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.
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.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".