Identifying ligand‐specific signalling within biased responses: focus on δ opioid receptor ligands
Bibliographic record
Abstract
UNLABELLED: Opioids activate GPCRs to produce powerful analgesic actions but at the same time induce side effects and generate tolerance, which restrict their clinical use. Reducing this undesired response profile has remained a major goal of opioid research and the notion of 'biased agonism' is raising increasing interest as a means of separating therapeutic responses from unwanted side effects. However, to fully exploit this opportunity, it is necessary to confidently identify biased signals and evaluate which type of bias may support analgesia and which may lead to undesired effects. The development of new computational tools has made it possible to quantify ligand-dependent signalling and discriminate this component from confounders that may also yield biased responses. Here, we analyse different approaches to identify and quantify ligand-dependent bias and review different types of confounders. Focus is on δ opioid receptor ligands, which are currently viewed as promising agents for chronic pain management. LINKED ARTICLES: This article is part of a themed section on Opioids: New Pathways to Functional Selectivity. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".