Exploring the Technology Landscape of 7TMR Drug Signaling Profiling
Bibliographic record
Abstract
Seven transmembrane domain receptors (7TMRs) constitute the largest family of transmembrane proteins in vertebrates and are the targets of more than 40% of currently marketed drugs. It is now accepted that these receptors are highly dynamic "microprocessors" that adopt a continuum of functionally distinct active conformations. The novel concept of biased agonism (or functional selectivity) posits that different ligands stabilize unique receptor conformations with each conformation imparting distinct signaling, and thus biological attributes, to a given receptor. The pharmacotherapeutic potential of biased agonism lies in possibility to develop molecules that selectively engage beneficial pathways while inhibiting or remaining inert towards those producing deleterious outcomes. Various strategies are now applied for the discovery of biased ligands. Many assays use second messenger levels (i.e., calcium, inositol trisphosphate, cAMP) as a quantitative readout of G-protein subtype-specific activity. However, due to complex cross-regulation between the various G-protein pathways, second messenger levels alone are not directly reflective of a ligand's activity on a specific pathway. Consequently, direct measurements of receptor-proximal events (such as G-protein activation and β-arrestin coupling) are required for a more accurate quantification of ligand's efficacy (or bias) towards different pathways. The discovery that various ligands of the same receptor can display different efficacies and potencies towards different receptor-downstream signaling pathways has not only revitalized the process of 7TMR drug discovery, but has significantly transformed the field of pharmacology as a whole. This review will showcase the current pharmacological toolbox available for the discovery and validation of biased ligands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".