The Delta-Opioid Receptor; a Target for the Treatment of Pain
Bibliographic record
Abstract
Nowadays, pain represents one of the most important societal burdens. Current treatments are, however, too often ineffective and/or accompanied by debilitating unwanted effects for patients dealing with chronic pain. Indeed, the prototypical opioid morphine, as many other strong analgesics, shows harmful unwanted effects including respiratory depression and constipation, and also produces tolerance, physical dependence, and addiction. The urgency to develop novel treatments against pain while minimizing adverse effects is therefore crucial. Over the years, the delta-opioid receptor (DOP) has emerged as a promising target for the development of new pain therapies. Indeed, targeting DOP to treat chronic pain represents a timely alternative to existing drugs, given the weak unwanted effects spectrum of DOP agonists. Here, we review the current knowledge supporting a role for DOP and its agonists for the treatment of pain. More specifically, we will focus on the cellular and subcellular localization of DOP in the nervous system. We will also discuss in further detail the molecular and cellular mechanisms involved in controlling the cellular trafficking of DOP, known to differ significantly from most G protein-coupled receptors. This review article will allow a better understanding of how DOP represents a promising target to develop new treatments for pain management as well as where we stand as of our ability to control its cellular trafficking and cell surface expression.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".