A novel structure-based virtual screening model for the hERG channel blockers

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.

Linked biaromatic compounds as cardioprotective agents

Cardiovascular diseases (CVDs) are widespread in the modern world, and their number is constantly growing. For a long time, CVDs have been the leading cause of morbidity and mortality worldwide. Drugs for the treatment of CVD have been developed almost since the beginning of the 20th century, and a large number of effective cardioprotective agents of various classes have been created. Nevertheless, the need for the design and development of new safe drugs for the treatment of CVD remains. Literature data indicate that a huge number of cardioprotective agents of various generations and mechanisms correspond to a single generalized pharmacophore model containing two aromatic nuclei linked by a linear linker. In this regard, we put forward a concept for the design of a new generation of cardioprotective agents with a multitarget mechanism of action within the indicated pharmacophore model. This review is devoted to a generalization of the currently known compounds with cardioprotective properties and corresponding to the pharmacophore model of biaromatic compounds linked by a linear linker. Particular attention is paid to the history of the creation of these drugs, approaches to their design, and analysis of the structure-action relationship within each class.

Astemizole Sensitizes Adrenocortical Carcinoma Cells to Doxorubicin by Inhibiting Patched Drug Efflux Activity

Adrenocortical carcinoma (ACC) presents a high risk of relapse and metastases with outcomes not improving despite extensive research and new targeted therapies. We recently showed that the Hedgehog receptor Patched is expressed in ACC, where it strongly contributes to doxorubicin efflux and treatment resistance. Here, we report the identification of a new inhibitor of Patched drug efflux, the anti-histaminergic drug astemizole. We show that astemizole enhances the cytotoxic, proapoptotic, antiproliferative and anticlonogenic effects of doxorubicin on ACC cells at concentrations of astemizole or doxorubicin that are not effective by themselves. Our results suggest that a low concentration of astemizole sensitizes ACC cells to doxorubicin, which is a component of the standard treatment for ACC composed of etoposide, doxorubicin, cisplatin and mitotane (EDPM). Patched uses the proton motive force to efflux drugs. This makes its function specific to cancer cells, thereby avoiding toxicity issues that are commonly observed with inhibitors of ABC multidrug transporters. Our data provide strong evidence that the use of astemizole or a derivative in combination with EDPM could be a promising therapeutic option for ACC by increasing the treatment effectiveness at lower doses of EDPM, which would reduce the severe side effects of this regimen.

Astemizole-based turn-on fluorescent probes for imaging hERG potassium channel

Based on the scaffold of astemizole, three novel turn-on fluorescent probes (N1-N3) for human ether-a-go-go-related gene (hERG) potassium channel were developed herein. These probes have reasonable fluorescence properties, acceptable cell toxicity, and potent inhibitory activity, all of which contribute to cell imaging at the nanomolar level. Overall, these probes have the potential for setting up a screening system for hERG channels.

Aggregation-Induced Emission: Lighting Up hERG Potassium Channel

Based on the scaffold of astemizole and E-4031, four AIE light-up probes (L1-L4) for Human Ether-a-go-go-Related Gene (hERG) potassium channel were developed herein using AIE fluorogen(TPE). These probes showing advantages such as low background interference, superior photostability, acceptable cell toxicity, and potent inhibitory activity, which could be used to image hERG channels at the nanomolar level. These AIE light-up probes hoped to provide guidelines for the design of more advanced AIE sensing and imaging hERG channels to a broad range of applications.

A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins

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

Prediction of hERG Liability - Using SVM Classification, Bootstrapping and Jackknifing

Drug-induced QT prolongation leads to life-threatening cardiotoxicity, mostly through blockage of the human ether-à-go-go-related gene (hERG) encoded potassium ion (K+ ) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under-sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC-ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities.

Low dose extended exposure to saxitoxin and its potential neurodevelopmental effects: A review

Saxitoxin (STX) and its analogs, the paralytic shellfish toxins (PSTs), are a group of potent neurotoxins well known for their role in acute paralytic poisoning by preventing the generation of action potentials in neuronal cells. They are found in both marine and freshwater environments globally and although acute exposure from the former has previously received more attention, low dose extended exposure from both sources is possible and to date has not been investigated. Given the known role of cellular electrical activity in neurodevelopment this pattern of exposure may be a significant public health concern. Additionally, the presence of PSTs is likely to be an ongoing and possibly increasing problem in the future. This review examines the neurodevelopmental toxicity of STX, the risk of extended or repeated exposure to doses with neurodevelopmental effects, the potential implications of this exposure and briefly, the steps taken and difficulties faced in preventing exposure.

