A computational model predicts adjunctive pharmacotherapy for cardiac safety via selective inhibition of the late cardiac Na current
Bibliographic record
Abstract
Background The QT interval is a phase of the cardiac cycle that corresponds to action potential duration (APD) including cellular repolarization (T-wave). In both clinical and experimental settings, prolongation of the QT interval of the electrocardiogram (ECG) and related proarrhythmia have been so strongly associated that a prolonged QT interval is largely accepted as surrogate marker for proarrhythmia. Accordingly, drugs that prolong the QT interval are not considered for further preclinical development resulting in removal of many promising drugs from development. While reduction of drug interactions with hERG is an important goal, there are promising means to mitigate hERG block. Here, we examine one possibility and test the hypothesis that selective inhibition of the cardiac late Na current (I NaL ) by the novel compound GS-458967 can suppress proarrhythmic markers. Methods and results New experimental data has been used to calibrate I NaL in the Soltis-Saucerman computationally based model of the rabbit ventricular action potential to study effects of GS-458967 on I NaL during the rabbit ventricular AP. We have also carried out systematic in silico tests to determine if targeted block of I NaL would suppress proarrhythmia markers in ventricular myocytes described by TRIaD : T riangulation , R everse use dependence, beat-to-beat I nstability of action potential duration, and temporal and spatial action potential duration D ispersion . Conclusions Our computer modeling approach based on experimental data, yields results that suggest that selective inhibition of I NaL modifies all TRIaD related parameters arising from acquired Long-QT Syndrome, and thereby reduced arrhythmia risk. This study reveals the potential for adjunctive pharmacotherapy via targeted block of I NaL to mitigate proarrhythmia risk for drugs with significant but unintended off-target hERG blocking effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".