Finding a better drug for epilepsy: Antiepileptogenesis targets
Bibliographic record
Abstract
For several decades, both in vitro and in vivo models of seizures and epilepsy have been employed to unravel the molecular and cellular mechanisms underlying the occurrence of spontaneous recurrent seizures (SRS)-the defining hallmark of the epileptic brain. However, despite great advances in our understanding of seizure genesis, investigators have yet to develop reliable biomarkers and surrogate markers of the epileptogenic process. Sadly, the pathogenic mechanisms that produce the epileptic condition, especially after precipitating events such as head trauma, inflammation, or prolonged febrile convulsions, are poorly understood. A major challenge has been the inherent complexity and heterogeneity of known epileptic syndromes and the differential genetic susceptibilities exhibited by patients at risk. Therefore, it is unlikely that there is only one fundamental pathophysiologic mechanism shared by all the epilepsies. Identification of antiepileptogenesis targets has been an overarching goal over the last decade, as current anticonvulsant medications appear to influence only the acute process of ictogenesis. Clearly, there is an urgent need to develop novel therapeutic interventions that are disease modifying-therapies that either completely or partially prevent the emergence of SRS. An important secondary goal is to develop new treatments that can also lessen the burden of epilepsy comorbidities (e.g., cognitive impairment, mood disorders) by preventing or reducing the deleterious changes during the epileptogenic process. This review summarizes novel antiepileptogenesis targets that were critically discussed at the XIth Workshop on the Neurobiology of Epilepsy (WONOEP XI) meeting in Grottaferrata, Italy. Further, emerging neurometabolic links among several target mechanisms and highlights of the panel discussion are presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".