Production of high loading insulin nanoparticles suitable for oral delivery by spray drying and freeze drying techniques
Bibliographic record
Abstract
Insulin nanoparticles (NPs) with high loading content have found diverse applications in different dosage forms. This work aimed to evaluate the impact of freeze-drying and spray drying process on the structures of insulin-loaded chitosan nanoparticles, with or without mannitol as cryoprotectants. We also assessed the quality of these nanoparticles by redissolving them. Before dehydration, the chitosan/sodium tripolyphosphate/insulin crosslinked nanoparticles were optimized to 318 nm of particle size, 0.18 of PDI, 99.4% of entrapment efficiency, and 25.01% of loading content. After reconstitution, all nanoparticles, except the one produced by the freeze-drying method without using mannitol, maintained their spherical particle structure. The nanoparticles dehydrated by spray drying without mannitol also showed the smallest mean particle size (376 nm) and highest loading content (25.02%) with similar entrapment efficiency (98.7%) and PDI (0.20) compared to mannitol-containing nanoparticles dehydrated by either spray drying or freeze-drying techniques. The nanoparticles dried by spray drying without mannitol also resulted in the fastest release and highest cellular uptake efficacy of insulin. This work shows that spray drying can dehydrate insulin nanoparticles without the need for cryoprotectants, creating a significant advantage in terms of greater loading capacity with lower additive requirements and operating costs as compared to conventional freeze drying approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".