Using the Intranasal Route to Administer Drugs to Treat Neurological and Psychiatric Illnesses: Rationale, Successes, and Future Needs
Bibliographic record
Abstract
While the intranasal administration of drugs to the brain has been gaining both research attention and regulatory success over the past several years, key fundamental and translational challenges remain to fully leveraging the promise of this drug delivery pathway for improving the treatment of various neurological and psychiatric illnesses. In response, this review highlights the current state of understanding of the nose-to-brain drug delivery pathway and how both biological and clinical barriers to drug transport using the pathway can been addressed, as illustrated by demonstrations of how currently approved intranasal sprays leverage these pathways to enable the design of successful therapies. Moving forward, aiming to better exploit the understanding of this fundamental pathway, we also outline the development of nanoparticle systems that show improvement in delivering approved drugs to the brain and how engineered nanoparticle formulations could aid in breakthroughs in terms of delivering emerging drugs and therapeutics while avoiding systemic adverse effects.
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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 | 0.002 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".