Characterization of HERG potassium channel inhibition using CoMSiA 3D QSAR and homology modeling approaches

Pearlstein, Robert A.; Vaz, Roy J.; Kang, Jiesheng; Chen, Xiaoliang; Preobrazhenskaya, M. N.; Shchekotikhin, Andrey E.; Королев, А. М.; Lysenkova, Lyudmila N.; Miroshnikova, Olga V; Hendrix, James; Rampe, David

doi:10.1016/s0960-894x(03)00196-3

Cited by 219 publications

(227 citation statements)

References 21 publications

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“…Activity data were expressed as an IC 50 value, measured by either patch clamp or radioligand binding assays, which were conducted in human HEK-293 and CHO cell lines. Although there were some differences in the pIC 50 values, these two assay results showed a consistent trend. Moreover, because we choose to build qualitative (classification) models, the quantitative variability in different assays can be considerably reduced by setting appropriate activity thresholds for separating blockers from nonblockers.…”

Section: Datasetsmentioning

confidence: 55%

“…Previous studies have revealed that most hERG inhibitors have two main characteristics: hydrophobic groups forming π-π stacking and protonated groups forming cation-π interaction with hERG channel residues [50,51] . It has also been suggested that, with increasing hydrophobicity and molecular diameter, the hERG inhibitory effect of a compound increases [9,37] .…”

Section: Molecular Features Important For Hergmentioning

confidence: 99%

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Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

Lü

Yin

et al. 2014

Acta Pharmacol Sin

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Aim: A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels. Methods: Doddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation. Results: A Bayesian classification model considering the effects of four molecular properties (M w , PPSA, ALogP and pK a _basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation. Conclusion: The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.

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Section: Datasetsmentioning

confidence: 55%

Section: Molecular Features Important For Hergmentioning

confidence: 99%

Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

Lü

Yin

et al. 2014

Acta Pharmacol Sin

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“…Prolonged QTc interval at supra-therapeutic doses can be explained by the blockade of the cardiac human ether-a-go-go (hERG) potassium channel. These channels are major regulators of the rapid delayed rectifier current (IKr) which conducts potassium (K+) out of the cardiac myocytes [11,12]. Defective function of the channels affects ventricular repolarization and leads to a prolonged relative refractory period of the cardiac myocytes [11].…”

Section: Discussionmentioning

confidence: 99%

“…These channels are major regulators of the rapid delayed rectifier current (IKr) which conducts potassium (K+) out of the cardiac myocytes [11,12]. Defective function of the channels affects ventricular repolarization and leads to a prolonged relative refractory period of the cardiac myocytes [11]. Cells become vulnerable to extrasystole with potential TdP and ventricular arrhythmia that is manifested by prolonged QT and QTc intervals on the surface EKG.…”

Section: Discussionmentioning

confidence: 99%

Ventricular fibrillation due to overdose of loperamide, the “poor man’s methadone”

Salama

Levin

Jha

et al. 2017

Journal of Community Hospital Internal Medicine Perspectives

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Loperamide is an over-the-counter antidiarrheal agent that is considered by many patients to be safe, but has been used as a drug of abuse due to its opioid properties. However, cardiotoxicity has been reported, prompting the FDA to release a warning regarding the arrhythmogenic potential of loperamide. We present a case of a 38-year-old female presenting with cardiac arrest thought to be secondary to abuse of the loperamide that she was using to alleviate the heroin withdrawal symptoms. Cardiac ischemia and other drug toxicities were ruled out. Loperamide induces QTc prolongation and cardiac dysrhythmias. She had recurrent ventricular arrhythmias with multiple cardiac arrests. The persistence of the cardiotoxicity for a longer duration than previously reported in the literature is unique in this clinical presentation. We also highlight the potential mechanisms for loperamide cardiotoxicity and its challenging management. Abbreviations: ACLS: Advanced cardiac life support; GI: Gastrointestinal

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“…although "local" models have also been generated around narrow structural series (24). These ligand-based computational models, along with a growing number of homology models (36,37), have provided insights that complement experimental studies such as site-directed mutagenesis (38,39).…”

Section: Introductionmentioning

confidence: 99%

Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

Chekmarev

Kholodovych

Balakin

et al. 2008

Chem. Res. Toxicol.

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Shape Signatures is a new computational tool that is being evaluated for applications in computational toxicology and drug discovery. The method employs a customized ray-tracing algorithm to explore the volume enclosed by the surface of a molecule and then uses the output to construct compact histograms (i.e., signatures) that encode for molecular shape and polarity. In the present study, we extend the application of the Shape Signatures methodology to the domain of computational models for cardiotoxicity. The Shape Signatures method is used to generate molecular descriptors that are then utilized with widely used classification techniques such as k nearest neighbors (k-NN), support vector machines (SVM), and Kohonen self-organizing maps (SOM). The performances of these approaches were assessed by applying them to a data set of compounds with varying affinity toward the 5-HT 2B receptor as well as a set of human ether-a-go-go-related gene (hERG) potassium channel inhibitors. Our classification models for 5-HT 2B represented the first attempt at global computational models for this receptor and exhibited average accuracies in the range of 73−83%. This level of performance is comparable to using commercially available molecular descriptors. The overall accuracy of the hERG Shape Signatures-SVM models was 69−73%, in line with other computational models published to date. Our data indicate that Shape Signatures descriptors can be used with SVM and Kohonen SOM and perform better in classification problems related to the analysis of highly clustered and heterogeneous property spaces. Such models may have utility for predicting the potential for cardiotoxicity in drug discovery mediated by the 5-HT 2B receptor and hERG.

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Characterization of HERG potassium channel inhibition using CoMSiA 3D QSAR and homology modeling approaches

Cited by 219 publications

References 21 publications

Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

Ventricular fibrillation due to overdose of loperamide, the “poor man’s methadone”

Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

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