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Record W2033009192 · doi:10.1038/sj.bjp.0704049

Agonist and antagonist activities on human NPFF<sub>2</sub> receptors of the NPY ligands GR231118 and BIBP3226

2001· article· en· W2033009192 on OpenAlexafffund
Catherine Mollereau, Christine Gouardères, Yvan Dumont, Masato Kotani, Michel Detheux, Henri Doods, Marc Parmentier, Rémi Quirion, Jean‐Marie Zajac

Bibliographic record

VenueBritish Journal of Pharmacology · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health ResearchCentre National de la Recherche ScientifiqueInstitut National de la Santé et de la Recherche Médicale
KeywordsNeuropeptide Y receptorReceptorAgonistAntagonistChemistryNociceptin receptorInternal medicineEndocrinologyEndogenous agonistNeuropeptideOpioid peptideOpioidBiologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Neuropeptide FF (NPFF) is a part of a neurotransmitter system acting as a modulator of endogenous opioid functions. At this time, no non-peptide or peptide NPFF-antagonists have been discovered. Here, we demonstrate that Neuropeptide Y (NPY) ligands, in fact possess significant ability to interact with the human NPFF(2) receptors. NPY Y(1) antagonist BIBP3226 and mixed Y(1) antagonist/Y(4) agonist GR231118 are able to displace with low affinity, 50 -- 100 nM, the specific binding on NPFF receptors expressed in CHO cells as well as in rat dorsal spinal cord, an affinity however superior to those determined against Y(2), Y(4) or Y(5) receptors. Furthermore, BIBP3226 which is unable to inhibit the forskolin-stimulated cyclic AMP production mediated by NPFF(2) receptors, antagonizes the effect of NPFF, revealing the first antagonist of NPFF receptors. These properties of NPY ligands on Neuropeptide FF receptors must be considered when evaluating pharmacological activities of these drugs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.267
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2001
Admission routes2
Has abstractyes

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