Enhanced Analgesic Effects and Gastrointestinal Safety of a Novel, Hydrogen Sulfide-Releasing Anti-Inflammatory Drug (ATB-352): A Role for Endogenous Cannabinoids
Bibliographic record
Abstract
Aims: The covalent linking of nonsteroidal anti-inflammatory drugs to a hydrogen sulfide (H 2 S)-releasing moiety has been shown to dramatically reduce gastrointestinal (GI) damage and bleeding, as well as increase anti-inflammatory and analgesic potency. We have tested the hypothesis that an H 2 S-releasing derivative of ketoprofen (ATB-352) would exhibit enhanced efficacy without significant GI damage in a mouse model of allodynia/hyperalgesia. Results: ATB-352 was significantly more potent and effective as an analgesic than ketoprofen and did not elicit GI damage. Pretreatment with an antagonist of the CB1 cannabinoid receptor (AM251) significantly reduced the analgesic effects of ATB-352. The CB1 antagonist exacerbated GI damage when coadministered with ketoprofen, but GI damage was not induced by the combination of ATB-352 and the CB1 antagonist. In vitro , ATB-352 was substantially more potent than ketoprofen as an inhibitor of fatty acid amide hydrolase, consistent with a contribution of endogenous cannabinoids to the analgesic effects of this drug. Blood anandamide levels were significantly depressed by ketoprofen, but remained unchanged after treatment with ATB-352. Innovation: Ketoprofen is a potent analgesic, but its clinical use, even in the short term, is significantly limited by its propensity to cause significant ulceration and bleeding in the GI tract. Covalently linking an H 2 S-releasing moiety to ketoprofen profoundly reduces the GI toxicity of the drug, while boosting analgesic effectiveness. Conclusion: This study demonstrates a marked enhancement of the potency and effectiveness of ATB-352, an H 2 S-releasing derivative of ketoprofen, in part, through the involvement of the endogenous cannabinoid system. This may have significant advantages for the control and management of pain, such as in a postoperative setting.
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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 | 0.000 |
| 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".