The spectrum of anticonvulsant efficacy of retigabine (ezogabine) in animal models: Implications for clinical use
Bibliographic record
Abstract
Retigabine [RTG (international nonproprietary name); ezogabine (EZG; U.S. adopted name)] is a first-in-class antiepileptic drug (AED) that reduces neuronal excitability by enhancing the activity of KCNQ (K(v)7) potassium (K(+)) channels. RTG/EZG has recently been approved by the European Medicines Agency and the U.S. Food and Drug Administration as adjunctive therapy in adults with partial-onset seizures. In this review we discuss the activity that RTG/EZG has demonstrated across a broad spectrum of in vitro/in vivo animal models of seizures, including generalized tonic-clonic, primary generalized (absence), and partial seizures, in addition to the compound's ability to resist and block the occurrence of seizures induced by a range of stimuli across different regions of the brain. The potency of RTG/EZG in models refractory to several conventional AEDs and the work done to assess antiepileptogenesis and neuroprotection are discussed. Studies that have evaluated the central nervous system side effects of RTG/EZG in animals are reviewed in order to compare these effects with adverse events observed in patients with epilepsy. Based on its demonstrated effect in a number of animal epilepsy models, the synergistic and additive activity of RTG/EZG with other AEDs supports its potential use in therapeutic combinations for different seizure types. The distinct mechanism of action of RTG/EZG from those of currently available AEDs, along with its broad preclinical activity, underscores the key role of KCNQ (K(v)7) K(+) channels in neuronal excitability, and further supports the potential efficacy of this unique molecule in the treatment of epilepsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".