In vivo and in vitro characterization of <scp>GL0034</scp> , a novel long‐acting <scp>glucagon‐like peptide</scp> ‐1 receptor agonist
Bibliographic record
Abstract
AIMS: To describe the in vitro characteristics and antidiabetic in vivo efficacy of the novel glucagon-like peptide-1 receptor agonist (GLP-1RA) GL0034. MATERIALS AND METHODS: Glucagon-like peptide-1 receptor (GLP-1R) kinetic binding parameters, cyclic adenosine monophosphate (cAMP) signalling, endocytosis and recycling were measured using HEK293 and INS-1832/3 cells expressing human GLP-1R. Insulin secretion was measured in vitro using INS-1832/3 cells, mouse islets and human islets. Chronic administration studies to evaluate weight loss and glycaemic effects were performed in db/db and diet-induced obese mice. RESULTS: Compared to the leading GLP-1RA semaglutide, GL0034 showed increased binding affinity and potency-driven bias in favour of cAMP over GLP-1R endocytosis and β-arrestin-2 recruitment. Insulin secretory responses were similar for both ligands. GL0034 (6 nmol/kg) led to at least as much weight loss and lowering of blood glucose as did semaglutide at a higher dose (14 nmol/kg). CONCLUSIONS: GL0034 is a G protein-biased agonist that shows powerful antidiabetic effects in mice, and may serve as a promising new GLP-1RA for obese patients with type 2 diabetes.
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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".