Non-β-blocking <i>R</i>-carvedilol enantiomer suppresses Ca2+ waves and stress-induced ventricular tachyarrhythmia without lowering heart rate or blood pressure
Bibliographic record
Abstract
Carvedilol is the current β-blocker of choice for suppressing ventricular tachyarrhythmia (VT). However, carvedilol's benefits are dose-limited, attributable to its potent β-blocking activity that can lead to bradycardia and hypotension. The clinically used carvedilol is a racemic mixture of β-blocking S-carvedilol and non-β-blocking R-carvedilol. We recently reported that novel non-β-blocking carvedilol analogues are effective in suppressing arrhythmogenic Ca(2+) waves and stress-induced VT without causing bradycardia. Thus, the non-β-blocking R-carvedilol enantiomer may also possess this favourable anti-arrhythmic property. To test this possibility, we synthesized R-carvedilol and assessed its effect on Ca(2+) release and VT. Like racemic carvedilol, R-carvedilol directly reduces the open duration of the cardiac ryanodine receptor (RyR2), suppresses spontaneous Ca(2+) oscillations in human embryonic kidney (HEK) 293 cells, Ca(2+) waves in cardiomyocytes in intact hearts and stress-induced VT in mice harbouring a catecholaminergic polymorphic ventricular tachycardia (CPVT)-causing RyR2 mutation. Importantly, R-carvedilol did not significantly alter heart rate or blood pressure. Therefore, the non-β-blocking R-carvedilol enantiomer represents a very promising prophylactic treatment for Ca(2+)- triggered arrhythmia without the bradycardia and hypotension often associated with racemic carvedilol. Systematic clinical assessments of R-carvedilol as a new anti-arrhythmic agent may be warranted.
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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".