VRQ397 (CRAVKY): a novel noncompetitive V2 receptor antagonist
Bibliographic record
Abstract
Vasopressin type 2 receptor (V2R) exhibits mostly important properties for hydroosmotic equilibrium and, to a lesser extent, on vasomotricity. Drugs currently acting on this receptor are analogs of the natural neuropeptide, arginine vasopressin (AVP), and hence are competitive ligands. Peptides that reproduce specific sequences of a given receptor have lately been reported to interfere with its action, and if such molecules arise from regions remote from the binding site they would be anticipated to exhibit noncompetitive antagonism, but this has yet to be shown for V2R. Six peptides reproducing juxtamembranous regions of V2R were designed and screened; the most effective peptide, cravky (labeled VRQ397), was characterized. VRQ397 was potent (IC(50) = 0.69 +/- 0.25 nM) and fully effective in inhibiting V2R-dependent physiological function, specifically desmopressin-L-desamino-8-arginine-vasopressin (DDAVP)-induced cremasteric vasorelaxation; this physiological functional assay was utilized to avoid overlooking interference of specific signaling events. A dose-response profile revealed a noncompetitive property of VRQ397; correspondingly, VRQ397 bound specifically to V2R-expressing cells could not displace its natural ligand, AVP, but modulated AVP binding kinetics (dissociation rate). Specificity of VRQ397 was further confirmed by its inability to bind to homologous V1 and oxytocin receptors and its inefficacy to alter responses to stimulation of these receptors. VRQ397 exhibited pharmacological permissiveness on V2R-induced signals, as it inhibited DDAVP-induced PGI(2) generation but not that of cAMP or recruitment of beta-arrestin2. Consistent with in vitro and ex vivo effects as a V2R antagonist, VRQ397 displayed anticipated in vivo aquaretic efficacy. We hereby describe the discovery of a first potent noncompetitive antagonist of V2R, which exhibits functional selectivity, in line with properties of a negative allosteric modulator.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".