International Union of Basic and Clinical Pharmacology. Guidelines for GPCR ligand bias
Bibliographic record
Abstract
G protein-coupled receptors modulate a plethora of physiological processes and mediate the effects of one-third of FDA-approved drugs. Notably, depending on which ligand has activated a particular receptor, it can engage different intracellular transducers. This paradigm of ligand-dependent ‘biased signaling’ dictates a need to advance beyond the level of receptors to consider the combined ligand-receptor pair in order to understand physiological signaling. Bias signaling also has the potential to improve medicines by reducing adverse effects. However, this is challenged by inconsistent interpretation of results and lack of commonly agreed guidelines. Here, we present recommended terminology and guidelines to conduct, report and quantify bias in a comparable and reproducible fashion. We expect these recommendations will facilitate a common understanding of experiments and findings across basic receptor research and drug discovery, while the area and the analytical methods to measure bias are still evolving, especially in complex cellular, tissue and organismal systems.
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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 | 0.042 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.034 | 0.056 |
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".