The hERG Potassium Channel and Drug Trapping: Insight from Docking Studies with Propafenone Derivatives

Antiarrhythmic effects of newly developed propafenone derivatives

It is well known that the presence of different chemical groups in drug molecules influences their pharmacological properties. The aim of our study is to investigate whether newly synthesized derivatives of propafenone, with changes in benzyl moiety, have a different effect upon arrhythmia, compared to propafenone. 5OCl-PF and 5OF-PF are derivatives of propafenone with -Cl or -F substituent on the ortho position of the benzyl moiety. For verification of their antiarrhythmic effect, we used an in vivo rat model of aconitine-induced arrhythmia. 5OCl-PF speeded the appearance of supraventricular premature beats (SVPB) and death more than aconitine. All animals treated with 5OCl-PF developed ventricular premature beats in salvos (VPBS), bigeminies (VPBB) and paroxysmal ventricular tachycardia (PVT). 5OF-PF had a negative chronotropic effect and potentiated atrial excitability (more SVPB). It had a positive effect on the occurrence and onset time of supraventricular tachycardia, VPBS, and PVT. Based on the obtained results, it can be concluded that newly synthesized propafenone derivatives have no better antiarrhythmic effect than the parent compound. In the future, our research will be focused on the synthesis of different derivatives and examining their antiarrhythmic effects.

TSSF-hERG: A machine-learning-based hERG potassium channel-specific scoring function for chemical cardiotoxicity prediction

The human ether-à-go-go-related gene (hERG) encodes the Kv11.1 voltage-gated potassium ion (K⁺) channel that conducts the rapidly activating delayed rectifier current (I_Kr) in cardiomyocytes to regulate the repolarization process. Some drugs, as blockers of hERG potassium channels, cannot be marketed due to prolonged QT intervals, as well known as cardiotoxicity. Predetermining the binding affinity values between drugs and hERG through in silico methods can greatly reduce the time and cost required for experimental verification. In this study, we collected 9,215 compounds with AutoDock Vina's docking structures as training set, and collected compounds from four references as test sets. A series of models for predicting the binding affinities of hERG blockers were built based on five machine learning algorithms and combinations of interaction features and ligand features. The model built by support vector regression (SVR) using the combination of all features achieved the best performance on both tenfold cross-validation and external verification, which was selected and named as TSSF-hERG (target-specific scoring function for hERG). TSSF-hERG is more accurate than the classic scoring function of AutoDock Vina and the machine-learning-based generic scoring function RF-Score, with a Pearson's correlation coefficient (Rp) of 0.765, a Spearman's rank correlation coefficient (Rs) of 0.757, a root-mean-square error (RMSE) of 0.585 in a tenfold cross-validation study. All results demonstrated that TSSF-hERG would be useful for improving the power of binding affinity prediction between hERG and compounds, which can be further used for prediction or virtual screening of the hERG-related cardiotoxicity of drug candidates.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

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

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

Toward a unifying strategy for the structure-based prediction of toxicological endpoints

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.

Computational assay of Zanamivir binding affinity with original and mutant influenza neuraminidase 9 using molecular docking

Based upon molecular docking, this study aimed to find notable in silico neuraminidase 9 (NA9) point mutations of the avian influenza A H7N9 virus that possess a Zanamivir resistant property and to determine the lead compound capable of inhibiting these NA9 mutations. Seven amino acids (key residues) at the binding site of neuraminidase 9 responsible for Zanamivir-NA9 direct interactions were identified and 72 commonly occurring mutant NA9 versions were created using the Sybyl-X 2.0 software. The docking scores obtained after Zanamivir was bound to all mutant molecules of NA9 revealed 3 notable mutations R292W, R118P, and R292K that could greatly reduce the binding affinity of the medicine. These 3 mutant NA9 versions were then bound to each of 154 different molecules chosen from 5 groups of compounds to determine which molecule(s) might be capable of inhibiting mutant neuraminidase 9, leading to the discovery of the lead compound of potent mutant NA9 inhibitors. This compound, together with other mutations occurring to NA9 identified in the study, would be used as data for further research regarding neuraminidase inhibitors and synthesizing new viable medications used in the fight against the virus.

