Protein Release by Controlled Desorption from Transiently Cationic Nanoparticles
Bibliographic record
Abstract
Therapeutic release from hydrogels is traditionally controlled by encapsulation within nanoparticles; however, this strategy is limited for the release of proteins due to poor efficiency and denaturation. To overcome this problem, we designed an encapsulation-free release platform where negatively charged proteins are adsorbed to the exterior of transiently cationic nanoparticles, thus allowing the nanoparticles to be formulated separately from the proteins. Release is then governed by the change in nanoparticle surface charge from positive to neutral. To achieve this, we synthesized eight zwitterionic poly(lactide- block -carboxybetaine) copolymer derivatives and formulated them into nanoparticles with differing surface chemistry. The nanoparticles were colloidally stable and lost positive charge at rates dependent on the hydrolytic stability of their surface ester groups. The nanoparticles (NPs) were dispersed in a physically cross-linked hyaluronan-based hydrogel with one of three negatively charged proteins (transferrin, panitumumab, or granulocyte-macrophage colony-stimulating factor) to assess their ability to control release. For all three proteins, dispersing NPs within the gels resulted in significant attenuation of release, with the extent modulated by the hydrolytic stability of the surface groups. Release was rapid from fast-hydrolyzing ester groups, reduced with slow-hydrolyzing bulky ester groups, and very slow with nonhydrolyzing amide groups. When positively charged lysozyme was loaded into the nanocomposite gel, there was no significant attenuation of release compared to gel alone. These data demonstrate that electrostatic interactions between the protein and NP are the primary driver of protein release from the hydrogel. All released proteins retained bioactivity as determined with in vitro cell assays. This release strategy shows tremendous versatility and provides a promising new platform for controlled release of anionic protein therapeutics.
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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".