Biased mu-opioid receptor agonists confer analgesia with reduced side effects
Bibliographic record
Abstract
Opioids are considered mainstay treatments for acute and terminal pain. In recent decades, however, overprescription and the increasing prevalence of illicit opioids has propelled North America into a state of “opioid crisis.” Along with the analgesic benefits, opioid use also commonly induces a number of side effects. Respiratory depression is an especially dangerous and potentially lethal example. The development of painkillers with improved safety profiles is thus a priority. Downstream to the mu-opioid receptor, which is responsible for the analgesic effects of opioids, β-arrestin-2 signaling has been suggested to be important for the manifestation of side effects, including respiratory depression. Two novel mu-opioid receptor agonists, TRV130 and PMZ21, have recently been reported to preferentially promote G protein-coupling over β-arrestin-2 signaling, thereby promoting analgesia with reduced side effects. TRV130 has been found in clinical trials to be more potent than morphine but safer in the setting of acute moderate-to-severe pain and is currently under New Drug Application review in the U.S. PMZ21 has shown promising and unique pain-relieving effects in mouse models, but further investigation is warranted to examine whether its therapeutic effects and safety profile are translatable to humans.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".