An overview of drug‐induced sodium channel blockade and changes in cardiac conduction: Implications for drug safety
Bibliographic record
Abstract
Abstract The human voltage‐gated sodium channel Nav1.5 (hNav1.5/SCN5A) plays a critical role in the initiation and propagation of action potentials in cardiac myocytes, and its modulation by various drugs has significant implications for cardiac safety. Drug‐dependent block of Nav1.5 current (INa) can lead to significant alterations in cardiac electrophysiology, potentially resulting in conduction slowing and an increased risk of proarrhythmic events. This review aims to provide a comprehensive overview of the mechanisms by which various pharmacological agents interact with Nav1.5, focusing on the molecular determinants of drug binding and the resultant electrophysiological effects. We discuss the structural features of Nav1.5 that influence drug affinity and specificity. Special attention is given to the concept of state‐dependent block, where drug binding is influenced by the conformational state of the channel, and its relevance to therapeutic efficacy and safety. The review also examines the clinical implications of INa block, highlighting case studies of drugs that have been associated with adverse cardiac events, and how the Vaughan‐Williams Classification system has been employed to qualify “unsafe” sodium channel block. Furthermore, we explore the methodologies currently used to assess INa block in nonclinical and clinical settings, with the hope of providing a weight of evidence approach including in silico modeling, in vitro electrophysiological assays and in vivo cardiac safety studies for mitigating proarrhythmic risk early in drug discovery. This review underscores the importance of understanding Nav1.5 pharmacology in the context of drug development and cardiac risk assessment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".