Atrial-selective K<sup>+</sup> channel blockers: potential antiarrhythmic drugs in atrial fibrillation?
Bibliographic record
Abstract
In the wake of demographic change in Western countries, atrial fibrillation has reached an epidemiological scale, yet current strategies for drug treatment of the arrhythmia lack sufficient efficacy and safety. In search of novel medications, atrial-selective drugs that specifically target atrial over other cardiac functions have been developed. Here, I will address drugs acting on potassium (K+) channels that are either predominantly expressed in atria or possess electrophysiological properties distinct in atria from ventricles. These channels include the ultra-rapidly activating, delayed outward-rectifying Kv1.5 channel conducting IKur, the acetylcholine-activated inward-rectifying Kir3.1/Kir3.4 channel conducting IK,ACh, the Ca2+-activated K+ channels of small conductance (SK) conducting ISK, and the two-pore domain K+ (K2P) channels (tandem of P domains, weak inward-rectifying K+ channels (TWIK-1), TWIK-related acid-sensitive K+ channels (TASK-1 and TASK-3)) that are responsible for voltage-independent background currents ITWIK-1, ITASK-1, and ITASK-3. Direct drug effects on these channels are described and their putative value in treatment of atrial fibrillation is discussed. Although many potential drug targets have emerged in the process of unravelling details of the pathophysiological mechanisms responsible for atrial fibrillation, we do not know whether novel antiarrhythmic drugs will be more successful when modulating many targets or a single specific one. The answer to this riddle can only be solved in a clinical context.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".