM moiety. In this perspective, we describe synthetic challenges for accessing a range of functionalized selective TRSMs and their molecular mechanism of action, especially as anti-infectives. Natural anti-infectives like (+)-Bionectin A, B, (+)-Gliocladine C, Balanocarpol having TRSMs selectively and effectively bind to target proteins in comparison to planar motif having more sp2 carbons perhaps due to conformation which reduces the penalty for conformational entropy with the enhancement of three-dimensionality. Properties of repurposed TRSMs like Almitrine, Ifenprodil, Baricitinib and Remdesivir with their recent progress in COVID-19 therapeutics with their mode of action are also delineated. This perspective is expected to deliver a user guide and reference source for scientists, researchers and academicians in pursuing newly designed TRSMs as therapeutics.

Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design

Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.

Ursolic acid as a potential inhibitor of Mycobacterium tuberculosis cytochrome bc1 oxidase-a molecular modelling perspective

The escalating burden of tuberculosis disease and drastic effects of current medicine has stimulated a search for alternative drugs. A medicinal plant Warburgia salutaris has been reported to possess inhibitory properties against M. tuberculosis. In this study, we apply computational methods to investigate the probability of W. salutaris compounds as potential inhibitors of M. tuberculosis QcrB protein. We performed molecular docking, molecular dynamics simulations, radius of gyration, principal component analysis (PCA), and molecular mechanics-generalized born surface area (MM-GBSA) binding-free energy calculations in explicit solvent to achieve our objective. The results suggested that ursolic acid (UA) and ursolic acid acetate (UAA) could serve as preferred potential inhibitors of mycobacterial QcrB compared to lansoprazole sulphide (LSPZ) and telacebec (Q203)-UA and UAA have a higher binding affinity to QcrB compared to LSPZ and Q203 drugs. UA binding affinity is attributed to hydrogen bond formation with Val120, Arg364 and Arg366, and largely resonated from van der Waals forces resulting from UA interactions with hydrophobic amino acids in its vicinity. UAA binds to the porphyrin ring binding site with higher binding affinity compared to LSPZ. The binding affinity results primarily from van der Waals forces between UAA and hydrophobic residues of QcrB in the porphyrin ring binding site where UAA binds competitively. UA and UAA formed stable complexes with the protein with reduced overall residue mobility, consequently supporting the magnitude of binding affinity of the respective ligands. UAA could potentially compete with the porphyrin ring for the binding site and deprive the mycobacterial cell from oxygen, consequently disturbing mycobacterial oxygen-dependent metabolic processes. Therefore, discovery of a compound that competes with porphyrin ring for the binding site may be useful in QcrB pharmocological studies. UA proved to be a superior compound, although its estimated toxicity profile revealed UA to be hepatotoxic within acceptable parameters. Although preliminary findings of this report still warrant experimental validation, they could serve as a baseline for the development of new anti-tubercular drugs from natural resources that target QcrB.

Molecular Dynamics-Derived Pharmacophore Model Explaining the Nonselective Aspect of KV10.1 Pore Blockers

The K_V10.1 voltage-gated potassium channel is highly expressed in 70% of tumors, and thus represents a promising target for anticancer drug discovery. However, only a few ligands are known to inhibit K_V10.1, and almost all also inhibit the very similar cardiac hERG channel, which can lead to undesirable side-effects. In the absence of the structure of the K_V10.1–inhibitor complex, there remains the need for new strategies to identify selective K_V10.1 inhibitors and to understand the binding modes of the known K_V10.1 inhibitors. To investigate these binding modes in the central cavity of K_V10.1, a unique approach was used that allows derivation and analysis of ligand–protein interactions from molecular dynamics trajectories through pharmacophore modeling. The final molecular dynamics-derived structure-based pharmacophore model for the simulated K_V10.1–ligand complexes describes the necessary pharmacophore features for K_V10.1 inhibition and is highly similar to the previously reported ligand-based hERG pharmacophore model used to explain the nonselectivity of K_V10.1 pore blockers. Moreover, analysis of the molecular dynamics trajectories revealed disruption of the π–π network of aromatic residues F359, Y464, and F468 of K_V10.1, which has been reported to be important for binding of various ligands for both K_V10.1 and hERG channels. These data indicate that targeting the K_V10.1 channel pore is also likely to result in undesired hERG inhibition, and other potential binding sites should be explored to develop true K_V10.1-selective inhibitors as new anticancer agents.

