Highly selective SGLT2 inhibitor dapagliflozin reduces seizure activity in pentylenetetrazol-induced murine model of epilepsy
Bibliographic record
Abstract
BACKGROUND: Worldwide, over 10 million individuals suffer from drug-resistant epilepsy. New therapeutic strategies are needed to address this debilitating disease. Inhibition of sodium-glucose linked transporters (SGLTs), which are variably expressed in the brain, has been demonstrated to reduce seizure activity in murine models of epilepsy. Here we investigated the effects of dapagliflozin, a highly competitive SGLT2 inhibitor currently used as a drug for diabetes mellitus, on seizure activity in rats with pentylenetetrazol (PTZ) induced seizures. METHODS: Laboratory rats (n = 48) were evenly randomized into two experiments, each with four study arms: (1) a vehicle-treated (placebo) arm infused with saline; (2) a control arm infused with PTZ; (3) a treatment arm with PTZ and dapagliflozin at 75 mg/kg, and (4) another treatment arm with PTZ and dapagliflozin at 150 mg/kg. Study subjects were assessed for seizures either via EEG as measured by spike wave percentage (SWP), or clinically via Racine's scales scores (RSS) and time to first myoclonic jerk (TFMJ). RESULTS: Rats treated with dapagliflozin had lower mean SWP on EEG (20.4% versus 75.3% for untreated rats). Behaviorally, treatment with dapagliflozin improved means RSS (2.33 versus 5.5) and mean TFMJ (68.3 versus 196.7 s). All of these findings were statistically significant with p-values of < 0.0001. There was a trend towards even better seizure control with the higher dose of dapagliflozin at 150 mg/kg, however this was not consistently statistically significant. CONCLUSIONS: Dapagliflozin decreased seizure activity in rats with PTZ-induced seizures. This may be explained by the anti-seizure effects of decreased glucose availability and a reduction in sodium transport across neuronal membranes which can confer a stabilizing effect against excitability and unwanted depolarization. The potential clinical role of dapagliflozin and other SGLT2 inhibitors as anti-seizure medications should be further explored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".