Opioid Analgesics: Rise and Fall of Ligand Biased Signalig and Future Perspectives in the Quest for the Holy Grail
Bibliographic record
Abstract
Opioid analgesics have been used for more than 5,000 years and remain the main pain medications prescribed today. Although morphine is considered the gold standard of pain relief, this selective µ-opioid receptor (MOP) agonist provides only moderate relief for many chronic pain conditions, and produces a number of unwanted effects that can affect the patient’s quality-of-life, prevent adherence to treatment or lead to addiction. In addition to the lack of progress in developing better analgesics, there have been no significant breakthroughs to date in combating the above-mentioned side effects. Fortunately, a better understanding of opioid pharmacology has given renewed hope for the development of better and safer pain medications. In this review, we describe how clinically approved opioids were initially characterized as biased ligands and what impact this approach might have on clinical practice. We also looked at the preclinical and clinical development of biased MOP agonists, focusing on the history of oliceridine, the first specifically designed biased analgesic. In addition, we explore the discrepancies between ligands with low intrinsic efficacy and those with biased properties. Finally, we examine the rationale behind the development of biased ligands during the opioid crisis.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".