Repurposed molecules for antiepileptogenesis: Missing an opportunity to prevent epilepsy?
Bibliographic record
Abstract
Prevention of epilepsy is a great unmet need. Acute central nervous system (CNS) insults such as traumatic brain injury (TBI), cerebrovascular accidents (CVA), and CNS infections account for 15%-20% of all epilepsy. Following TBI and CVA, there is a latency of days to years before epilepsy develops. This allows treatment to prevent or modify postinjury epilepsy. No such treatment exists. In animal models of acquired epilepsy, a number of medications in clinical use for diverse indications have been shown to have antiepileptogenic or disease-modifying effects, including medications with excellent side effect profiles. These include atorvastatin, ceftriaxone, losartan, isoflurane, N-acetylcysteine, and the antiseizure medications levetiracetam, brivaracetam, topiramate, gabapentin, pregabalin, vigabatrin, and eslicarbazepine acetate. In addition, there are preclinical antiepileptogenic data for anakinra, rapamycin, fingolimod, and erythropoietin, although these medications have potential for more serious side effects. However, except for vigabatrin, there have been almost no translation studies to prevent or modify epilepsy using these potentially "repurposable" medications. We may be missing an opportunity to develop preventive treatment for epilepsy by not evaluating these medications clinically. One reason for the lack of translation studies is that the preclinical data for most of these medications are disparate in terms of types of injury, models within different injury type, dosing, injury-treatment initiation latencies, treatment duration, and epilepsy outcome evaluation mode and duration. This makes it difficult to compare the relative strength of antiepileptogenic evidence across the molecules, and difficult to determine which drug(s) would be the best to evaluate clinically. Furthermore, most preclinical antiepileptogenic studies lack information needed for translation, such as dose-blood level relationship, brain target engagement, and dose-response, and many use treatment parameters that cannot be applied clinically, for example, treatment initiation before or at the time of injury and dosing higher than tolerated human equivalent dosing. Here, we review animal and human antiepileptogenic evidence for these medications. We highlight the gaps in our knowledge for each molecule that need to be filled in order to consider clinical translation, and we suggest a platform of preclinical antiepileptogenesis evaluation of potentially repurposable molecules or their combinations going forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".