Overcoming challenges of HERG potassium channel liability through rational design: Eag1 inhibitors for cancer treatment

Two decades of research have proven the relevance of ion channel expression for tumor progression in virtually every indication, and it has become clear that inhibition of specific ion channels will eventually become part of the oncology therapeutic arsenal. However, ion channels play relevant roles in all aspects of physiology, and specificity for the tumor tissue remains a challenge to avoid undesired effects. Eag1 (K_V 10.1) is a voltage-gated potassium channel whose expression is very restricted in healthy tissues outside of the brain, while it is overexpressed in 70% of human tumors. Inhibition of Eag1 reduces tumor growth, but the search for potent inhibitors for tumor therapy suffers from the structural similarities with the cardiac HERG channel, a major off-target. Existing inhibitors show low specificity between the two channels, and screenings for Eag1 binders are prone to enrichment in compounds that also bind HERG. Rational drug design requires knowledge of the structure of the target and the understanding of structure-function relationships. Recent studies have shown subtle structural differences between Eag1 and HERG channels with profound functional impact. Thus, although both targets' structure is likely too similar to identify leads that exclusively bind to one of the channels, the structural information combined with the new knowledge of the functional relevance of particular residues or areas suggests the possibility of selective targeting of Eag1 in cancer therapies. Further development of selective Eag1 inhibitors can lead to first-in-class compounds for the treatment of different cancers.

hERG toxicity assessment: Useful guidelines for drug design

All along the drug development process, one of the most frequent adverse side effects, leading to the failure of drugs, is the cardiac arrhythmias. Such failure is mostly related to the capacity of the drug to inhibit the human ether-à-go-go-related gene (hERG) cardiac potassium channel. The early identification of hERG inhibition properties of biological active compounds has focused most of attention over the years. In order to prevent the cardiac side effects, a great number of in silico, in vitro and in vivo assays have been performed. The main goal of these studies is to understand the reasons of these effects, and then to give information or instructions to scientists involved in drug development to avoid the cardiac side effects. To evaluate anticipated cardiovascular effects, early evaluation of hERG toxicity has been strongly recommended for instance by the regulatory agencies such as U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Thus, following an initial screening of a collection of compounds to find hits, a great number of pharmacomodulation studies on the novel identified chemical series need to be performed including activity evaluation towards hERG. We provide in this concise review clear guidelines, based on described examples, illustrating successful optimization process to avoid hERG interactions as cases studies and to spur scientists to develop safe drugs.

Evaluation of Possible Proarrhythmic Potency: Comparison of the Effect of Dofetilide, Cisapride, Sotalol, Terfenadine, and Verapamil on hERG and Native IKr Currents and on Cardiac Action Potential

The proarrhythmic potency of drugs is usually attributed to the IKr current block. During safety pharmacology testing analysis of IKr in cardiomyocytes was replaced by human ether-a-go-go-related gene (hERG) test using automated patch-clamp systems in stable transfected cell lines. Aim of this study was to compare the effect of proarrhythmic compounds on hERG and IKr currents and on cardiac action potential. The hERG current was measured by using both automated and manual patch-clamp methods on HEK293 cells. The native ion currents (IKr, INaL, ICaL) were recorded from rabbit ventricular myocytes by manual patch-clamp technique. Action potentials in rabbit ventricular muscle and undiseased human donor hearts were studied by conventional microelectrode technique. Dofetilide, cisapride, sotalol, terfenadine, and verapamil blocked hERG channels at 37°C with an IC50 of 7 nM, 18 nM, 343 μM, 165 nM, and 214 nM, respectively. Using manual patch-clamp, the IC50 values of sotalol and terfenadine were 78 µM and 31 nM, respectively. The IC50 values calculated from IKr measurements at 37°C were 13 nM, 26 nM, 52 μM, 54 nM, and 268 nM, respectively. Cisapride, dofetilide, and sotalol excessively lengthened, terfenadine, and verapamil did not influence the action potential duration. Terfenadine significantly inhibited INaL and moderately ICaL, verapamil blocked only ICaL. Automated hERG assays may over/underestimate proarrhythmic risk. Manual patch-clamp has substantially higher sensitivity to certain drugs. Action potential studies are also required to analyze complex multichannel effects. Therefore, manual patch-clamp and action potential experiments should be a part of preclinical safety tests.

