Overcoming barriers: nanomedicine-based strategies for nose-to-brain delivery
Bibliographic record
Abstract
For therapeutics to reach the brain, the several administration routes available come with some disadvantages, with the primary biological obstacle being the blood-brain barrier (BBB), which is not easy to penetrate despite the sophisticated technologies which have been developed. In addition, reaching specific brain structures invokes additional challenges, entailing more complicated delivery strategies. Nose-to-brain (N2B) delivery or the intranasal (IN) administration route provides a less invasive alternative. With the wealth of knowledge available on N2B delivery of nanomedicines and biotherapeutics, there is an opportunity to synthesize the current literature, especially in terms of promising strategies to improve N2B delivery of nanomedicines, highlighting experimental evaluation and translational challenges. We also emphasized the latest advancements in experimental models for nasal delivery. Aiming to bridge the gap between bench research and clinical application, we reviewed the cases of insulin and oxytocin, two biotherapeutics with high clinical potential for CNS-related diseases, and explore how nanomedicine-based platforms can enhance their effectiveness. This review offers a roadmap for overcoming barriers and accelerating the clinical translation of N2B therapeutics.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".