Production of high loading insulin nanoparticles suitable for oral delivery by spray drying and freeze drying techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 | 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 it