Covalent drug immobilization in poly(ester amide) nanoparticles for controlled release
Bibliographic record
Abstract
Abstract The use of polymeric nanoparticles to encapsulate and deliver drug molecules is a promising approach for improving drug properties such as water dispersibility, pharmacokinetics, and selectivity for the in vivo target. Described here is the development of poly(ester amide) (PEA) nanoparticles prepared from PEAs with pendant functional groups that allow for covalent conjugation of the drugs in order to mitigate the undesirable burst release of drug, commonly observed for nanoparticle‐based drug delivery systems. Parameters including the surfactant and PEA concentration in an emulsification‐evaporation procedure were studied in order to determine conditions for preparing particles with diameters < 200 nm. A hydroxyl‐functionalized rhodamine derivative, as a model drug, was then conjugated to a PEA having pendant carboxylic acid groups to afford a PEA‐rhodamine conjugate with the dye covalently attached by ester linkages. The emulsification‐evaporation procedure was used to prepare nanoparticles from this conjugate and these particles were found to release the dye much more slowly and without a burst effect, in comparison with analogous nanoparticles having the rhodamine physically encapsulated. The same approach was applied to the anti‐cancer drug floxuridine and the resulting nanoparticles also afforded sustained drug release. This work suggests the promise of PEAs with pendant functional groups for providing nanoparticle‐based drug delivery vehicles with slow and sustained drug release.
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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".