In silico assessment of drug safety in human heart applied to late sodium current blockers
Bibliographic record
Abstract
Drug-induced action potential (AP) prolongation leading to Torsade de Pointes is a major concern for the development of anti-arrhythmic drugs. Nevertheless the development of improved anti-arrhythmic agents, some of which may block different channels, remains an important opportunity. Partial block of the late sodium current (I(NaL)) has emerged as a novel anti-arrhythmic mechanism. It can be effective in the settings of free radical challenge or hypoxia. In addition, this approach can attenuate pro-arrhythmic effects of blocking the rapid delayed rectifying K(+) current (I(Kr)). The main goal of our computational work was to develop an in-silico tool for preclinical anti-arrhythmic drug safety assessment, by illustrating the impact of I(Kr)/I(NaL) ratio of steady-state block of drug candidates on "torsadogenic" biomarkers. The O'Hara et al. AP model for human ventricular myocytes was used. Biomarkers for arrhythmic risk, i.e., AP duration, triangulation, reverse rate-dependence, transmural dispersion of repolarization and electrocardiogram QT intervals, were calculated using single myocyte and one-dimensional strand simulations. Predetermined amounts of block of I(NaL) and I(Kr) were evaluated. "Safety plots" were developed to illustrate the value of the specific biomarker for selected combinations of IC(50)s for I(Kr) and I(NaL) of potential drugs. The reference biomarkers at baseline changed depending on the "drug" specificity for these two ion channel targets. Ranolazine and GS967 (a novel potent inhibitor of I(NaL)) yielded a biomarker data set that is considered safe by standard regulatory criteria. This novel in-silico approach is useful for evaluating pro-arrhythmic potential of drugs and drug candidates in the human ventricle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".