Drug Side-Effect Profiles Prediction: From Empirical to Structural Risk Minimization

The identification of drug side-effects is considered to be an important step in drug design, which could not only shorten the time but also reduce the cost of drug development. In this paper, we investigate the relationship between the potential side-effects of drug candidates and their chemical structures. The preliminary Regularized Regression (RR) model for drug side-effects prediction has promising features in the efficiency of model training and the existence of a closed form solution. It performs better than other state-of-the-art methods, in terms of minimum accuracy and average accuracy. In order to dig inside how drug structure will associate with side effect, we further propose weighted GTS (Generalized T-Student Kernel: WGTS) SVM model from a structural risk minimization perspective. The SVM model proposed in this paper provides a better understanding of drug side-effects in the process of drug development. The usefulness of the WGTS model lies in the superior performance in a cross validation setting on 888 approved drugs with 1385 side-effects profiling from SIDER database. This work is expected to shed light on intriguing studies that predict potential un-identifying side-effects and suggest how we can avoid drug side-effects by the removal of some distinguished chemical structures.

New flavonoid - N,N-dibenzyl(N-methyl)amine hybrids: Multi-target-directed agents for Alzheimer´s disease endowed with neurogenic properties

The design of multi-target directed ligands (MTDLs) is a valid approach for obtaining effective drugs for complex pathologies. MTDLs that combine neuro-repair properties and block the first steps of neurotoxic cascades could be the so long wanted remedies to treat neurodegenerative diseases (NDs). By linking two privileged scaffolds with well-known activities in ND-targets, the flavonoid and the N,N-dibenzyl(N-methyl)amine (DBMA) fragments, new CNS-permeable flavonoid - DBMA hybrids (1-13) were obtained. They were subjected to biological evaluation in a battery of targets involved in Alzheimer's disease (AD) and other NDs, namely human cholinesterases (hAChE/hBuChE), β-secretase (hBACE-1), monoamine oxidases (hMAO-A/B), lipoxygenase-5 (hLOX-5) and sigma receptors (σ₁R/σ₂R). After a funnel-type screening, 6,7-dimethoxychromone - DBMA (6) was highlighted due to its neurogenic properties and an interesting MTD-profile in hAChE, hLOX-5, hBACE-1 and σ₁R. Molecular dynamic simulations showed the most relevant drug-protein interactions of hybrid 6, which could synergistically contribute to neuronal regeneration and block neurodegeneration.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II

Assessing the hERG liability in the early stages of drug discovery programs is important. The recent increase of hERG-related information in public databases enabled various successful applications of machine learning techniques to predict hERG inhibition. However, most of these researches constructed the datasets from only one database, limiting the predictability and scope of the models. In this study, a hERG classification model was constructed using the largest dataset for hERG inhibition built by integrating multiple databases. The integrated dataset consisted of more than 291,000 structurally diverse compounds derived from ChEMBL, GOSTAR, PubChem, and hERGCentral. The prediction model was built by support vector machine (SVM) with descriptor selection based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the descriptor set for maximum prediction performance with the minimal number of descriptors. The SVM classification model using 72 selected descriptors and ECFP_4 structural fingerprints recorded kappa statistics of 0.733 and accuracy of 0.984 for the test set, substantially outperforming the prediction performance of the current commercial applications for hERG prediction. Finally, the applicability domain of the prediction model was assessed based on the molecular similarity between the training set and test set compounds.

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.

Construction of an integrated database for hERG blocking small molecules

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

New synthetic approaches towards analogues of bedaquiline

Multi-drug resistant tuberculosis (MDR-TB) is of growing global concern and threatens to undermine increasing efforts to control the worldwide spread of tuberculosis (TB). Bedaquiline has recently emerged as a new drug developed to specifically treat MDR-TB. Despite being highly effective as a result of its unique mode of action, bedaquiline has been associated with significant toxicities and as such, safety concerns are limiting its clinical use. In order to access pharmaceutical agents that exhibit an improved safety profile for the treatment of MDR-TB, new synthetic pathways to facilitate the preparation of bedaquiline and analogues thereof have been discovered.

Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks

Background

Blockage of some ion channels and in particular, the hERG (human Ether-a’-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (¹³C and ¹⁵N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model.

Results

An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs.

Conclusion

A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1895-2) contains supplementary material, which is available to authorized users.

Ventricular fibrillation due to overdose of loperamide, the "poor man's methadone"

Loperamide is an over-the-counter antidiarrheal agent that is considered by many patients to be safe, but has been used as a drug of abuse due to its opioid properties. However, cardiotoxicity has been reported, prompting the FDA to release a warning regarding the arrhythmogenic potential of loperamide. We present a case of a 38-year-old female presenting with cardiac arrest thought to be secondary to abuse of the loperamide that she was using to alleviate the heroin withdrawal symptoms. Cardiac ischemia and other drug toxicities were ruled out. Loperamide induces QTc prolongation and cardiac dysrhythmias. She had recurrent ventricular arrhythmias with multiple cardiac arrests. The persistence of the cardiotoxicity for a longer duration than previously reported in the literature is unique in this clinical presentation. We also highlight the potential mechanisms for loperamide cardiotoxicity and its challenging management.

Abbreviations: ACLS: Advanced cardiac life support; GI: Gastrointestinal

Computational approaches to understand the adverse drug effect on potassium, sodium and calcium channels for predicting TdP cardiac arrhythmias

Ion channels play a crucial role in the cardiovascular system. Our understanding of cardiac ion channel function has improved since their first discoveries. The flow of potassium, sodium and calcium ions across cardiomyocytes is vital for regular cardiac rhythm. Blockage of these channels, delays cardiac repolarization or tend to shorten repolarization and may induce arrhythmia. Detection of drug risk by channel blockade is considered essential for drug regulators. Advanced computational models can be used as an early screen for torsadogenic potential in drug candidates. New drug candidates that are determined to not cause blockage are more likely to pass successfully through preclinical trials and not be withdrawn later from the marketplace by manufacturer. Several different approved drugs, however, can cause a distinctive polymorphic ventricular arrhythmia known as torsade de pointes (TdP), which may lead to sudden death. The objective of the present study is to review the mechanisms and computational models used to assess the risk that a drug may TdP.

KEY POINTS

There is strong evidence from multiple studies that blockage of the L-type calcium current reduces risk of TdP. Blockage of sodium channels slows cardiac action potential conduction, however, not all sodium channel blocking antiarrhythmic drugs produce a significant effect, while late sodium channel block reduces TdP. Interestingly, there are some drugs that block the hERG potassium channel and therefore cause QT prolongation, but they are not associated with TdP. Recent studies confirmed the necessity of studying multiple distinctionic ion channels which are responsible for cardiac related diseases or TdP, to obtain an improved clinical TdP risk prediction of compound interactions and also for designing drugs.

GZ-793A inhibits the neurochemical effects of methamphetamine via a selective interaction with the vesicular monoamine transporter-2

Lobeline and lobelane inhibit the behavioral and neurochemical effects of methamphetamine via an interaction with the vesicular monoamine transporter-2 (VMAT2). However, lobeline has high affinity for nicotinic receptors, and tolerance develops to the behavioral effects of lobelane. A water-soluble analog of lobelane, R-N-(1,2-dihydroxypropyl)-2,6-cis-di-(4-methoxyphenethyl)piperidine hydrochloride (GZ-793A), also interacts selectively with VMAT2 to inhibit the effects of methamphetamine, but does not produce behavioral tolerance. The current study further evaluated the mechanism underlying the GZ-793A-mediated inhibition of the neurochemical effects of methamphetamine. In contrast to lobeline, GZ-793A does not interact with the agonist recognition site on α4β2^* and α7^* nicotinic receptors. GZ-793A (0.3-100µM) inhibited methamphetamine (5µM)-evoked fractional dopamine release from rat striatal slices, and did not evoke dopamine release in the absence of methamphetamine. Furthermore, GZ-793A (1-100µM) inhibited neither nicotine (30µM)-evoked nor electrical field-stimulation-evoked (100Hz/1min) fractional dopamine release. Unfortunately, GZ-793A inhibited [³H]dofetilide binding to human-ether-a-go-go related gene channels expressed on human embryonic kidney cells, and further, prolonged action potentials in rabbit cardiac Purkinje fibers, suggesting the potential for GZ-793A to induce ventricular arrhythmias. Thus, GZ-793A selectively inhibits the neurochemical effects of methamphetamine and lacks nicotinic receptor interactions; however, development as a pharmacotherapy for methamphetamine use disorders will not be pursued due to its potential cardiac liabilities.

