<i>In Silico</i>
            Prediction of hERG Inhibition

Jing, Yankang; Easter, Alison; Peters, David M.; Kim, Norman; Enyedy, Istvan

doi:10.4155/fmc.15.18

Cited by 58 publications

(37 citation statements)

References 98 publications

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“…[10, 35] The training sets vary from tens to a few thousands of compounds, where large datasets are either compilation of literature data, such as ChEMBL, [13b, 36] or corporate datasets off the public domain. [8, 37] In this study, we presented a 3,024 non-redundant hERG dataset, consisting of the marketed drugs, drug candidates reaching the clinical trials, and other bioactive compounds.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing

Sun

Huang

Xia

et al. 2016

Molecular Informatics

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Drug-induced QT prolongation leads to life-threatening cardiotoxicity, mostly through blockage of the human ether-a-go-go-related gene (hERG) encoded potassium ion (K+) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under-sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC-ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities.

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Section: Resultsmentioning

confidence: 99%

“…[10] Compounds with weak hERG activity at µM range should be flagged for their potential to trigger cardiotoxicity, especially when accidentally over-dosed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing

Sun

Huang

Xia

et al. 2016

Molecular Informatics

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“…48 The drug safety profiler includes the prediction of Cytochrome P450 (CYP) regioselectivity (CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2), probability of producing positive Ames (mutagenicity prediction), and human ether-a-go-go-related gene (hERG) (cardiotoxicity prediction). 49,50 The accuracy and sensitivity of all the above methods were successfully evaluated by the supplier, ACD/Labs. All the data sheets are available on the company website (http:// www.acdlabs.com).…”

Section: Prediction Of Drug-like Propertiesmentioning

confidence: 99%

Synergistic antibacterial effects of herbal extracts and antibiotics on methicillin-resistant Staphylococcus aureus: A computational and experimental study

Kuok

Hoi

et al. 2017

Exp Biol Med (Maywood)

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Antibiotic resistant is a well-known threat to global health and methicillin-resistant Staphylococcus aureus is one of the most significant ones. These resistant bacteria kill thousands of people every year and therefore a new effective antimicrobial treatment is necessary. This study identified the herbs and their associated bioactive ingredients that can potential the effects of current antibiotics. These herbs have long history of human usage in China and have well-defined monograph in the Chinese Pharmacopeia. These indicate their relatively high clinical safety and may have a quicker drug development process than that of a new novel antibiotic. Based on the results of this study, the authors will perform further in vitro and animal studies, aiming to accumulate significant data for the application of clinical trial. AbstractAntibiotic resistance has become a serious global concern, and the discovery of antimicrobial herbal constituents may provide valuable solutions to overcome the problem. In this study, the effects of therapies combining antibiotics and four medicinal herbs on methicillinresistant Staphylococcus aureus (MRSA) were investigated. Specifically, the synergistic effects of Magnolia officinalis, Verbena officinalis, Momordica charantia, and Daphne genkwa in combination with oxacillin or gentamicin against methicillin-resistant (ATCC43300) and methicillin-susceptible (ATCC25923) S. aureus were examined. In vitro susceptibility and synergistic testing were performed to measure the minimum inhibitory concentration and fractional inhibitory concentration (FIC) index of the antibiotics and medicinal herbs against MRSA and methicillin-susceptible S. aureus. To identify the active constituents in producing these synergistic effects, in silico molecular docking was used to investigate the binding affinities of 139 constituents of the four herbs to the two common MRSA inhibitory targets, penicillin binding proteins 2a (PBP2a) and 4 (PBP4). The physicochemical and absorption, distribution, metabolism, and excretion properties and drug safety profiles of these compounds were also analyzed. D. genkwa extract potentiated the antibacterial effects of oxacillin against MRSA, as indicated by an FIC index value of 0.375. M. officinalis and V. officinalis produced partial synergistic effects when combined with oxacillin, whereas M. charantia was found to have no beneficial effects in inhibiting MRSA. Overall, tiliroside, pinoresinol, magnatriol B, and momorcharaside B were predicted to be PBP2a or PBP4 inhibitors with good drug-like properties. This study identifies compounds that deserve further investigation with the aim of developing therapeutic agents to modulate the effect of antibiotics on MRSA.

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“…Great efforts have been applied towards the identification of novel hERG channel blockers and the prediction of cardiac liabilities through computational methods. A comprehensive analysis of this approach is not within the scope of this review (for recent reviews see Jing, Y. et al 29 and Braga, R. C. et al 20 ). In general, these methods can be divided into ligand-based and structure-based approaches; the latter being based on hERG channel homology models.…”

Section: Preclinical Herg Channel-related Cardiotoxicity Assessmentmentioning

confidence: 99%

Natural products modulating the hERG channel: heartaches and hope

Kratz

Grienke

Scheel³

et al. 2017

Nat. Prod. Rep.

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In Silico Prediction of hERG Inhibition

Cited by 58 publications

References 98 publications

Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing

Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing

Synergistic antibacterial effects of herbal extracts and antibiotics on methicillin-resistant Staphylococcus aureus: A computational and experimental study

Natural products modulating the hERG channel: heartaches and hope

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