Protein Engineering Strategies for Sustained Glucagon-Like Peptide-1 Receptor–Dependent Control of Glucose Homeostasis
Bibliographic record
Abstract
OBJECTIVE: We have developed a novel platform for display and delivery of bioactive peptides that links the biological properties of the peptide to the pharmacokinetic properties of an antibody. Peptides engineered in the MIMETIBODY platform have improved biochemical and biophysical properties that are quite distinct from those of Fc-fusion proteins. CNTO736 is a glucagon-like peptide 1 (GLP-1) receptor agonist engineered in our MIMETIBODY platform. It retains many activities of native GLP-1 yet has a significantly enhanced pharmacokinetic profile. Our goal was to develop a long-acting GLP-1 receptor agonist with sustained efficacy. RESEARCH DESIGN AND METHODS: In vitro and in vivo activity of CNTO736 was evaluated using a variety of rodent cell lines and diabetic animal models. RESULTS: Acute pharmacodynamic studies in diabetic rodents demonstrate that CNTO736 reduces fasting and postprandial glucose, decreases gastric emptying, and inhibits food intake in a GLP-1 receptor-specific manner. Reduction of food intake following CNTO736 dosing is coincident with detection of the molecule in the circumventricular organs of the brain and activation of c-fos in regions protected by the blood-brain barrier. Diabetic rodents dosed chronically with CNTO736 have lower fasting and postprandial glucose and reduced body weight. CONCLUSIONS: Taken together, our data demonstrate that CNTO736 produces a spectrum of GLP-1 receptor-dependent actions while exhibiting significantly improved pharmacokinetics relative to the native GLP-1 peptide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
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.
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 teacher head, 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".