Stereoselective Blockage of Quinidine and Quinine in the hERG Channel and the Effect of Their Rescue Potency on Drug-Induced hERG Trafficking Defect
Bibliographic record
Abstract
-related gene) potassium channel which is essential for myocardium repolarization. Abnormality of repolarization increases risk of arrhythmia. The aim of our research is to study and compare the impacts of quinidine and quinine on hERG. Results show that both drugs block the hERG channel, with quinine 14-fold less potent than quinidine. In addition, they presented distinct impacts on channel dynamics. The results imply their stereospecific block effect on the hERG channel. However, F656C-hERG reversed this stereoselectivity. The mutation decreases affinity of the two drugs with hERG, and quinine was more potent than quinidine in F656C-hERG blockage. These data suggest that F656 residue contributes to the stereoselective pocket for quinidine and quinine. Further study demonstrates that both drugs do not change hERG protein levels. In rescue experiments, we found that they exert no reverse effect on pentamidine- or desipramine-induced hERG trafficking defect, although quinidine has been reported to rescue trafficking-deficient pore mutation hERG G601S based on the interaction with F656. Our research demonstrated stereoselective effects of quinidine and quinine on the hERG channel, and this is the first study to explore their reversal potency on drug-induced hERG deficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".