Tuning hERG Out: Antitarget QSAR Models for Drug Development

TLDR

Several non‑cardiovascular drugs have been withdrawn because they inhibit hERG K⁺ channels, which can cause severe arrhythmia and death, and FDA‑required safety testing has spurred interest in computational tools to identify potential blockers early in drug discovery. The study aimed to develop predictive QSAR models for hERG blockage using the largest publicly available dataset of 11,958 ChEMBL compounds. Models were constructed and validated according to OECD guidelines with four descriptor types and four machine‑learning methods, and then applied to screen the World Drug Index for potential hERG blockers and non‑blockers. The models achieved 0.83–0.93 classification accuracy on external data, revealed SAR rules for converting blockers to non‑blockers, reliably identified blockers/non‑blockers in marketed drugs, and are available via a free web server. Keywords: Antitarget, drug development, hERG, QSAR modeling, virtual screening.

Abstract

Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death.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 FDArequired 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 wellcharacterized 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). Keywords: Antitarget, drug development, hERG, QSAR modeling, virtual screening.