Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models...

Area of Science:

• Cardiovascular pharmacology
• Medicinal chemistry
• Computational toxicology

Background:

• The human ether-à-go-go-related gene (hERG) channel is vital for cardiac repolarization.
• Dysfunctional hERG channels can cause long QT syndrome (LQTS), leading to arrhythmias and sudden cardiac death.
• Drug-induced hERG channel blockade is a significant safety concern, necessitating careful drug design.

Purpose of the Study:

• To identify critical heteroatoms in ligands responsible for effective hERG channel blockade.
• To establish quantitative structure-activity relationship (QSAR) models for predicting hERG channel inhibitory activity.
• To elucidate the specific roles of heteroatom types and numbers in hERG channel interactions.

Main Methods:

• Development of ligand-based quantitative structure-activity relationship (QSAR) models using extensive ligand libraries.
• Application of machine learning techniques, specifically K-nearest partial least squares (KPLS), with eight distinct fingerprints.
• Utilizing molecular docking, molecular dynamics simulations, and MM/GBSA calculations for detailed interaction analysis.

Main Results:

• QSAR models effectively identified key structural features, particularly heteroatoms, influencing hERG channel blockage.
• The KPLS method demonstrated high efficiency in modeling hERG channel activity based on ligand fingerprints.
• Analysis revealed the significant impact of heteroatom types and numbers on the potency of hERG blockers.

Conclusions:

• Heteroatoms play a pivotal role in the mechanism of hERG channel blockade by drug ligands.
• This research provides a quantitative understanding of heteroatom contributions to hERG channel activity.
• The findings can guide the design of safer drugs with reduced risk of hERG-mediated cardiovascular adverse effects.