Design of Insulin-Loaded Nanoparticles Enabled by Multistep Control of Nanoprecipitation and Zinc Chelation
Bibliographic record
Abstract
Nanoparticle (NP) carriers provide new opportunities for controlled delivery of drugs, and have potential to address challenges such as effective oral delivery of insulin. However, due to the difficulty of efficiently loading insulin and other proteins inside polymeric NPs, their use has been mostly restricted to the encapsulation of small molecules. To better understand the processes involved in encapsulation of proteins in NPs, we study how buffer conditions, ionic chelation, and preparation methods influence insulin loading in poly(lactic-co-glycolic acid)-b-poly(ethylene glycol) (PLGA-PEG) NPs. We report that, although insulin is weakly bound and easily released from the NPs in the presence of buffer ions, insulin loading can be increased by over 10-fold with the use of chelating zinc ions and by the optimization of the pH during nanoprecipitation. We further provide ways of changing synthesis parameters to control NP size while maintaining high insulin loading. These results provide a simple method to enhance insulin loading of PLGA-PEG NPs and provide insights that may extend to other protein drug delivery systems that are subject to limited loading.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".