Inhibition of hERG Channel Trafficking: An Under-Explored Mechanism for Drug-Induced QT Prolongation

ChemMedChem ◽  
2008 ◽  
Vol 3 (10) ◽  
pp. 1501-1502 ◽  
Author(s):  
Kap-Sun Yeung ◽  
Nicholas A. Meanwell
Keyword(s):  
Qt Prolongation ◽  
Herg Channel ◽  
Drug Induced ◽  
Channel Trafficking

scholarly journals High Glucose Represses hERG K+ Channel Expression through Trafficking Inhibition

2015 ◽  
Vol 37 (1) ◽  
pp. 284-296 ◽  
Author(s):  
Yuan-Qi Shi ◽  
Meng Yan ◽  
Li-Rong Liu ◽  
Xiao Zhang ◽  
Xue Wang ◽  
...  
Keyword(s):  
Western Blot ◽  
High Glucose ◽  
Qt Prolongation ◽  
Herg Channel ◽  
K Channel ◽  
Delayed Rectifier ◽  
Patch Clamp Technique ◽  
Down Regulation ◽  
Channel Trafficking ◽  
Herg Current

Background/Aims: Abnormal QT prolongation is the most prominent cardiac electrical disturbance in patients with diabetes mellitus (DM). It is well known that the human ether-ago-go-related gene (hERG) controls the rapid delayed rectifier K+ current (IKr) in cardiac cells. The expression of the hERG channel is severely down-regulated in diabetic hearts, and this down-regulation is a critical contributor to the slowing of repolarization and QT prolongation. However, the intracellular mechanisms underlying the diabetes-induced hERG deficiency remain unknown. Methods: The expression of the hERG channel was assessed via western blot analysis, and the hERG current was detected with a patch-clamp technique. Results: The results of our study revealed that the expression of the hERG protein and the hERG current were substantially decreased in high-glucose-treated hERG-HEK cells. Moreover, we demonstrated that the high-glucose-mediated damage to the hERG channel depended on the down-regulation of protein levels but not the alteration of channel kinetics. These discoveries indicated that high glucose likely disrupted hERG channel trafficking. From the western blot and immunoprecipitation analyses, we found that high glucose induced trafficking inhibition through an effect on the expression of Hsp90 and its interaction with hERG. Furthermore, the high-glucose-induced inhibition of hERG channel trafficking could activate the unfolded protein response (UPR) by up-regulating the expression levels of activating transcription factor-6 (ATF-6) and the ER chaperone protein calnexin. In addition, we demonstrated that 100 nM insulin up-regulated the expression of the hERG channel and rescued the hERG channel repression caused by high glucose. Conclusion: The results of our study provide the first evidence of a high-glucose-induced hERG channel deficiency resulting from the inhibition of channel trafficking. Furthermore, insulin promotes the expression of the hERG channel and ameliorates the high-glucose-induced inhibition of the hERG channel.

scholarly journals An explainable algorithm for detecting drug-induced QT-prolongation at risk of torsades de pointes (TdP) regardless of heart rate and T-wave morphology

2021 ◽  
Vol 131 ◽  
pp. 104281
Author(s):  
Alaa Alahmadi ◽  
Alan Davies ◽  
Jennifer Royle ◽  
Leanna Goodwin ◽  
Katharine Cresswell ◽  
...  
Keyword(s):  
At Risk ◽  
Heart Rate ◽  
Torsades De Pointes ◽  
Qt Prolongation ◽  
Drug Induced ◽  
T Wave ◽  
Wave Morphology

Drug-induced QT Prolongation Atlas (DIQTA) for enhancing cardiotoxicity management

2021 ◽  
Author(s):  
Shihai Li ◽  
Zili Xu ◽  
Mingkun Guo ◽  
Menglong Li ◽  
Zhining Wen
Keyword(s):  
Qt Prolongation ◽  
Drug Induced

