Na <sub>V</sub> 1.6 inhibition drives the efficacy of voltage-gated sodium channel inhibitors to prevent electrically induced seizures in both wild type and <i> Scn8a <sup>N1768D/+</sup> </i> gain-of-function mice
Bibliographic record
Abstract
Abstract Inhibitors of voltage-gated sodium channels (Na V s) are important anti-epileptic drugs, but the contribution of specific channel isoforms is unknown since available inhibitors are nonselective. We created a series of compounds with diverse selectivity profiles enabling block of Na V 1.6 alone or together with Na V 1.2. Mice with a heterozygous gain-of-function mutation (N1768D/+) in Scn8a (encoding Na V 1.6) responded with a tonic-clonic seizure to a mild 6 Hz stimulus that was innocuous to wild-type mice. Pharmacologic inhibition of Na V 1.6 in Scn8a N1768D/+ mice prevented seizures. Inhibitors were also effective in a direct current maximal electroshock seizure assay in wild-type mice. Na V 1.6 inhibition correlated with efficacy in both models, even without inhibition of other CNS Na V isoforms. Our data suggest Na V 1.6 inhibition is a driver of efficacy for Na V inhibitor anti-seizure medicines. Selective Na V 1.6 inhibitors may provide targeted therapies for human Scn8a developmental and epileptic encephalopathies and better tolerated treatments for idiopathic epilepsies. Graphical Abstract
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".