Pharmacokinetics and Pharmacodynamics of diclofenac in the presence and absence of glibenclamide in the rat
Bibliographic record
Abstract
PURPOSE: There are evidences that glibenclamide, a sulfonylurea antidiabetic agent, reduces the analgesic action of non-steroidal antiinflammatory drugs (NSAIDs), opioids and neuromodulators in animal models. The purpose of this work was to examine in the rat if such interaction involves pharmacokinetic mechanisms or is solely limited to the pharmacodynamic level. METHODS: All studies were carried out in female Wistar rats. Analgesia was assessed using the formalin test. Fifty microliters of diluted formalin was injected subcutaneously into the dorsal surface of the right hind paw. Nociceptive behavior was quantified as the number of flinches of the injected paw during 60 min after injection and a reduction in formalin-induced flinching was interpreted as an analgesic response. Rats were treated with oral diclofenac (3-18 mg/kg) in presence and the absence of oral glibenclamide (1-30 mg/kg). To evaluate the possibility of a pharmacokinetic interaction, the oral bioavailability of diclofenac (18 mg/kg) was studied in presence and the absence of glibenclamide (10 mg/kg). RESULTS: Oral administration of diclofenac produced a dose-dependent antinociceptive effect in the formalin test. Coadministration of glibenclamide significantly reduced diclofenac-induced antinociception. Notwithstanding, the interaction does no appear to involve pharmacokinetic mechanisms, as oral glibenclamide failed to produce any significant alteration in oral diclofenac bioavailability. CONCLUSION: Concomitant systemic administration of glibenclamide and diclofenac results in a reduction of the analgesic effect of the NSAID in the formalin test in the rat. This interaction, however, appears due solely to a pharmacodynamic mechanisms as diclofenac pharmacokinetics are not altered.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".