The Endocannabinoid System and Synthetic Cannabinoids in Preclinical Models of Seizure and Epilepsy
Bibliographic record
Abstract
Cannabinoids are compounds that are structurally and/or functionally related to the primary psychoactive constituent of Cannabis sativa, [INCREMENT]-tetrahydrocannabinol (THC). Cannabinoids can be divided into three broad categories: endogenous cannabinoids, plant-derived cannabinoids, and synthetic cannabinoids (SCs). Recently, there has been an unprecedented surge of interest into the pharmacological and medicinal properties of cannabinoids for the treatment of epilepsies. This surge has been stimulated by an ongoing shift in societal opinions about cannabinoid-based medicines and evidence that cannabidiol, a nonintoxicating plant cannabinoid, has demonstrable anticonvulsant activity in children with treatment-refractory epilepsy. The major receptors of the endogenous cannabinoid system (ECS)-the type 1 and 2 cannabinoid receptors (CB1R, CB2R)-have critical roles in the modulation of neurotransmitter release and inflammation, respectively; so, it is not surprising therefore that the ECS is being considered as a target for the treatment of epilepsy. SCs were developed as potential new drug candidates and tool compounds for studying the ECS. Beyond the plant cannabinoids, an extensive research effort is underway to determine whether SCs that directly target CB1R, CB2R, or the enzymes that breakdown endogenous cannabinoids have anticonvulsant effects in preclinical rodent models of epilepsy and seizure. This research demonstrates that many SCs do reduce seizure severity in rodent models and may have both positive and negative pharmacodynamic and pharmacokinetic interactions with clinically used antiepilepsy drugs. Here, we provide a comprehensive review of the preclinical evidence for and against SC modulation of seizure and discuss the important questions that need to be addressed in future studies.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".