Effects of amlodipine, diltiazem, and verapamil on the anticonvulsant action of topiramate against maximal electroshock-induced seizures in micePresented in part at the 11th Congress of the European Federation of Neurological Societies, Brussels, Belgium, 25–28 August 2007.
Bibliographic record
Abstract
To assess the effect of 3 calcium channel antagonists (amlodipine, diltiazem, and verapamil) on the anticonvulsant action of topiramate (a new generation antiepileptic drug) in the mouse maximal electroshock seizure (MES) model. Amlodipine (20 mg/kg) significantly enhanced the anticonvulsant activity of topiramate in the MES test in mice, reducing its ED50 value from 54.83 to 33.10 mg/kg (p < 0.05). Similarly, diltiazem (5 and 10 mg/kg) markedly potentiated the antiseizure action of topiramate against MES, lowering its ED50 value from 54.83 to 32.48 mg/kg (p < 0.05) and 28.68 mg/kg (p < 0.01), respectively. In contrast, lower doses of amlodipine (5 and 10 mg/kg) and diltiazem (2.5 mg/kg) and all doses of verapamil (5, 10, and 20 mg/kg) had no significant impact on the antiseizure action of topiramate. Pharmacokinetic verification of the interaction of topiramate with amlodipine and diltiazem revealed that neither amlodipine nor diltiazem affected total brain topiramate concentration in experimental animals, and thus, the observed interactions were concluded to be pharmacodynamic in nature. The favorable combinations of topiramate with amlodipine or diltiazem deserve more attention from a clinical viewpoint because the enhanced antiseizure action of topiramate was not associated with any pharmacokinetic changes in total brain topiramate concentration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".