In situ-gelling hydrophobized starch nanoparticle-based nanoparticle network hydrogels for the effective delivery of intranasal olanzapine to treat brain disorders
Bibliographic record
Abstract
Intranasal (IN) delivery offers potential to deliver antipsychotic drugs with improved efficacy to the brain. However, the solubilization of such drugs and the frequency of required re-application both represent challenges to its practical implementation in treating various mental illnesses including schizophrenia. Herein, we report a sprayable nanoparticle network hydrogel (NNH) consisting of hydrophobically-modified starch nanoparticles (SNPs) and mucoadhesive chitosan oligosaccharide lactate (COL) that can gel in situ within the nasal cavity and release ultra-small penetrative SNPs over time. Hydrophobization of the SNPs enables enhanced uptake and prolonged release of poorly water soluble drugs such as olanzapine from the NNH depot through mucous and ultimately into the brain via the nose-to-brain (N2B) pathway. The hydrogel shows high in vitro cytocompatibility in mouse striatal neuron and human primary nasal cell lines and in vivo efficacy in an amphetamine-induced pre-clinical rat schizophrenia model, with IN-delivered NNH hydrogels maintaining successful attenuation of locomotor activity for up to 4 h while all other tested treatments (drug-only IN or conventional intraperitoneal delivery) failed to attenuate at any time point past 0.5 h. As such, in situ-gelling NNHs represent a safe excipient for the IN delivery of hydrophobic drugs directly to the brain using customized SNPs that exhibit high penetration and drug complexing properties to maximize effective drug delivery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".