Systemic Delivery of Peptide Hormones Using Nasal Powders: Strategies and Future Perspectives
Bibliographic record
Abstract
Background: Peptide Hormones (PH) are mainly administered as parenteral injections due to their peculiar physicochemical properties, and susceptibility to enzymatic degradation after oral administration. With invasive routes, however, patient safety, acceptability, and compliance become a concern, especially when a patient has a chronic condition that requires repeated injections. The delivery of peptide hormones via the nasal route has gained momentum over the last few decades as a noninvasive alternative to parenteral injections and commercially available nasal liquid products. Objective: The aim of this paper was to review: (1) The benefits and limitations of nasal powder products, (2) Formulation strategies to enhance nasal delivery of peptide hormone drugs, (3) Nasal powder devices, and (4) Future perspectives of therapeutic nasal powders. The drugs examined specifically include calcitonin, desmopressin, ghrelin, glucagon, human growth hormone, insulin, octreotide, and oxytocin. Methods:: Nasal delivery of peptide hormones using powders was reviewed with the following databases: EBSCO, PUBMED, Web of Science, ClinicalTrials.gov, and EU Clinical Trials Register. Results: Nasal powders are a promising drug delivery system that may be safer and more effective than traditional injections and presently marketed nasal liquids for peptide hormone drugs. Conclusion: With sustained interest and growing body of supporting evidence, a range of nasal powders for systemic delivery of these drugs and delivery devices can be expected to enter the market in the future and offer more options to patients
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".