Advances in the Development of Bradykinin Receptor Ligands
Bibliographic record
Abstract
Kinins are blood-derived local-acting peptides that elicit specific cellular effects via the stimulation of two related G protein coupled receptors. While the B(2)receptor subtype, constitutively expressed in various tissues, is believed to mediate most of the physiological actions of kinins in healthy conditions, the B(1) receptor, highly regulated during inflammation, has been associated with the sustained actions of these peptides in various pathological situations. Potent peptide and nonpeptide modulators of both kinin receptors have been produced as pharmacological tools and potential therapeutics over the last three decades. More recently, the accumulating evidence suggesting that B(1) receptor blockade could be useful for the treatment of pain and inflammatory disorders has led to a shift in drug development efforts toward the synthesis of orally bioavailable nonpeptide B(1) receptor antagonists. Nonpeptide ligands of either receptor subtype produced by several industrial organizations often possess significant structural commonalities that can lead to the definition of a pharmacophore, especially when the receptor docking models are compared. The field of kinin receptors ligands research has reached an exciting step of its history, as the near future will reveal whether these molecules are therapeutically beneficial in various human diseases. This review will concisely summarize our current understanding of the biology of kinins and their receptors, before discussing the recent medicinal chemistry developments and challenges that bring new kinin receptor ligands closer to clinical applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".