A pH‐sensitive opioid does not exhibit analgesic tolerance in a mouse model of colonic inflammation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Tolerance to the analgesic effects of opioids and resultant dose escalation is associated with worsening of side effects and greater addiction risk. Here, we compare the development of tolerance to the conventional opioid fentanyl with a novel pH-sensitive μ-opioid receptor (MOR) agonist, (±)-N-(3-fluoro-1-phenethylpiperidine-4-yl)-N-phenyl propionamide (NFEPP) that is active only in acidic inflammatory microenvironments. EXPERIMENTAL APPROACH: An opioid tolerance model was developed in male C57BL/6 mice, with and without dextran sulphate sodium colitis, using increasing doses of either fentanyl or NFEPP over 5 days. Visceral nociception was assessed in vivo by measuring visceromotor responses (VMRs) to noxious colorectal distensions and in vitro measuring colonic afferent nerve activity of mesenteric nerves and performing patch-clamp recordings from isolated dorsal root ganglia neurons. Somatic thermal nociception was tested using a tail immersion assay. Cardiorespiratory effects were analysed by pulse oximeter experiments. KEY RESULTS: VMRs and tail immersion tests demonstrated tolerance to fentanyl, but not to NFEPP in colitis mice. Cross-tolerance also occurred to fentanyl, but not to NFEPP. The MOR agonist DAMGO inhibited colonic afferent nerve activity in colitis mice exposed to chronic NFEPP, but not those from fentanyl-treated mice. Similarly, in patch-clamp recordings from isolated dorsal root ganglia neurons, DAMGO inhibited neurons from NFEPP-, but not fentanyl-treated mice. CONCLUSION AND IMPLICATIONS: NFEPP did not exhibit tolerance in an inflammatory pain model, unlike fentanyl. Consequently, dose escalation to maintain analgesia during an evolving inflammation could be avoided, mitigating the potential risk of side effects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".