Intranasal delivery of insulin: An update on status quo and challenges for diabetes treatment
Bibliographic record
Abstract
Diabetes mellitus is a chronic metabolic disorder characterised by either autoimmune-mediated destruction of pancreatic β-cells or impaired physiological responses to circulating insulin. Conventional subcutaneous insulin administration does not achieve optimal pharmacokinetics due to delayed systemic absorption and its inability to replicate the prandial, pulsatile secretion pattern of endogenous insulin. In contrast, intranasal insulin delivery offers a promising non-invasive alternative, with a pharmacokinetic profile that more closely mirrors physiological insulin release following meals, thereby improving postprandial glycaemic control. This review outlines the advantages and limitations of the intranasal route for insulin administration in comparison to other delivery methods. It also describes the structural and physiological barriers of the nasal cavity, and the specific challenges associated with delivering protein therapeutics via this route. Emphasis is placed on strategies designed to enhance the mucosal permeation of insulin, including the use of nanotechnology, safe absorption enhancers and mucoadhesive polymers, drug complexation, and the development of particulate and responsive drug delivery systems. Finally, recent advances in clinical and preclinical studies of intranasal insulin formulations are discussed, highlighting the translational potential of this approach in diabetes management.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".