PEG-Graft Density Controls Polymeric Nanoparticle Micelle Stability
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Polymeric nanoparticle micelles typically comprise amphiphilic block copolymers, having a hydrophobic core that is useful for chemotherapeutic encapsulation, and a hydrophilic corona for aqueous stability. Formulations often require the use of excipients to overcome poor particle stability, yet these excipients can be cytotoxic. In order to create a stable polymeric nanoparticle micelle without the use of excipients, we investigate a series of amphiphilic polymers where the hydrophobic core composition and molar mass is maintained and the hydrophilic corona is varied. With the graft copolymer, poly( d, l -lactide- co -2-methyl-2-carboxytrimethylenecarbonate)- g -poly(ethylene glycol) (P(LA- co -TMCC)- g -PEG), we demonstrate how PEG density can be tuned to improve the stability of the resulting self-assembled micelle. Increased PEG density leads to micelles that resist aggregation during lyophilization, allowing resuspension in aqueous media with narrow distribution. Furthermore, high PEG density micelles resist dissociation in serum protein containing media, with almost no dissociation seen in serum after 72 h. By changing the number of PEG chains per polymer backbone from 0.5 to 6, we observe increased stability of the nanoparticle micelles. All formulations are cytocompatible, as measured with MDA-MB-231 cells, and show no evidence for hemolysis, as measured with red blood cells. Importantly, PEG density does not impact drug loading within the nanoparticle micelle core, as demonstrated with the potent chemotherapeutic drug, docetaxel, confirming the role of the hydrophobic core for encapsulation. The surface properties of the polymeric nanoparticle micelles can thus be selectively modulated by variation in PEG density, which in turn influences stability, obviates the need for excipients and provides key insights into the design of drug delivery platforms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".