A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics...

Area of Science:

• Pharmacology
• Computational Biology
• Cardiology

Background:

• Acquired cardiac long QT syndrome (LQTS) is a significant drug toxicity, often caused by blocking the human ether-á-go-go-related (hERG) K+ channel.
• This channel blockade has led to drug recalls and clinical trial terminations, highlighting the need for predictive tools.

Purpose of the Study:

• To develop and validate a sensitive computational atomistic model for predicting hERG channel blockage.
• To enhance the safety and efficiency of drug development by identifying potential hERG blockers early.

Main Methods:

• Utilized long molecular dynamics simulations to create a computational atomistic model.
• Tested the model on 18 compounds, assessing its sensitivity and specificity in predicting hERG blocking activity.
• Experimentally validated predictions using hERG binding assays and patch clamp electrophysiology.

Main Results:

• The computational model demonstrated high sensitivity and specificity in identifying hERG blockers among 18 test compounds.
• The model successfully discriminated between potent, weak, and non-hERG blockers.
• Experimental validation confirmed the model's predictions, including identifying a recently halted drug as a potent hERG inhibitor.

Conclusions:

• The developed computational model is a powerful and accurate tool for predicting hERG channel blockage.
• This model can significantly improve drug safety and streamline the drug development process.
• Early prediction of hERG activity can prevent costly drug failures and mitigate cardiotoxicity risks.