Implication of delta opioid receptor subtype 2 but not delta opioid receptor subtype 1 in the development of morphine analgesic tolerance in a rat model of chronic inflammatory pain
Bibliographic record
Abstract
Opioids are well known for their robust analgesic effects. Chronic activation of mu opioid receptors (MOPs) is, however, accompanied by various unwanted effects such as analgesic tolerance. Among other mechanisms, interactions between MOPs and delta opioid receptors (DOPs) are thought to play an important role in morphine-induced behavioral adaptations. Interestingly, certain conditions such as inflammation enhance the function of the DOP through a MOP-dependent mechanism. Here, we investigated the role of DOPs during the development of morphine tolerance in an animal model of chronic inflammatory pain. Using behavioral approaches, we first established that repeated systemic morphine treatment induced morphine analgesic tolerance in rats coping with chronic inflammatory pain. We then observed that blockade of DOPs with subcutaneous naltrindole (NTI), a selective DOP antagonist, significantly attenuated the development of morphine tolerance in a dose-dependent manner. We confirmed that this effect was DOP mediated by showing that an acute injection of NTI had no effect on morphine-induced analgesia in naive animals. Previous pharmacological characterizations revealed the existence of DOP subtype 1 and DOP subtype 2. As opposed to NTI, 7-benzylidenenaltrexone and naltriben were reported to be selective DOP subtype 1 and DOP subtype 2 antagonists, respectively. Interestingly, naltriben but not 7-benzylidenenaltrexone was able to attenuate the development of morphine analgesic tolerance in inflamed rats. Altogether, our results suggest that targeting of DOP subtype 2 with antagonists provides a valuable strategy to attenuate the analgesic tolerance that develops after repeated morphine administration in the setting of chronic inflammatory pain.
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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 | 0.000 | 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".