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

BACKGROUND

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

METHODS AND RESULTS

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

CONCLUSIONS

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

Investigation of miscellaneous hERG inhibition in large diverse compound collection using automated patch-clamp assay

AIM

hERG potassium channels display miscellaneous interactions with diverse chemical scaffolds. In this study we assessed the hERG inhibition in a large compound library of diverse chemical entities and provided data for better understanding of the mechanisms underlying promiscuity of hERG inhibition.

METHODS

Approximately 300 000 compounds contained in Molecular Library Small Molecular Repository (MLSMR) library were tested. Compound profiling was conducted on hERG-CHO cells using the automated patch-clamp platform-IonWorks Quattro(™).

RESULTS

The compound library was tested at 1 and 10 μmol/L. IC50 values were predicted using a modified 4-parameter logistic model. Inhibitor hits were binned into three groups based on their potency: high (IC50<1 μmol/L), intermediate (1 μmol/L< IC50<10 μmol/L), and low (IC50>10 μmol/L) with hit rates of 1.64%, 9.17% and 16.63%, respectively. Six physiochemical properties of each compound were acquired and calculated using ACD software to evaluate the correlation between hERG inhibition and the properties: hERG inhibition was positively correlative to the physiochemical properties ALogP, molecular weight and RTB, and negatively correlative to TPSA.

CONCLUSION

Based on a large diverse compound collection, this study provides experimental evidence to understand the promiscuity of hERG inhibition. This study further demonstrates that hERG liability compounds tend to be more hydrophobic, high-molecular, flexible and polarizable.

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).

Global analysis reveals families of chemical motifs enriched for HERG inhibitors

Promiscuous inhibition of the human ether-à-go-go-related gene (hERG) potassium channel by drugs poses a major risk for life threatening arrhythmia and costly drug withdrawals. Current knowledge of this phenomenon is derived from a limited number of known drugs and tool compounds. However, in a diverse, naïve chemical library, it remains unclear which and to what degree chemical motifs or scaffolds might be enriched for hERG inhibition. Here we report electrophysiology measurements of hERG inhibition and computational analyses of >300,000 diverse small molecules. We identify chemical ‘communities’ with high hERG liability, containing both canonical scaffolds and structurally distinctive molecules. These data enable the development of more effective classifiers to computationally assess hERG risk. The resultant predictive models now accurately classify naïve compound libraries for tendency of hERG inhibition. Together these results provide a more complete reference map of characteristic chemical motifs for hERG liability and advance a systematic approach to rank chemical collections for cardiotoxicity risk.

The Therapeutic Potential of hERG1 K+ Channels for Treating Cancer and Cardiac Arrhythmias

hERG potassium channels present pharmacologists and medicinal chemists with a dilemma. On the one hand hERG is a major reason for drugs being withdrawn from the market because of drug induced long QT syndrome and the associated risk of inducing sudden cardiac death, and yet hERG blockers are still widely used in the clinic to treat cardiac arrhythmias. Moreover, in the last decade overwhelming evidence has been provided that hERG channels are aberrantly expressed in cancer cells and that they contribute to tumour cell proliferation, resistance to apoptosis, and neoangiogenesis. Here we provide an overview of the properties of hERG channels and their role in excitable cells of the heart and nervous system as well as in cancer. We consider the therapeutic potential of hERG, not only with regard to the negative impact due to drug induced long QT syndrome, but also its future potential as a treatment in the fight against cancer.

