Insulin Delivery to the Brain via the Nasal Route: Unraveling the Potential for Alzheimer's Disease Therapy
Bibliographic record
Abstract
This comprehensive review delves into the potential of intranasal insulin delivery for managing Alzheimer's Disease (AD) while exploring the connection between AD and diabetes mellitus (DM). Both conditions share features of insulin signalling dysregulation and oxidative stress that accelerate inflammatory response. Given the physiological barriers to brain drug delivery, including the blood-brain barrier, intranasal administration emerges as a non-invasive alternative. Notably, intranasal insulin has shown neuroprotective effects, impacting Aβ clearance, tau phosphorylation, and synaptic plasticity. In preclinical studies and clinical trials, intranasally administered insulin achieved rapid and extensive distribution throughout the brain, with optimal formulations exhibiting minimal systemic circulation. The detailed mechanism of insulin transport through the nose-to-brain pathway is elucidated in the review, emphasizing the role of olfactory and trigeminal nerves. Despite promising prospects, challenges in delivering protein drugs from the nasal cavity to the brain remain, including enzymes, tight junctions, mucociliary clearance, and precise drug deposition, which hinder its translation to clinical settings. The review encompasses a discussion of the strategies to enhance the intranasal delivery of therapeutic proteins, such as tight junction modulators, cell-penetrating peptides, and nano-drug carrier systems. Moreover, successful translation of nose-to-brain drug delivery necessitates a holistic understanding of drug transport mechanisms, brain anatomy, and nasal formulation optimization. To date, no intranasal insulin formulation has received regulatory approval for AD treatment. Future research should address challenges related to drug absorption, nasal deposition, and the long-term effects of intranasal insulin. In this context, the evaluation of administration devices for nose-to-brain drug delivery becomes crucial in ensuring precise drug deposition patterns and enhancing bioavailability.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".