October 16, 2023ACS OmegaOpen Access

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

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Why the study?

Disruptions in hERG channel function cause drug-induced long QT syndrome and sudden cardiac death, requiring identification of crucial ligand heteroatoms initiating hERG channel blocking.

Structured PICO

Population

Extensive ligand libraries with associated hERG channel blockage pIC50 values

Intervention

Machine learning-assisted QSAR models (KPLS method) based on heteroatom types and numbers

Outcome

Prediction of hERG channel blockage (pIC50 values) and identification of crucial heteroatomssurrogate

Machine learning-assisted QSAR models identified the crucial role of heteroatom types and numbers in predicting hERG channel blockage, which may aid in assessing the risk of drug-induced long QT syndrome.

Implication

Novel heteroatom model may enhance hERG liability prediction; hypothesis-generating until prospectively validated in drug development.

Abstract

High Resolution Image Download MS PowerPoint Slide The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure–activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC 50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.