Evaluation of Selective 5-HT<sub>2C</sub> Agonists in Acute Seizure Models
Bibliographic record
Abstract
The 5-HT releaser/reuptake inhibitor fenfluramine has been recently reported to provide benefit as an adjunctive treatment for Dravet and Lennox-Gastaut syndromes, two types of severe childhood epilepsy. Despite its enhancement of 5-HT function, many effects of fenfluramine have been demonstrated to be dependent on 5-HT 2C receptor activation, suggesting that 5-HT 2C receptor activation may have an anticonvulsant property. The present study was designed to evaluate fenfluramine and 5-HT agonists of varying 5-HT 2C agonist selectivity, the relatively nonselective mCPP and Ro 60-0175, and the selective 5-HT 2C agonists lorcaserin and CP-809101 across a variety of acute seizure tests conducted in adult rats and mice, which have been instrumental in identifying the majority of clinically efficacious antiepileptic drugs. Tests included the maximal electroshock seizure (MES), MES threshold, and 6 Hz electrical convulsive seizure models and the chemoconvulsant pentylenetetrazole test. The effect of mCPP, lorcaserin, and CP-809101 against electrically evoked seizures in amygdala kindled rats was also investigated. Overall, at doses known to interact with 5-HT 2C R, there was no clear class-related effect of these agonists in any test. The only notable antiseizure effect of fenfluramine was inhibition of MES-induced tonic seizures in the rat. The current preclinical studies using the classical acute seizure tests and an amygdala kindling model do not identify a reliable antiseizure effect of fenfluramine, an agent now used in the treatment of human epilepsies, including Dravet syndrome and Lennox-Gastaut syndrome. Given the nature of these epilepsies, early life and/or genetic models may have better construct validity and be more appropriate for further study.
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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 | 0.000 | 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".