In Silico Prediction of the Chemical Block of Human Ether-a-Go-Go-Related Gene (hERG) K+ Current

Abstract

A variety of compounds with different chemical properties directly interact with the cardiac repolarizing K + channel encoded by the human ether-a-go-go-related gene (hERG). This causes acquired forms of QT prolongation, which can result in lethal cardiac arrhythmias, including torsades de pointes one of the most serious adverse effects of various therapeutic agents. Prediction of this phenomenon will improve the safety of pharmacological therapy and also facilitate the process of drug development. Here we propose a strategy for the development of an in silico system to predict the potency of chemical compounds to block hERG. The system consists of two sequential processes. The first process is a ligand-based prediction to estimate half-maximal concentrations for the block of compounds inhibiting hERG current using the relationship between chemical features and activities of compounds. The second process is a protein-based prediction that comprises homology modeling of hERG, docking simulation of chemical-channel interaction, analysis of the shape of the channel pore cavity, and Brownian dynamics simulation to estimate hERG currents in the presence and absence of chemical blockers. Since each process is a combination of various calculations, the criterion for assessment at each calculation and the strategy to integrate these steps are significant for the construction of the system to predict a chemical's block of hERG current and also to predict the risk of inducing cardiac arrhythmias from the chemical information. The principles and criteria of elemental computations along this strategy are described.Key words: hERG, quantitative structure-activity relationship, molecular docking, Brownian dynamics simulation.Many ion channels and ion transporting systems contribute to the generation of the action potential of the heart. The ventricular action potential possesses a longlasting plateau that is terminated by the activation of repolarizing K + currents, I Kr and I Ks . The disturbance of these currents causes prolongation of the action potential duration and thus the QT interval of the electrocardiogram, and in many cases a worsening of transmural dispersion of repolarization [1][2][3]. These are the substrates for generation of life-threatening cardiac arrhythmias, including torsades de pointes. Many compounds inhibit I Kr , whose pore-forming subunit is the human ether-a-gogo-related gene (hERG) product. This can cause acquired forms of long QT syndrome [4][5][6]. The compounds exhibit a broad spectrum, including not only cardiac ion channel blockers, but also antipsychotic agents, antidepressant agents, antimicrobial agents, phosphodiesterase inhibitors, topoisomerase II inhibitors, and antagonists for G protein-coupled receptors, such as nonsedating antihistamine H 1 receptor blockers and β blockers [6]. The development of methods that could predict this phenomenon would improve the safety of pharmacological therapy and also facilitate the process of drug development.Here we propose an in silico str...

Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty

“…There have been many different publications on models for hERG binding, including classic QSAR models, pharmacophore models, homology models, and MMPA. − The general hERG pharmacophore includes a positively ionizable nitrogen and two or three aliphatic or aromatic features distributed around the positively ionizable feature. It has been recognized that both log P ow and the p K a of the nitrogen are correlated with hERG binding.…”

Tuning hERG Out: Antitarget QSAR Models for Drug Development

“…Most of those models are indeed not compliant with the OECD guidance on QSAR model development and validation [82]. More specifically, the primary drawbacks of the majority of published QSAR studies are: (i) most models do not have proof of passing the Y-randomization test [21, 23, 26, 28, 29, 31–35, 38, 40, 41, 45–49, 51–56, 58, 59, 63–65, 68–70, 75, 79]; (ii) no proof of applicability domain (AD) estimation is provided [21, 23, 26–29, 31–36, 40, 45, 48–53, 56, 58, 63–65, 68–71, 75, 79]; and (iii) model predictivity is not acceptable [39, 61, 66]. As a consequence, despite the large number of QSAR models for hERG blockage available in the literature, only very few models can actually be employed to predict hERG blockage [60, 61, 74, 78].…”

Evaluation of an in silico cardiac safety assay: Using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge

“…Through integrated risk assessment, these approaches could complement existing safety tests and reduce the current use of animal-based experiments. An in silico approach, frequently enlisted within pharmaceutical companies, is Quantitative Structure Activity Relationship (QSAR) modelling which uses information regarding the chemical structure of compounds to infer properties of their biological activity (Inanobe et al, 2008).…”