Leveraging Colloidal Aggregation for Drug-Rich Nanoparticle Formulations
Bibliographic record
Abstract
While limited drug loading continues to be problematic for chemotherapeutics formulated in nanoparticles, we found that we could take advantage of colloidal drug aggregation to achieve high loading when combined with polymeric excipients. We demonstrate this approach with two drugs, fulvestrant and pentyl-PABC doxazolidine (PPD; a prodrug of doxazolidine, which is a DNA cross-linking anthracycline), and two polymers, polysorbate 80 (UP80) and poly(d,l-lactide-co-2-methyl-2-carboxytrimethylene carbonate)-graft-poly(ethylene glycol) (PLAC-PEG; a custom-synthesized, self-assembling amphiphilic polymer). In both systems, drug-loaded nanoparticles had diameters < 200 nm and were stable for up to two days in buffered saline solution and for up to 24 h in serum-containing media at 37 °C. While colloidal drug aggregates alone are typically unstable in saline and serum-containing media, we attribute the colloid stability observed herein to the polymeric excipients and consequent decreased protein adsorption. We expect this strategy of polymer-stabilized colloidal drug aggregates to be broadly applicable in delivery formulations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".