Abstract
BackgroundDrug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates.ResultIn this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models.ConclusionThe neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.
Highlights
Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene
The model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity
A representative mechanism of cardiotoxicity involves the binding of compounds to the cardiac potassium channel encoded by the human ether-a-go-go-related gene, which results in long QT syndrome (LQTS) and eventually leads to fatal ventricular arrhythmias and sudden death [1, 2]
Summary
Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). A representative mechanism of cardiotoxicity involves the binding of compounds to the cardiac potassium channel encoded by the human ether-a-go-go-related gene (hERG), which results in long QT syndrome (LQTS) and eventually leads to fatal ventricular arrhythmias and sudden death [1, 2]. Many drugs, such as terfenadine, cisapride, astemizole, Experimental high-throughput screening methods have been developed [4], but experimental methods for drug-induced cardiotoxicity are time-consuming and costly. Several ligand-based in silico models have been developed to predict drug-hERG interactions based on the pharmacophore, quantitative structure-activity relationship (QSAR), and classification models [5,6,7,8]
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