Local interactions between anandamide, an endocannabinoid, and ibuprofen, a nonsteroidal anti-inflammatory drug, in acute and inflammatory pain
Bibliographic record
Abstract
Anandamide, an endocannabinoid, is degraded by the enzyme fatty acid amide hydrolase which can be inhibited by nonsteroidal anti-inflammatory drugs (NSAIDs). The present work was designed to study the peripheral interactions between anandamide and ibuprofen (a non-specific cyclooxygenase inhibitor) in the rat formalin test. We first determined the ED50 for anandamide (0.018 microg +/- 0.009), ibuprofen (0.18 microg +/- 0.09), and their combination (0.006 microg +/- 0.002). Drugs were given 15 min before a 2.5% formalin injection into the dorsal surface of the right hind paw. Results were analyzed using isobolographic analysis. The antinociceptive interaction between anandamide and ibuprofen was synergistic. To further investigate the mechanisms by which the combination of anandamide with ibuprofen produced their antinociceptive effects, we used specific antagonists for the cannabinoid CB1 (AM251; 80 microg) and CB2 (AM630; 25 microg) receptors. We demonstrated that the antinociceptive effects of ibuprofen were not antagonized by either AM251 or AM630 and that those of anandamide were antagonized by AM251 but not by AM630. The synergistic antinociceptive effects of the combination of anandamide with ibuprofen were completely antagonized by AM251 but only partially inhibited by AM630. In conclusion, locally (hind paw) injected anandamide, ibuprofen or combination thereof decreased pain behavior in the formalin test. The combination of anandamide with ibuprofen produced synergistic antinociceptive effects involving both cannabinoid CB1 and CB2 receptors. Comprehension of the mechanisms involved needs further investigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".