Global Performance and Trend of QSAR/QSPR Research: A Bibliometric Analysis

A bibliometric analysis based on the Science Citation Index Expanded was conducted to provide insights into the publication performance and research trend of quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) from 1993 to 2012. The results show that the number of articles per year quadrupled from 1993 to 2006 and plateaued since 2007. Journal of Chemical Information and Modeling was the most prolific journal. The internal methodological innovations in acquiring molecular descriptors and modeling stimulated the articles' increase in the research fields of drug design and synthesis, and chemoinformatics; while the external regulatory demands on model validation and reliability fueled the increase in environmental sciences. "Prediction endpoints", "statistical algorithms", and "molecular descriptors" were identified as three research hotspots. The articles from developed countries were larger in number and more influential in citation, whereas those from developing countries were higher in output growth rates.

Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

AIM

A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels.

METHODS

Doddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation.

RESULTS

A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation.

CONCLUSION

The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.

Novel arylsulfonamide derivatives with 5-HT₆/5-HT₇ receptor antagonism targeting behavioral and psychological symptoms of dementia

In order to target behavioral and psychological symptoms of dementia (BPSD), we used molecular modeling-assisted design to obtain novel multifunctional arylsulfonamide derivatives that potently antagonize 5-HT(6/7/2A) and D2 receptors, without interacting with M1 receptors and hERG channels. In vitro studies confirmed their antagonism of 5-HT(7/2A) and D2 receptors and weak interactions with key antitargets (M1R and hERG) associated with side effects. Marked 5-HT6 receptor affinities were also observed, notably for 6-fluoro-3-(piperidin-4-yl)-1,2-benzoxazole derivatives connected by a 3-4 unit alkyl linker with mono- or bicyclic, lipophilic arylsulfonamide moieties. N-[4-[4-(6-Fluoro-1,2-benzoxazol-3-yl)piperidin-1-yl]butyl]benzothiophene-2-sulfonamide (72) was characterized in vitro on 14 targets and antitargets. It displayed dual blockade of 5-HT6 and D2 receptors and negligible interactions at hERG and M1 receptors. Unlike reference antipsychotics, 72 displayed marked antipsychotic and antidepressant activity in rats after oral administration, in the absence of cognitive or motor impairment. This profile is particularly attractive when targeting a fragile, elderly BPSD patient population.

Recent approaches to medicinal chemistry and therapeutic potential of dipeptidyl peptidase-4 (DPP-4) inhibitors

Dipeptidyl peptidase-4 (DPP-4) is one of the widely explored novel targets for Type 2 diabetes mellitus (T2DM) currently. Research has been focused on the strategy to preserve the endogenous glucagon like peptide (GLP)-1 activity by inhibiting the DPP-4 action. The DPP-4 inhibitors are weight neutral, well tolerated and give better glycaemic control over a longer duration of time compared to existing conventional therapies. The journey of DPP-4 inhibitors in the market started from the launch of sitagliptin in 2006 to latest drug teneligliptin in 2012. This review is mainly focusing on the recent medicinal aspects and advancements in the designing of DPP-4 inhibitors with the therapeutic potential of DPP-4 as a target to convey more clarity in the diffused data.

Progress in computational toxicology

INTRODUCTION

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed.

METHODS

A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods.

RESULTS

The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data.

DISCUSSION

Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.

Combining structure- and ligand-based approaches for studies of interactions between different conformations of the hERG K+ channel pore and known ligands

Drug-induced insurgence of cardiotoxic effects signaled by the prolongation of the QT interval in the electrocardiogram, has the potential to evolve into a characteristic arrhythmic event named Torsade de Pointes (TdP). Although several different mechanisms can theoretically lead to prolonged QT interval, most of drugs showing this side effect, prolong the cardiac repolarization time through the inhibition of the rapid component of the delayed repolarizing current (IKr) which in humans is carried by a K(+) channel protein encoded by hERG. In this study, four 3D-models, representing different conformational states of hERG K(+) channel, were built by a homology-based technique. A dataset of 59 compounds was collected from the literature and rationally selected according to the availability of IC50 values derived from whole-cell patch clamp performed at 37 °C on HEK cells. Molecular docking was carried out on each one of the four conformations of the channel, hundreds of docking-based molecular descriptors were obtained and used, together with other 2D and 3D molecular descriptors, to develop QSAR models. The statistical parameters describing the accordance between predicted and experimental data and the interpretation of the QSAR models enabled us to assess the reliability of the four 3D-models of the channel pore, thus allowing to look in more depth at binding modes and key features of the interactions occurring between the hERG K(+) channel and ligands endowed of blocking activity.