Chitosan Surface-Modified Hydroxycamptothecin Loaded Nanoparticles with Enhanced Transport Across Caco-2 Cell Monolayer
Bibliographic record
Abstract
This study explored the feasibility of using surface-modified nanoparticulate drug delivery system to enhance the transepithelial transport of antitumor drugs. An antitumor drug, 10-hydroxycamptothecin, was encapsulated into nanoparticles made of biodegradable poly(caprolactone-co-lactide)-PEG-poly(caprolactone-co-lactide) by a novel two-step nano-precipitation method. The obtained nanoparticles had a drug loading content of 10.4% and a size of 256.3 nm, exhibiting a steady and sustained in vitro release profile. By incubation in chitosan containing medium, the drug-loaded nanoparticles could be subsequently surface-modified with chitosan. The surface modification was monitored by dynamic light scattering method, zeta potential observation, and transmission electron microscopy, and its degree could be easily adjusted by varying the concentration of chitosan in the incubation medium. Caco-2 cell monolayer was used as an in vitro model to evaluate the intestinal 10-hydroxycamptothecin absorption. The absorptive transport of 10-hydroxycamptothecin could be improved to some extent by drug loaded nanoparticles and could be further enhanced in the case of surface-modified nanoparticles, suggesting that chitosan surface-modified nanoparticles may be a promising oral delivery system for antitumor drugs.
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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".