Δ<sup>9</sup>‐Tetrahydrocannabinolic acid alleviates collagen‐induced arthritis: Role of PPARγ and CB<sub>1</sub> receptors
Bibliographic record
Abstract
Background and Purpose Δ 9 ‐Tetrahydrocannabinolic acid (Δ 9 ‐THCA‐A), the precursor of Δ 9 ‐THC, is a non‐psychotropic phytocannabinoid that shows PPARγ agonist activity. Here, we investigated the ability of Δ 9 ‐THCA‐A to modulate the classic cannabinoid CB 1 and CB 2 receptors and evaluated its anti‐arthritis activity in vitro and in vivo. Experimental Approach Cannabinoid receptors binding and intrinsic activity, as well as their downstream signalling, were analysed in vitro and in silico. The anti‐arthritis properties of Δ 9 ‐THCA‐A were studied in human chondrocytes and in the murine model of collagen‐induced arthritis (CIA). Plasma disease biomarkers were identified by LC‐MS/MS based on proteomic and elisa assays. Key Results Functional and docking analyses showed that Δ 9 ‐THCA‐A can act as an orthosteric CB 1 receptor agonist and also as a positive allosteric modulator in the presence of CP‐55,940. Also, Δ 9 ‐THCA‐A seemed to be an inverse agonist for CB 2 receptors. In vivo, Δ 9 ‐THCA‐A reduced arthritis in CIA mice, preventing the infiltration of inflammatory cells, synovium hyperplasia, and cartilage damage. Furthermore, Δ 9 ‐THCA‐A inhibited expression of inflammatory and catabolic genes on knee joints. The anti‐arthritic effect of Δ 9 ‐THCA‐A was blocked by either SR141716 or T0070907. Analysis of plasma biomarkers, and determination of cytokines and anti‐collagen antibodies confirmed that Δ 9 ‐THCA‐A mediated its activity mainly through PPARγ and CB 1 receptor pathways. Conclusion and Implications Δ 9 ‐THCA‐A modulates CB 1 receptors through the orthosteric and allosteric binding sites. In addition, Δ 9 ‐THCA‐A exerts anti‐arthritis activity through CB 1 receptors and PPARγ pathways, highlighting its potential for the treatment of chronic inflammatory diseases such as rheumatoid arthritis.
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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) | 0.000 | 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".