Ligand functional selectivity and quantitative pharmacology at G protein-coupled receptors
Bibliographic record
Abstract
INTRODUCTION: In recent years, it has become clear that individual GPCRs can elicit multiple G-protein-dependent and -independent cellular responses. This has led to the discovery that certain ligands can differentially modulate these responses, a concept known as functional selectivity. AREAS COVERED: In this review, the authors describe the various manifestations of functional selectivity and its potential implication in drug discovery. The authors provide a historical perspective of the observations and methodologies that led to the evolution of this concept. The authors also describe the proposed molecular mechanisms responsible for the engagement of distinct subsets of signaling repertoire by different ligands. The review offers the reader a synthetic view of how functional selectivity could be used in the design of safer and more effective drugs. EXPERT OPINION: Our better understanding of the various ways by which compounds modulate GPCR activity has led to a parallel expansion of the terminology used to describe these phenomena. The authors propose a standardization of this nomenclature as an essential step to both simplify and clarify the language used among researchers to facilitate future collaboration and discovery of these important therapeutic targets. Such clarification of the various aspects of functional selectivity, coupled with the development of tools for effective monitoring, will undoubtedly bring this emerging concept into the general paradigm of drug discovery at GPCRs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".