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In silico prediction of hERG inhibition
Yankang Jing1, Alison Easter, David Peters
1Biogen Idec, 250 Binney Street, Cambridge, MA 02142, USA.
Insights
hERG channel screening is vital for drug safety, as blocking this channel prolongs cardiac action potentials and can cause sudden death. Computational models offer a fast, cost-effective method for early drug discovery screening.
Area of Science:
- Cardiovascular Pharmacology
- Computational Chemistry
- Drug Discovery
Background:
- The hERG channel is crucial for cardiac repolarization, controlling the delayed rectifying potassium current (IKr).
- Blockade of the hERG channel prolongs the QT interval, increasing the risk of fatal arrhythmias and leading to drug withdrawal.
- Early screening for hERG channel interactions is essential for drug development safety.
Purpose of the Study:
- To review and classify computational in silico prediction models for hERG channel activity.
- To highlight the importance of hERG screening in early drug discovery to prevent adverse cardiac events.
Main Methods:
- Classification of hERG prediction models into distinct categories.
- Discussion of quantitative structure-activity relationship (QSAR) models (2D and 3D).
- Inclusion of pharmacophore, classification, and structure-based (homology modeling) approaches.
Main Results:
- Computational models provide a rapid and economical approach for screening drug candidates.
- Various in silico models exist for predicting hERG channel interactions, aiding in risk assessment.
Conclusions:
- In silico hERG screening is a valuable tool in early drug discovery, mitigating risks associated with cardiotoxicity.
- The reviewed models (2D/3D QSAR, pharmacophore, classification, structure-based) offer diverse strategies for predicting hERG liability.
Abstract:
The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure-activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).
