Chitosan Nanoparticles at the Biological Interface: Implications for Drug Delivery
Bibliographic record
Abstract
The unique properties of chitosan make it a useful choice for various nanoparticulate drug delivery applications. Although chitosan is biocompatible and enables cellular uptake, its interactions at cellular and systemic levels need to be studied in more depth. This review focuses on the various physical and chemical properties of chitosan that affect its performance in biological systems. We aim to analyze recent research studying interactions of chitosan nanoparticles (NPs) upon their cellular uptake and their journey through the various compartments of the cell. The positive charge of chitosan enables it to efficiently attach to cells, increasing the probability of cellular uptake. Chitosan NPs are taken up by cells via different pathways and escape endosomal degradation due to the proton sponge effect. Furthermore, we have reviewed the interaction of chitosan NPs upon in vivo administration. Chitosan NPs are immediately surrounded by a serum protein corona in systemic circulation upon intravenous administration, and their biodistribution is mainly to the liver and spleen indicating RES uptake. However, the evasion of RES system as well as the targeting ability and bioavailability of chitosan NPs can be improved by utilizing specific routes of administration and covalent modifications of surface properties. Ongoing clinical trials of chitosan formulations for therapeutic applications are paving the way for the introduction of chitosan into the pharmaceutical market and for their toxicological evaluation. Chitosan provides specific biophysical properties for effective and tunable cellular uptake and systemic delivery for a wide range of applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".