Combined CB1 antagonist AM6545 and NOP agonist SCH221510 worsen DSS-induced colitis in mice
Bibliographic record
Abstract
BACKGROUND: Despite the broad range of treatment options available for intestinal inflammation, the development of novel therapeutics remains essential due to the diminishing effectiveness of current therapies over time. Both the endocannabinoid system (ECS) and nociceptin/orphanin FQ peptide (NOP) receptors have been implicated in the pathogenesis of diseases associated with intestinal inflammation, highlighting their potential as therapeutic targets. OBJECTIVES: We hypothesized that an interaction exists between cannabinoid receptors 1 and 2 (CB1 and CB2) and the NOP receptor, which may hold therapeutic relevance for the treatment of colitis. MATERIAL AND METHODS: In this study, we used 3 selective ligands: a CB1 antagonist (AM6545), a CB2 antagonist (AM630) and a NOP agonist (SCH221510) in a mouse model of colitis induced by 3% dextran sulfate sodium (DSS). Quantification of several secondary messengers was conducted using western blot analysis. Real-time quantitative polymerase chain reaction (qPCR) was employed to assess CB1 expression levels in colonic tissue, while liquid chromatography-mass spectrometry (LC-MS) was used to evaluate the concentrations of endocannabinoids and related lipid mediators. RESULTS: We observed a statistically significant increase in the macroscopic score and a nonsignificant increase in the microscopic score in inflamed mice treated with both AM6545 and SCH221510 compared to those treated with SCH221510 alone. Additionally, the combination-treated group exhibited significantly lower levels of extracellular signal-regulated kinases 1/2 (ERK1/2) and significantly higher levels of phosphorylated protein kinase B (p-AKT) and β-arrestin relative to the SCH221510-only group. CONCLUSIONS: Our study offers novel insights into the interaction between the ECS and the NOP receptor, which may inform the development of new therapeutic strategies for inflammatory conditions such as colitis.
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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) | 0.001 | 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 | 0.001 | 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".