Intranasal Therapeutics for Neurodegenerative Disorders: Overcoming the Blood–Brain Barrier with Smart Formulations and Devices
Bibliographic record
Abstract
Neurodegenerative diseases have always posed a significant therapeutic challenge due to the restrictive nature of the blood-brain barrier (BBB). Intranasal drug delivery has emerged as a noninvasive approach to bypass the BBB, enabling targeted brain drug delivery while improving drug retention and transport. This review explores the physiological basis of the nose-to-brain pathway and various formulation strategies including mucoadhesive systems, permeation enhancers, and magnetophoretic approaches. Additionally, strategies to enhance intranasal delivery, such as P-glycoprotein inhibitors, cell-penetrating peptides, and enzyme inhibitors, are discussed alongside nanotechnology-based carriers, including surface-modified and bioconjugated systems. The role of specialized intranasal drug delivery devices (e.g., ViaNase, Optimist, and SipNose) in enhancing precision dosing is also highlighted. Despite its promise, intranasal delivery faces challenges such as limited therapeutic windows, scalability issues, and the constraint of the nasal cavity volume, which can accommodate only 200 μL of liquid per nostril. Optimizing drug stability, achieving accurate dosing, and enhancing bioavailability without nasal irritation remain key hurdles. Future research should focus on the development of commercially feasible nanoformulations and innovative medical devices to improve drug targeting and treatment efficacy for patients with neurodegenerative 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".