Abstract 15056: Machine Learning Prediction of Long QT Syndrome

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Steven Simon ◽  
Divneet Mandair ◽  
Michael A Rosenberg ◽  
Premanand Tiwar
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Improve Patient Safety ◽  
Accurate Method ◽  
Qt Prolongation ◽  
Common Data Model ◽  
Qtc Interval ◽  
Drug Induced ◽  
Sensitivity Specificity ◽  
Physician Order Entry

Introduction: Drug-induced QT prolongation causes significant morbidity and mortality but could be preventable with prediction of susceptible individuals. Machine learning (ML) algorithms applied to electronic health record (EHR) data may provide a method for identifying these individuals and could be automated to directly alert providers of risk. This study applies ML techniques to EHR data to identify an integrated model that can be deployed to predict risk of drug-induced QT prolongation. Methods: We examined data from the UCHealth EHR that has been harmonized to the Observational Medical Outcomes Partnership common data model and identified inpatients who had received a medication known to prolong the QT interval and had an electrocardiogram (ECG) performed within 24 hours. We used a binary outcome of the development of a QTc interval > 500 ms by ECG within 24 hours of medication initiation or no ECG with a QTc interval > 500 ms throughout the encounter. We compared multiple machine learning methods (logistic regression, random forest, naïve Bayes, and deep neural network (DNN)) by classification accuracy as assessed by AUC and F 1 score. We performed calibration and scaling of the final model. Results: We identified 35639 inpatients who received a known QT-prolonging medication and had an ECG within 24 hours. Of those, 4558 patients developed a QTc > 500 ms and 31081 patients did not. The DNN model provided reasonable classification accuracy (F1 score 0.404; AUC 0.71) and was the most accurate method tested. A range of decision cutpoints were plotted with respective sensitivity/specificity (Figure 1). Conclusions: We found that by applying a DNN to EHR data, we could reasonably predict individuals susceptible to drug-induced QT prolongation. Varying cutpoints can be used to tailor the model to the desired sensitivity. Future studies are needed to validate this model in novel EHRs and within the physician order entry system to assess ability to improve patient safety.

scholarly journals Drug-induced QT prolongation in COVID-19 pneumonia: influence on in-hospital survival

2021 ◽  
Vol 74 (1) ◽  
pp. 111-112
Author(s):  
Daniel García-Rodríguez ◽  
Paloma Remior ◽  
Eusebio García-Izquierdo ◽  
Jorge Toquero ◽  
Víctor Castro ◽  
...  
Keyword(s):  
Qt Prolongation ◽  
Drug Induced ◽  
Hospital Survival

scholarly journals Managing drug-induced QT prolongation in clinical practice

2020 ◽  
pp. postgradmedj-2020-138661
Author(s):  
Rani Khatib ◽  
Fatima R N Sabir ◽  
Caroline Omari ◽  
Chris Pepper ◽  
Muzahir Hassan Tayebjee
Keyword(s):  
Qt Interval ◽  
Simple Algorithm ◽  
Torsades De Pointes ◽  
Daily Practice ◽  
Qt Prolongation ◽  
Key Factors ◽  
Drug Induced ◽  
Electrolyte Disturbances ◽  
Related Risk ◽  
Life Threatening

Many drug therapies are associated with prolongation of the QT interval. This may increase the risk of Torsades de Pointes (TdP), a potentially life-threatening cardiac arrhythmia. As the QT interval varies with a change in heart rate, various formulae can adjust for this, producing a ‘corrected QT’ (QTc) value. Normal QTc intervals are typically <450 ms for men and <460 ms for women. For every 10 ms increase, there is a ~5% increase in the risk of arrhythmic events. When prescribing drugs associated with QT prolongation, three key factors should be considered: patient-related risk factors (eg, female sex, age >65 years, uncorrected electrolyte disturbances); the potential risk and degree of QT prolongation associated with the proposed drug; and co-prescribed medicines that could increase the risk of QT prolongation. To support clinicians, who are likely to prescribe such medicines in their daily practice, we developed a simple algorithm to help guide clinical management in patients who are at risk of QT prolongation/TdP, those exposed to QT-prolonging medication or have QT prolongation.

Sign in / Sign up

Export Citation Format

Share Document