Intranasal delivery of glucagon-like peptide-1 to the brain for obesity treatment: opportunities and challenges
Bibliographic record
Abstract
INTRODUCTION: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), approved by the US FDA for obesity treatment, are typically administered subcutaneously, an invasive method leading to suboptimal patient adherence and peripheral side effects. Additionally, this route requires the drug to cross the restrictive blood-brain barrier (BBB), limiting its safety and effectiveness in weight management and cognitive addiction disorders. Delivering the drug intranasally could overcome these drawbacks. AREAS COVERED: This review summarizes GLP-1 RAs used as anti-obesity agents, focusing on the intranasal route as a potential pathway to deliver these biomolecules to the brain. It also discusses strategies to overcome challenges associated with nasal delivery. EXPERT OPINION: Nose-to-brain (N2B) pathways can address limitations of the subcutaneous route for GLP-1 RAs. However, peptide delivery to the brain is challenging due to nasal physiological barriers and the drug's physicochemical properties. Innovative approaches, such as cell permeation enhancers, mucoadhesive systems, and nanocarriers in nasal formulations, along with efficient drug delivery devices, show promising preclinical results. Despite this, successful preclinical data does not guarantee clinical effectiveness, highlighting the need for comprehensive clinical investigations to optimize formulations and fully utilize the nose-to-brain interface for peptide delivery.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".