Structural analysis of hERG channel blockers and the implications for drug design

AlRawashdah, Sara; Chandrasekaran, Suryanarayanan; Barakat, Khaled

doi:10.1016/j.jmgm.2023.108405

Cited by 2 publications

(4 citation statements)

References 108 publications

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“…It is crucial to recognize that there are several potential sources of bias and inconsistencies that could impact the ultimate curated database and, hence, the outcomes of the developed models. These sources serve as a constraining factor for any models developed around cardiac ion channels . This encompasses disparities in the experimental assays employed for data collection. , For instance, Guo et al demonstrated that limitations in the same patch clamp instrument can yield varying measurements for the same compound and to attain stable drug concentrations, repeated compound additions proved to be critical.…”

Section: Methodsmentioning

confidence: 99%

“…These sources serve as a constraining factor for any models developed around cardiac ion channels . This encompasses disparities in the experimental assays employed for data collection. , For instance, Guo et al demonstrated that limitations in the same patch clamp instrument can yield varying measurements for the same compound and to attain stable drug concentrations, repeated compound additions proved to be critical. Moreover, discrepancies can arise when using standard patch-clamp techniques compared to automated instruments, resulting in 3-fold shifts in IC50 values for highly hydrophobic compounds .…”

Section: Methodsmentioning

confidence: 99%

“…40 This encompasses disparities in the experimental assays employed for data collection. 40,41 For instance, Guo et al 42 demonstrated that limitations in the same patch clamp instrument can yield varying measurements for the same compound and to attain stable drug concentrations, repeated compound additions proved to be critical. Moreover, discrepancies can arise when using standard patch-clamp techniques compared to automated instruments, resulting in 3-fold shifts in IC50 values for highly hydrophobic compounds.…”

Section: Compilation Of a Cardiotoxicity Databasementioning

confidence: 99%

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Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

Arab,

Egghe,

Laukens

et al. 2023

J. Chem. Inf. Model.

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In the field of drug discovery, there is a substantial challenge in seeking out chemical structures that possess desirable pharmacological, toxicological, and pharmacokinetic properties. Complications arise when drugs interfere with the functioning of cardiac ion channels, leading to serious cardiovascular consequences. The discontinuation and removal of numerous approved drugs from the market or at late development stages in the pipeline due to such inhibitory effects further highlight the urgency of addressing this issue. Consequently, the early prediction of potential blockers targeting cardiac ion channels during the drug discovery process is of paramount importance. This study introduces a deep learning framework that computationally determines the cardiotoxicity associated with the voltage-gated potassium channel (hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated sodium channel (Nav1.5) for drug candidates. The predictive capabilities of three feature representationsmolecular fingerprints, descriptors, and graph-based numerical representationsare rigorously benchmarked. Additionally, a novel training and evaluation data set framework is presented, enabling predictive model training of drug off-target cardiotoxicity using a comprehensive and large curated data set covering these three cardiac ion channels. To facilitate these predictions, a robust and comprehensive small molecule cardiotoxicity prediction tool named CToxPred has been developed. It is made available as open source under the permissive MIT license at .

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Compilation Of a Cardiotoxicity Databasementioning

confidence: 99%

See 1 more Smart Citation

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

Arab,

Egghe,

Laukens

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…In silico strategies, such as machine learning (ML)-based classifiers and structure-based modeling, offer valuable and reliable complements to experimental approaches in addressing the issue of hERG-associated cardiac toxicity. , These computational methods leverage large data sets and advanced algorithms to analyze the structural and functional properties of hERG channels and their interactions with potential blockers. By training machine learning models on experimental data, researchers can accurately predict the hERG-blocking potential of novel compounds.…”

Section: Introductionmentioning

confidence: 99%

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

Haddad,

Oktay,

Erol

et al. 2023

ACS Omega

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The human ether-a-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.

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Structural analysis of hERG channel blockers and the implications for drug design

Cited by 2 publications

References 108 publications

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

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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