Molecular pharmacology of selective Na <sub>v</sub> 1.6 and dual Na <sub>v</sub> 1.6 and Na <sub>v</sub> 1.2 channel inhibitors that suppress excitatory neuronal activity ex vivo
Bibliographic record
Abstract
Abstract Sodium channel inhibitors are used to treat neurological disorders of hyperexcitability. However, all currently available sodium channel targeting anti-seizure medications are non-selective among the Na V isoforms which potentially limits efficacy and therapeutic safety margins. XPC-7724 and XPC-5462 represent a new class of small molecule compounds. These compounds target inhibition of the Na V 1.6 and Na V 1.2 channels in excitatory pyramidal neurons and possess a molecular selectivity of >100 fold against Na V 1.1 channels that are dominant in inhibitory cells. This profile will enable pharmacological dissection of the physiological roles of Na V 1.2 and Na V 1.6 and help to define the role of each channel in disease states. These compounds bind to and stabilize the inactivated-state of the channels, demonstrate higher potency with longer residency times, and slower off-rates than carbamazepine and phenytoin. These compounds possess cellular selectivity ex vivo in inhibiting action potential firing in cortical excitatory pyramidal neurons, whilst sparing fast spiking inhibitory interneurons. XPC-5462 also suppresses epileptiform activity in an ex vivo brain slice seizure model. This class of compounds provides a unique approach for treating neuronal excitability disorders by selectively down-regulating excitatory circuits. 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) | 0.015 | 0.002 |
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".