Signaling diversity of mu- and delta- opioid receptor ligands: Re-evaluating the benefits of β-arrestin/G protein signaling bias
Bibliographic record
Abstract
Opioid analgesics are elective for treating moderate to severe pain but their use is restricted by severe side effects. Signaling bias has been proposed as a viable means for improving this situation. To exploit this opportunity, continuous efforts are devoted to understand how ligand-specific modulations of receptor functions could mediate the different in vivo effects of opioids. Advances in the field have led to the development of biased agonists based on hypotheses that allocated desired and undesired effects to specific signaling pathways. However, the prevalent hypothesis associating β-arrestin to opioid side effects was recently challenged and multiple of the newly developed biased drugs may not display the superior side effects profile that was sought. Moreover, biased agonism at opioid receptors is now known to be time- and cell-dependent, which adds a new layer of complexity for bias estimation. Here, we first review the signaling mechanisms underlying desired and undesired effects of opioids. We then describe biased agonism at opioid receptors and discuss the different perspectives that support the desired and undesired effects of opioids in view of exploiting biased signaling for therapeutic purposes. Finally, we explore how signaling kinetics and cellular background can influence the magnitude and directionality of bias at those receptors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".