Tuning hERG out: Antitarget QSAR Models for Drug Development
By Rodolpho C. Braga, Vinicius M. Alves, Meryck F B Silva, Eugene N. Muratov, Denis Fourches, Alexander Tropsha, Carolina Horta Andrade. 2014
92 citations recorded by the source corpus.
Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).
Subfields: Cardiology and Cardiovascular Medicine, Computational Theory and Mathematics, Molecular Biology
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Citations in the corpus
- Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, methodology): “To illustrate this point, Table 1 shows that RMD can accurately predict binding to human ether-`a-go-go–related gene (hERG), a potassium ion channel that is an “antitarget” for drug discovery, as binding can cause cardiac arrest (35, 36).”
- Compilation and physicochemical classification analysis of a diverse hERG inhibition database (2016, unknown): “[27] using a public data set constructed from ChEMBL data, similar to ours in composition.”