Tuning HERG out: antitarget QSAR models for drug development.

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:Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death.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 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).

journal_name

Curr Top Med Chem

authors

Braga RC,Alves VM,Silva MF,Muratov E,Fourches D,Tropsha A,Andrade CH

doi

10.2174/1568026614666140506124442

subject

Has Abstract

pub_date

2014-01-01 00:00:00

pages

1399-415

issue

11

eissn

1568-0266

issn

1873-4294

pii

CTMC-EPUB-60387

journal_volume

14

pub_type

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