Rational development of fingolimod nano-embedded microparticles as nose-to-brain neuroprotective therapy for ischemic stroke
Bibliographic record
Abstract
Ischemic stroke is one of the major diseases causing varying degrees of dysfunction and disability worldwide. The current management of ischemic stroke poses significant challenges due to short therapeutic windows and limited efficacy, highlighting the pressing need for novel neuroprotective treatment strategies. Previous studies have shown that fingolimod (FIN) is a promising neuroprotective drug. Here, we report the rational development of FIN nano-embedded nasal powders using full factorial design experiments, aiming to provide rapid neuroprotection after ischemic stroke. Flash nanoprecipitation was employed to produce FIN nanosuspensions with the aid of polyvinylpyrrolidone and cholesterol as stabilizers. The optimized nanosuspension (particle size = 134.0 ± 0.6 nm, PDI = 0.179 ± 0.021, physical stability = 72 ± 0 h, and encapsulation efficiency of FIN = 90.67 ± 0.08%) was subsequently spray-dried into a dry powder, which exhibited excellent redispersibility (RdI = 1.09 ± 0.04) and satisfactory drug deposition in the olfactory region using a customized 3D-printed nasal cast (45.4%) and an Alberta Idealized Nasal Inlet model (8.6%) at 15 L/min. The safety of the optimized FIN nano-embedded dry powder was confirmed in cytotoxicity studies with nasal (RPMI 2650 and Calu-3 cells) and brain related cells (SH-SY5Y and PC 12 cells), while the neuroprotective effects were demonstrated by observed behavioral improvements and reduced cerebral infarct size in a middle cerebral artery occlusion mouse stroke model. The neuroprotective effect was further evidenced by increased expression of anti-apoptotic protein BCL-2 and decreased expression of pro-apoptotic proteins CC3 and BAX in brain peri-infarct tissues. Our findings highlight the potential of nasal delivery of FIN nano-embedded dry powder as a rapid neuroprotective treatment strategy for acute ischemic stroke.
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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) | 0.000 | 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".