Can in vitro studies aid in the development and use of antiseizure therapies? A report of the ILAE/AES Joint Translational Task Force
Bibliographic record
Abstract
In vitro preparations (defined here as cultured cells, brain slices, and isolated whole brains) offer a variety of approaches to modeling various aspects of seizures and epilepsy. Such models are particularly amenable to the application of anti-seizure compounds, and consequently are a valuable tool to screen the mechanisms of epileptiform activity, mode of action of known anti-seizure medications (ASMs), and the potential efficacy of putative new anti-seizure compounds. Despite these applications, all disease models are a simplification of reality and are therefore subject to limitations. In this review, we summarize the main types of in vitro models that can be used in epilepsy research, describing key methodologies as well as notable advantages and disadvantages of each. We argue that a well-designed battery of in vitro models can form an effective and potentially high-throughput screening platform to predict the clinical usefulness of ASMs, and that in vitro models are particularly useful for interrogating mechanisms of ASMs. To conclude, we offer several key recommendations that maximize the potential value of in vitro models in ASM screening. This includes the use of multiple in vitro tests that can complement each other, carefully combined with in vivo studies, the use of tissues from chronically epileptic (rather than naïve wild-type) animals, and the integration of human cell/tissue-derived preparations.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".