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Record W2557947909 · doi:10.1039/c6ob02247b

Structure–activity relationship of novel macrocyclic biased apelin receptor agonists

2016· article· en· W2557947909 on OpenAlexafffund
Alexandre Murza, Xavier Sainsily, Jérôme Côté, Laurent Bruneau‐Cossette, Élie Besserer‐Offroy, Jean‐Michel Longpré, Richard Leduc, Robert Dumaine, Olivier Lesur, Mannix Auger‐Messier, Philippe Sarret, Éric Marsault

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2016
Typearticle
Languageen
FieldMedicine
TopicApelin-related biomedical research
Canadian institutionsUniversité de Sherbrooke
FundersFonds de Recherche du Québec - SantéCanada Foundation for InnovationMerck Sharp and DohmeUniversité de SherbrookeNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsChemistryApelinReceptorStereochemistryCombinatorial chemistryBiochemistry

Abstract

fetched live from OpenAlex

Apelin is the endogenous ligand for the G protein-coupled receptor APJ and exerts a key role in regulating cardiovascular functions. We report herein a novel series of macrocyclic analogues of apelin-13 in which the N- and C-terminal residues as well as the macrocycle composition were chemically modified to modulate structure-activity relationships on the APJ receptor. To this end, the binding affinity and the ability to engage G protein-dependent and G protein-independent signalling pathways of the resulting analogues were assessed. In this series, the position and the nature of the C-terminal aromatic residue is a determinant for APJ interaction and β-arrestin recruitment, as previously demonstrated for linear apelin-13 derivatives. We finally discovered compounds 1, 4, 11 and 15, four potent G protein-biased apelin receptor agonists exhibiting affinity in the nanomolar range for APJ. These macrocyclic compounds represent very useful pharmacological tools to explore the therapeutic potential of the apelinergic system.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.047
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.296
Teacher spread0.274 · 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.

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

Citations31
Published2016
Admission routes2
Has abstractyes

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