Key result
In silico models predict hERG channel blockers to reduce cardiotoxicity risk in drug discovery.
Why the study?
Evaluating hERG liability is mandatory for drug safety to avoid QT prolongation and fatal arrhythmias, but standard experimental assays are complex, expensive, and time-consuming.
Answers are grounded in this paper and cite their sources.
This review highlights the utility of computational methods in predicting hERG channel liability to reduce cardiotoxicity risks during early drug discovery.
Inhibition of the human ether‐à‐go‐go‐related gene (hERG) ion channel may lead to prolonged QT interval and even fatal ventricular arrhythmia. Therefore, evaluating the hERG liability has become a mandatory requirement for drug development and drug safety process. However, standard experimental assays are complex, expensive and time‐consuming. Various in silico tools have been developed to identify potential hERG blockers and reduce in advance the risk of cardiotoxicity‐related attritions during the early drug discovery phase. In this review, we first outlined the structure and gating properties of the hERG channel briefly. Then analyzed the advantages of computational technology, and comprehensively summarized the in silico methods of hERG‐related cardiotoxicity, including ligand‐based and structure‐based methods. Additionally, the current predictive models in prediction of hERG blockage were emphatically reviewed. Finally, issues involved in the current computational methods, as well as the future direction for developing reliable models of hERG channel blockers, are discussed.
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Shan et al. (2022) conducted a review in hERG channel blockage. Computational methods (in silico tools) was evaluated. In silico computational methods, including ligand-based and structure-based models, provide advantageous tools for predicting hERG channel blockers and reducing cardiotoxicity risk in drug discovery.
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