In Silico Classification of hERG Channel Blockers: a Knowledge‐Based Strategy

Dubus, Elodie; Ijjaali, Ismaïl; Petitet, François; Michel, Aurélia

doi:10.1002/cmdc.200500099

Cited by 49 publications

(73 citation statements)

References 24 publications

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“…Recent studies have involved homology modeling, docking studies, classical or hologram 2D-QSAR (two-dimensional quantitative structure-activity relationships) and 3D-QSAR (Aptula and Cronon, 2004;Aronov, 2005;Cavalli et al, 2002;Cianchetta et al, 2005;Coi et al, 2006;Diller and Hobbs, 2007;Du et al, 2004Du et al, , 2007Ekins et al, 2002Ekins et al, , 2006Fioravanzo et al, 2004;Guth, 2007;Johnson et al, 2007;Keserü, 2003;Pearlstein et al, 2003;Seierstad and Agrafiotis, 2006;Thai and Ecker, 2008) or classification methods (Aronov and Goldman, 2004;Aronov, 2005;Dubus et al, 2006;Gepp and Hutter, 2006;Guth, 2007;Leong, 2007;Li et al, 2008;Song and Clark, 2006;Sun, 2006;Tobita et al, 2005;Waring and Johnstone, 2007). The data quality and consistency is the crucial issue for the in silico models' quality and performance.…”

Section: Electrophysiological Assaysmentioning

confidence: 98%

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

Polak¹,

Wiśniowska

Brandys³

2008

J of Applied Toxicology

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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.

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Section: Electrophysiological Assaysmentioning

confidence: 98%

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

Polak¹,

Wiśniowska

Brandys³

2008

J of Applied Toxicology

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“…Whilst some models [13][14][15][16][17] have been developed to discriminate between potent inhibitors (IC 50 < 1 mM) and 'non-inhibitors' (IC 50 > X mM, where X ! 10), these would be of limited utility in drug discovery, where many compound series exhibit 'moderate' inhibition -with IC 50 values in the range of 1-10 mM.…”

Section: Binary Classifiersmentioning

confidence: 99%

Development and Comparison of hERG Blocker Classifiers: Assessment on Different Datasets Yields Markedly Different Results

Robinson

Glen

Mitchell

2011

Molecular Informatics

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In recent years, considerable effort has been invested in the development of classification models for prospective hERG inhibitors, due to the implications of hERG blockade for cardiotoxicity and the low throughput of functional hERG assays. We present novel approaches for binary classification which seek to separate strong inhibitors (IC50 <1 µM) from 'non-blockers' exhibiting moderate (1-10 µM) or weak (IC50 ≥10 µM) inhibition, as required by the pharmaceutical industry. Our approaches are based on (discretized) 2D descriptors, selected using Winnow, with additional models generated using Random Forest (RF) and Support Vector Machines (SVMs). We compare our models to those previously developed by Thai and Ecker and by Dubus et al. The purpose of this paper is twofold: 1. To propose that our approaches (with Matthews Correlation Coefficients from 0.40 to 0.87 on truly external test sets, when extrapolation beyond the applicability domain was not evident and sufficient quantities of data were available for training) are competitive with those currently proposed in the literature. 2. To highlight key issues associated with building and assessing truly predictive models, in particular the considerable variation in model performance when training and testing on different datasets.

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“…The ligand-based approach has been adopted to extract the features of hERG blockers in terms of inhibition of hERG current [9][10][11][12][13]. These methods include traditional two-dimensional (2D)-quantitative structure activity relationships (QSAR) [11,[14][15][16], 3D-QSAR [17][18][19][20][21][22], and classifi cation [23][24][25][26] methods. These methods extract chemical properties such as steric, hydrophobic, and electronic properties from the compounds.…”

Section: Ligand-based Prediction System For the Blockade Of Herg Currentmentioning

confidence: 99%

“…Or a combinatorial method with a classification [23,24] based on a decision tree algorithm, which divides blockers into a hierarchy of homogeneous subgroups using the descriptors, can be used. By fi tting the compounds to subgroup-specifi c pharmacophore models, the predicted activities of compounds have been reported to become closer to their actual activities [9,16,46].…”

Section: Issues and Resolutionsmentioning

confidence: 99%

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

et al. 2008

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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...

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In Silico Classification of hERG Channel Blockers: a Knowledge‐Based Strategy

Cited by 49 publications

References 24 publications

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

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

Development and Comparison of hERG Blocker Classifiers: Assessment on Different Datasets Yields Markedly Different Results

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

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