Astemizole Derivatives as Fluorescent Probes for hERG Potassium Channel Imaging

The detection and imaging of hERG potassium channels in living cells can provide useful information for hERG-correlation studies. Herein, three small-molecule fluorescent probes, based on the potent hERG channel inhibitor astemizole, for the imaging of hERG channels in hERG-transfected HEK293 cells (hERG-HEK293) and human colorectal cancer cells (HT-29), are described. These probes are expected to be applied in the physiological and pathological studies of hERG channels.

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).

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

Analysis of van der Waals surface area properties for human ether-a-go-go-related gene blocking activity: computational study on structurally diverse compounds

In the present investigation, a computational analysis was performed on a data set comprised of human ether-a-go-go-related gene (hERG) blockers (triethanolamine, 1,3-thiazol-2-yl and tetrasubstituted imidazoline derivatives) in order to investigate the structural features required to reduce the hERG-induced cardiotoxicity problems in an early stage of drug discovery. The results derived from the quantitative structure-activity relationship (QSAR) analysis showed that the volume, surface area and shape descriptors (vsurf_) contributed significantly in all the models. This reveals that the hydrogen-bonding and hydrophilicity properties (vsurf_HB1, vsurf_CW4 and a_acc) on the van der Waals (vdW) surface of the molecule is negatively contributed for the hERG blocking activity and the hydrophobic property (vsurf_D6) and the total polar volume (vsurf_Wp2) on the vdW surface of the molecule are favourable for the activity. Further, the pharmacophore analysis also shows that the Aro/Hyd/Acc contour is one of the important biophore sites for the hERG blocking activity. This suggests that the presence of aromatic, hydrophobic and hydrogen-bonding groups in the molecules is favourable for interaction. In comparison with our earlier works (explaining the role of topological and hydrophobicity properties for the hERG blocking activity), these studies provided additional information on the importance of vdW surface area properties for the hERG blocking activity. These results can be used with other molecular modelling studies for the design of novel molecules that are free of cardiotoxicity.

Drug repositioning for personalized medicine

Human diseases can be caused by complex mechanisms involving aberrations in numerous proteins and pathways. With recent advances in genomics, elucidating the molecular basis of disease on a personalized level has become an attainable goal. In many cases, relevant molecular targets will be identified for which approved drugs already exist, and the potential repositioning of these drugs to a new indication can be investigated. Repositioning is an accelerated route for drug discovery because existing drugs have established clinical and pharmacokinetic data. Personalized medicine and repositioning both aim to improve the productivity of current drug discovery pipelines, which expend enormous time and cost to develop new drugs, only to have them fail in clinical trials because of lack of efficacy or toxicity. Here, we discuss the current state of research in these two fields, focusing on recent large-scale efforts to systematically find repositioning candidates and elucidate individual disease mechanisms in cancer. We also discuss scenarios in which personalized drug repositioning could be particularly rewarding, such as for diseases that are rare or have specific mutations, as well as current challenges in this field. With an increasing number of drugs being approved for rare cancer subtypes, personalized medicine and repositioning approaches are poised to significantly alter the way we diagnose diseases, infer treatments and develop new drugs.

ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage

Inhibition of the human ether-a-go-go related gene (hERG) potassium channel may result in QT interval prolongation, which causes severe cardiac side effects and is a major problem in clinical studies of drug candidates. The development of in silico tools to filter out potential hERG potassium channel blockers in early stages of the drug discovery process is of considerable interest. Here, a diverse set of 806 compounds with hERG inhibition data was assembled, and the binary hERG classification models using naive Bayesian classification and recursive partitioning (RP) techniques were established and evaluated. The naive Bayesian classifier based on molecular properties and the ECFP_8 fingerprints yielded 84.8% accuracy for the training set using the leave-one-out (LOO) cross-validation procedure and 85% accuracy for the test set of 120 molecules. For the two additional test sets, the model achieved 89.4% accuracy for the WOMBAT-PK test set, and 86.1% accuracy for the PubChem test set. The naive Bayesian classifiers gave better predictions than the RP classifiers. Moreover, the Bayesian classifier, employing molecular fingerprints, highlights the important structural fragments favorable or unfavorable for hERG potassium channel blockage, which offers extra valuable information for the design of compounds avoiding undesirable hERG activity.

