Antiepileptic drug discovery: lessons from the past and future challenges
Bibliographic record
Abstract
Historically, most antiepileptic drugs (AEDs) have been discovered either by serendipity, or the screening of compounds using acute seizure models. However, an increasing understanding of the molecular mechanisms underlying epileptogenesis has led to more rational approaches to drug discovery, which have focused on either enhancing inhibitory gamma-amino butyric acid (GABA)-ergic, or antagonizing excitatory glutamatergic, neurotransmission. Unfortunately, AEDs generated using such strategies have poor efficacy and safety profiles, as they interfere with normal cell processes, while ignoring the complex underlying pathophysiology of epilepsy. Recently, however, the use of new epilepsy models has led to the discovery of levetiracetam, an AED with a truly unique mechanism of action, devoid of anticonvulsant activity in normal animals, but with potent seizure suppression in genetic and kindled chronic epilepsy models, and an unusually high safety margin. The recent identification of brivaracetam and seletracetam, which optimize this unique mechanism of action, may further improve the medical management of epilepsy. The experience with levetiracetam, brivaracetam and seletracetam reveals that new experimental epilepsy models can detect AEDs possessing a unique mechanism of action and thereby target the future challenge of providing clinicians novel additions to the current armamentarium of AEDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".