Populations of in silico myocytes and tissues reveal synergy of multiatrial‐predominant K<sup>+</sup>‐current block in atrial fibrillation
Bibliographic record
Abstract
Background and Purpose Pharmacotherapy of atrial fibrillation (AF), the most common cardiac arrhythmia, remains unsatisfactory due to low efficacy and safety concerns. New therapeutic strategies target atrial‐predominant ion‐channels and involve multichannel block (poly)therapy. As AF is characterized by rapid and irregular atrial activations, compounds displaying potent antiarrhythmic effects at fast and minimal effects at slow rates are desirable. We present a novel systems pharmacology framework to quantitatively evaluate synergistic anti‐AF effects of combined block of multiple atrial‐predominant K+ currents (ultra‐rapid delayed rectifier K+ current, IKur, small conductance Ca2+‐activated K+ current, IKCa, K2P3.1 2‐pore‐domain K+ current, IK2P) in AF. Experimental Approach We constructed experimentally calibrated populations of virtual atrial myocyte models in normal sinus rhythm and AF‐remodelled conditions using two distinct, well‐established atrial models. Sensitivity analyses on our atrial populations was used to investigate the rate dependence of action potential duration (APD) changes due to blocking IKur, IK2P or IKCa and interactions caused by blocking of these currents in modulating APD. Block was simulated in both single myocytes and one‐dimensional tissue strands to confirm insights from the sensitivity analyses and examine anti‐arrhythmic effects of multi‐atrial‐predominant K+ current block in single cells and coupled tissue. Key Results In both virtual atrial myocytes and tissues, multiple atrial‐predominant K+‐current block promoted favourable positive rate‐dependent APD prolongation and displayed positive rate‐dependent synergy, that is, increasing synergistic antiarrhythmic effects at fast pacing versus slow rates. Conclusion and Implications Simultaneous block of multiple atrial‐predominant K+ currents may be a valuable antiarrhythmic pharmacotherapeutic strategy for AF.
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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 | 0.001 |
| 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".