The contribution of Na<sub>V</sub>1.6 to the efficacy of voltage‐gated sodium channel inhibitors in wild type and Na<sub>V</sub>1.6 gain‐of‐function (GOF) mouse seizure control
Bibliographic record
Abstract
Background and Purpose Inhibitors of voltage‐gated sodium channels (NaVs) are important anti‐epileptic drugs, but the contribution of specific channel isoforms is unknown since available inhibitors are non‐selective. We aimed to create novel, isoform selective inhibitors of Nav channels as a means of informing the development of improved antiseizure drugs. Experimental Approach We created a series of compounds with diverse selectivity profiles enabling block of NaV1.6 alone or together with NaV1.2. These novel NaV inhibitors were evaluated for their ability to inhibit electrically evoked seizures in mice with a heterozygous gain‐of‐function mutation (N1768D/+) in Scn8a (encoding NaV1.6) and in wild‐type mice. Key Results Pharmacologic inhibition of NaV1.6 in Scn8aN1768D/+ mice prevented seizures evoked by a 6‐Hz shock. Inhibitors were also effective in a direct current maximal electroshock seizure assay in wild‐type mice. NaV1.6 inhibition correlated with efficacy in both models, even without inhibition of other CNS NaV isoforms. Conclusions and Implications Our data suggest NaV1.6 inhibition is a driver of efficacy for NaV inhibitor anti‐seizure medicines. Sparing the NaV1.1 channels of inhibitory interneurons did not compromise efficacy. Selective NaV1.6 inhibitors may provide targeted therapies for human Scn8a developmental and epileptic encephalopathies and improved treatments for idiopathic epilepsies.
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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) | 0.001 | 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".