Three-Dimensional Molecular Modelling of Receptor-Based Mechanisms in Toxicology

Previous chapters have discussed the generation and use of relatively simple descriptors (such as log P, topological descriptors etc) in predicting toxicity; such descriptors alone can accurately predict certain endpoints. However, other endpoints require a more complex modelling process. Molecules exist as 3-dimensional entities and where toxicity is the result of specific spatially-related interactions between the toxicant and a biological macromolecule, for example receptor-mediated effects, models must be able to take into account this 3-dimensional interaction. This chapter will present a brief overview of the use of ligand-based and receptors-based 3-dimensional approaches in toxicity prediction. An introduction to relevant software and example case studies where the approaches have been successfully employed will be presented.

Collation, assessment and analysis of literature in vitro data on hERG receptor blocking potency for subsequent modeling of drugs' cardiotoxic properties

The assessment of the torsadogenic potency of a new chemical entity is a crucial issue during lead optimization and the drug development process. It is required by the regulatory agencies during the registration process. In recent years, there has been a considerable interest in developing in silico models, which allow prediction of drug-hERG channel interaction at the early stage of a drug development process. The main mechanism underlying an acquired QT syndrome and a potentially fatal arrhythmia called torsades de pointes is the inhibition of potassium channel encoded by hERG (the human ether-a-go-go-related gene). The concentration producing half-maximal block of the hERG potassium current (IC(50)) is a surrogate marker for proarrhythmic properties of compounds and is considered a test for cardiac safety of drugs or drug candidates. The IC(50) values, obtained from data collected during electrophysiological studies, are highly dependent on experimental conditions (i.e. model, temperature, voltage protocol). For the in silico models' quality and performance, the data quality and consistency is a crucial issue. Therefore the main objective of our work was to collect and assess the hERG IC(50) data available in accessible scientific literature to provide a high-quality data set for further studies.

A composite model for HERG blockade

hERG blockade is one of the major toxicological problems in lead structure optimization. Reliable ligand-based in silico models for predicting hERG blockade therefore have considerable potential for saving time and money, as patch-clamp measurements are very expensive and no crystal structures of the hERG-encoded channel are available. Herein we present a predictive QSAR model for hERG blockade that differentiates between specific and nonspecific binding. Specific binders are identified by preliminary pharmacophore scanning. In addition to descriptor-based models for the compounds selected as hitting one of two different pharmacophores, we also use a model for nonspecific binding that reproduces blocking properties of molecules that do not fit either of the two pharmacophores. PLS and SVR models based on interpretable quantum mechanically derived descriptors on a literature dataset of 113 molecules reach overall R(2) values between 0.60 and 0.70 for independent validation sets and R(2) values between 0.39 and 0.76 after partitioning according to the pharmacophore search for the test sets. Our findings suggest that hERG blockade may occur through different types of binding, so that several different models may be necessary to assess hERG toxicity.

Computational studies of the binding site of alpha1A-adrenoceptor antagonists

Aimed at achieving a good understanding of the 3-dimensional structures of human alpha1A-adrenoceptor (alpha1A-AR), we have successfully developed its homology model based on the crystal structure of beta2-AR. Subsequent structural refinements were performed to mimic the receptor's natural membrane environment by using molecular mechanics (MM) and molecular dynamics (MD) simulations in the GBSW implicit membrane model. Through molecular docking and further simulations, possible binding modes of subtype-selective alpha1A-AR antagonists, Silodosin, RWJ-69736 and (+)SNAP-7915, were examined. Results of the modeling and docking studies are qualitatively consistent with available experimental data from mutagenesis studies. The homology model built should be very useful for designing more potent subtype-selective alpha1A-AR antagonists and for guiding further mutagenesis studies.

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed 'binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor (kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant K(i) of 135 microM. Our studies suggest that validated QSAR models could complement structure based docking and scoring approaches in identifying promising hits by virtual screening of molecular libraries.

Computer-aided prediction of QT-prolongation

Drug-induced cardiac arrhythmia is acknowledged as a serious obstacle in successful development of new drugs. Several methods for in silico prediction of acquired long QT syndrome (LQTS) caused by the pharmacological blockade of human hERG K+ channels are discussed in literature. We propose to use the computer program PASS, which estimates the probabilities of about 3000 biological activities, not only for prediction of hERG blockade and QT-prolongation but also for the analysis of indirect mechanisms of these actions. After addition in the PASS training set of 163 compounds with data on QT-Prolongation and re-training, it was shown that accuracy of prediction was 87.1% and 81.8% for hERG blockade and QT-prolongation, respectively. Using computer program PharmaExpert we found that in the predicted biological activity spectra there was a certain correlation between the hERG blockade and some other molecular mechanisms of action. Possible role of 1-phosphatidylinositol-4-phospate 5-kinase, dimethylargininase and progesterone 11 alpha-monooxygenase inhibition in hERG blockade was discussed.