PEG-Graft Density Controls Polymeric Nanoparticle Micelle Stability
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
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.
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.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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