Targeting GLP-1 receptor trafficking to improve agonist efficacy
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor (GLP-1R) activation promotes insulin secretion from pancreatic beta cells, causes weight loss, and is an important pharmacological target in type 2 diabetes (T2D). Like other G protein-coupled receptors, the GLP-1R undergoes agonist-mediated endocytosis, but the functional and therapeutic consequences of modulating GLP-1R endocytic trafficking have not been clearly defined. Here, we investigate a series of biased GLP-1R agonists with variable propensities for GLP-1R internalization and recycling. Compared to a panel of FDA-approved GLP-1 mimetics, compounds that retain GLP-1R at the plasma membrane produce greater long-term insulin release, which is dependent on a reduction in β-arrestin recruitment and faster agonist dissociation rates. Such molecules elicit glycemic benefits in mice without concomitant increases in signs of nausea, a common side effect of GLP-1 therapies. Our study identifies a set of agents with specific GLP-1R trafficking profiles and the potential for greater efficacy and tolerability as T2D treatments.
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 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 | 0.001 |
| 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".