Emerging technologies to achieve oral delivery of GLP-1 and GLP-1 analogs for treatment of type 2 diabetes mellitus (T2DM)
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) is a gastrointestinal (GI) peptide hormone that stimulates insulin secretion, gene expression and -cell proliferation, representing a potentially novel and promising therapeutic agent for the treatment of T2DM. DPP-IV-resistant, long-acting GLP-1 analogs have already been approved by FDA as injectable drugs for treating patients with T2DM. Oral delivery of therapeutic peptides and proteins would be preferred owing to advantages of lower cost, ease of administration and greater patient adherence. However, oral delivery of proteins can be affected by rapid enzymatic degradation in the GI tract and poor penetration across the intestinal membrane, which may require amounts that exceed practical consideration. Various production strategies have been explored to overcome challenges associated with the oral delivery of therapeutic peptides and proteins. The goal of this review is to provide an overview of the current state of progress made towards the oral delivery of GLP-1 and its analogs in the treatment of T2DM, with special emphasis on the development of plant and food-grade bacterial delivery systems. Recently, genetically engineered plants and food-grade bacteria have been increasingly explored as novel carrier systems for the oral delivery of peptide and protein drugs. These have a largely unexplored potential to serve both as an expression system and as a delivery vehicle for clinically relevant, cost effective therapeutics. As such, they hold great promise for human biopharmaceuticals and novel therapies against various diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".