Improvements in Drug-Delivery Properties by Co-Encapsulating Curcumin in SN-38-Loaded Anticancer Polymeric Nanoparticles
Bibliographic record
Abstract
SN-38 is an immensely potent anticancer agent although its use necessitates encapsulation to overcome issues of poor solubility and stability. Since SN-38 is a notoriously challenging drug to encapsulate, new avenues to increase encapsulation efficiency in polymer nanoparticles (PNPs) are needed. In this paper, we show that nanoprecipitation with curcumin (CUR) increases SN-38 encapsulation efficiencies in coloaded SN-38/CUR-PNPs based on poly(ε-caprolactone)- block -poly(ethylene glycol) (PCL- b -PEG) by up to a factor of 10. In addition, we find a dramatic decrease in PNP polydispersities, from 0.34 to 0.07, as the initial CUR-to-polymer ratio increases from 0 to 10, with only a modest increase in PNP size (from 40 to 55 nm). Compared to coloaded PNP formation using nanoprecipitation in the bulk or in a gas–liquid, a two-phase microfluidic reactor shows similar trends with respect to CUR content, although improvements in SN-38 encapsulation efficiencies both with and without CUR are found using the microfluidic method. Additional precipitation studies without copolymer suggest that CUR increases the dispersion of SN-38 in the solvent medium of micelle formation, which may contribute to the observed encapsulation enhancement. Cytotoxicity studies of unencapsulated SN-38/CUR mixtures show that addition of CUR does not significantly affect SN-38 potency against either U87 (glioblastoma) or A204 (rhabdomyosarcoma) cell lines. However, we find significant differences in the potencies of SN-38/CUR-PNP formulations depending on initial CUR amounts, with an optimized formulation showing subnanomolar cytotoxicity against A204 cells, significantly more potent than either free SN-38 or PNPs containing only SN-38.
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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) | 0.001 | 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 | 0.000 | 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".