Characterization of [Nphe<sup>1</sup>]nociceptin(1‐13)NH<sub>2</sub>, a new selective nociceptin receptor antagonist
Bibliographic record
Abstract
1.. Nociceptin (orphanin FQ) is a novel neuropeptide capable of inducing a variety of biological actions via activation of a specific G-protein coupled receptor. However, the lack of a selective nociceptin receptor antagonist has hampered our understanding of nociceptin actions and the role of this peptide in pathophysiological states. As part of a broader programme of research, geared to the identification and characterization of nociceptin receptor ligands, we report that the novel peptide [Nphe(1)]nociceptin(1-13)NH(2) acts as the first truly selective and competitive nociceptin receptor antagonist and is devoid of any residual agonist activity. 2. [Nphe(1)]nociceptin(1-13)NH(2) binds selectively to recombinant nociceptin receptors expressed in Chinese hamster ovary (CHO) cells (pK(i) 8.4) and competitively antagonizes the inhibitory effects of nociceptin (i) on cyclic AMP accumulation in CHO cells (pA(2) 6.0) and (ii) on electrically evoked contractions in isolated tissues of the mouse, rat and guinea-pig with pA(2) values ranging from 6.0 to 6.4. 3. [Nphe(1)]nociceptin(1-13)NH(2) is also active in vivo, where it prevents the pronociceptive and antimorphine actions of intracerebroventricularly applied nociceptin, measured in the mouse tail withdrawal assay. Moreover, [Nphe(1)]nociceptin(1-13)NH(2) produces per se a dose dependent, naloxone resistant antinociceptive action and, at relatively low doses, potentiates morphine-induced analgesia. 4. Collectively our data indicate that [Nphe(1)]nociceptin(1-13)NH(2), acting as a nociceptin receptor antagonist, may be the prototype of a new class of analgesics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".