Innovative drug discovery strategies in epilepsy: integrating next-generation syndrome-specific mouse models to address pharmacoresistance and epileptogenesis
Bibliographic record
Abstract
Introduction Although there are numerous treatment options already available for epilepsy, over 30% of patients remain resistant to these antiseizure medications (ASMs). Historically, ASM discovery has relied on the demonstration of efficacy through the use of ‘traditional’ acute in vivo seizure models (e.g. maximal electroshock, subcutaneous pentylenetetrazol, and kindling). However, advances in genetic sequencing technologies and remaining medical needs for people with treatment-resistant epilepsy or special patient populations have encouraged recent efforts to identify novel compounds in syndrome-specific models of epilepsy. Syndrome-specific models, including Scn1a variant models of Dravet syndrome and APP/PS1 mice associated with familial early-onset Alzheimer’s disease, have already led to the discovery of two mechanistically novel treatments for developmental and epileptic encephalopathies (DEEs), namely cannabidiol and soticlestat, respectively.Areas covered In this review, the authors discuss how it is likely that next-generation drug discovery efforts for epilepsy will more comprehensively integrate syndrome-specific epilepsy models into early drug discovery providing the reader with their expert perspectives.Expert opinion The percentage of patients with pharmacoresistant epilepsy has remained unchanged despite over 30 marketed ASMs. Consequently, there is a high unmet need to reinvent and revise discovery strategies to more effectively address the remaining needs of patients with specific epilepsy syndromes, including drug-resistant epilepsy and DEEs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".