Methadone Reverses Analgesic Tolerance Induced by Morphine Pretreatment
Bibliographic record
Abstract
BACKGROUND: Opiates such as morphine are the most powerful analgesics, but their protracted use is restrained by the development of tolerance to analgesic effects. Recent works suggest that tolerance to morphine might be due to its inability to promote mu opioid receptor endocytosis, and the co-injection of morphine with a mu opioid receptor internalizing agonist like [D-Ala(2),N-Me-Phe(4),Gly-ol(5)]enkephalin reduces tolerance to morphine. So far, no studies have been conducted to evaluate the ability of methadone to reduce morphine tolerance in morphine-pretreated animals, a treatment sequence that could be encountered in opiate rotation protocol. We investigated the ability of methadone (a mu opioid receptor internalizing agonist used in therapy) to reverse morphine tolerance and the associated cellular mechanisms in the periaqueductal gray matter, a region involved in pain control. METHODS: We measured analgesic response following a challenge dose of morphine in the hot plate test and investigated regulation of mu opioid receptor (coupling and endocytosis) and some cellular mechanisms involved in tolerance such as adenylate cyclase superactivation and changes in N-methyl-d-aspartate receptor subunits expression and phosphorylation state. RESULTS: A chronic treatment with morphine promoted tolerance to its analgesic effects and was associated with a lack of mu opioid receptor endocytosis, adenylate cyclase overshoot, NR2A and NR2B downregulation, and phosphorylation of NR1. We reported that a methadone treatment in morphine-treated mice reversed morphine tolerance to analgesia by promoting mu opioid receptor endocytosis and blocking cellular mechanisms of tolerance. CONCLUSIONS: Our data might lead to rational strategies to tackle opiate tolerance in the frame of opiate rotation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".