Molecular docking and molecular dynamics studies on the structure-activity relationship of fluoroquinolone for the HERG channel

Fluoroquinolones play an important role in the treatment of serious bacterial infections, but at the same time they could lead to cardiac toxicity due to the blockage of the HERG potassium channel, which even leads to the withdrawal of some fluoroquinolones. Blockage of the HERG potassium channel by drugs or drug-like compounds has become a critical problem in drug discovery. Though there were large amounts of bioactivity data of fluoroquinolones on the blockage of HERG, little structural basis of binding of blockers to the HERG channel was known. Here, we combined molecular docking, molecular dynamics simulations, free energy calculations and binding energy decomposition analysis to explore the binding modes of fluoroquinolones in the HERG potassium channel. The calculated binding free energies were consistent with the experimental binding affinities. Our results showed that the CH3 group in MX was favorable for the binding to the HERG channel, while Tyr652 and Phe656 were critical for the hydrophobic interaction between fluoroquinolones and the HERG channel. We expected that our results of calculation could provide important insights for the rational design and discovery of drugs.

Modelling three-dimensional protein structures for applications in drug design

A structural perspective of drug target and anti-target proteins, and their molecular interactions with biologically active molecules, largely advances many areas of drug discovery, including target validation, hit and lead finding and lead optimisation. In the absence of experimental 3D structures, protein structure prediction often offers a suitable alternative to facilitate structure-based studies. This review outlines recent methodical advances in homology modelling, with a focus on those techniques that necessitate consideration of ligand binding. In this context, model quality estimation deserves special attention because the accuracy and reliability of different structure prediction techniques vary considerably, and the quality of a model ultimately determines its usefulness for structure-based drug discovery. Examples of G-protein-coupled receptors (GPCRs) and ADMET-related proteins were selected to illustrate recent progress and current limitations of protein structure prediction. Basic guidelines for good modelling practice are also provided.

How to solve the problems of docking into a symmetric binding site: the example of the HERG channel

Many proteins, such as the hERG K⁺ channel or the HIV-1 protease, have a high degree of rotational symmetry. If the binding site of a ligand is composed of symmetrical subunits, the analysis of the docking poses of ligands is quite challenging. In the case of hERG, the four-fold symmetry of the entire channel is fully reflected in the binding site, which allows up to four poses with different coordinates of the ligand, but an identical interaction pattern. In light of our docking studies into the hERG potassium channel, we developed an algorithm (ROTALI) to detect the poses that are duplicates due to the symmetry of the channel. This led to a reduction in the number of poses to be considered in the subsequent steps by up to 52%.

hERG K+ Channels: Structure, Function, and Clinical Significance

The human ether-a-go-go related gene (hERG) encodes the pore-forming subunit of the rapid component of the delayed rectifier K+ channel, Kv11.1, which are expressed in the heart, various brain regions, smooth muscle cells, endocrine cells, and a wide range of tumor cell lines. However, it is the role that Kv11.1 channels play in the heart that has been best characterized, for two main reasons. First, it is the gene product involved in chromosome 7-associated long QT syndrome (LQTS), an inherited disorder associated with a markedly increased risk of ventricular arrhythmias and sudden cardiac death. Second, blockade of Kv11.1, by a wide range of prescription medications, causes drug-induced QT prolongation with an increase in risk of sudden cardiac arrest. In the first part of this review, the properties of Kv11.1 channels, including biogenesis, trafficking, gating, and pharmacology are discussed, while the second part focuses on the pathophysiology of Kv11.1 channels.

Trapping and dissociation of propafenone derivatives in HERG channels

BACKGROUND AND PURPOSE

Human ether-a-go-go related gene (HERG) channel inhibitors may be subdivided into compounds that are trapped in the closed channel conformation and others that dissociate at rest. The structural peculiarities promoting resting state dissociation from HERG channels are currently unknown. A small molecule-like propafenone is efficiently trapped in the closed HERG channel conformation. The aim of this study was to identify structural moieties that would promote dissociation of propafenone derivatives.

EXPERIMENTAL APPROACH

Human ether-a-go-go related gene channels were heterologously expressed in Xenopus oocytes and potassium currents were recorded using the two-microelectrode voltage clamp technique. Recovery from block by 10 propafenone derivatives with variable side chains, but a conserved putative pharmacophore, was analysed.

KEY RESULTS

We have identified structural determinants of propafenone derivatives that enable drug dissociation from the closed channel state. Propafenone and four derivatives with 'short' side chains were trapped in the closed channel. Five out of six bulky derivatives efficiently dissociated from the channel at rest. One propafenone derivative with a similar bulk but lacking an H-bond acceptor in this region was trapped. Correlations were observed between molecular weight and onset of channel block as well as between pK(a) and recovery at rest.

CONCLUSION AND IMPLICATIONS

The data show that extending the size of a trapped HERG blocker-like propafenone by adding a bulky side chain may impede channel closure and thereby facilitate drug dissociation at rest. The presence of an H-bond acceptor in the bulky side chain is, however, essential.