Preparation, characterization, and antitumor activity of paclitaxel-loaded folic acid modified and TAT peptide conjugated PEGylated polymeric liposomes
Bibliographic record
Abstract
Targeting therapy is a promising strategy for enhancing the therapeutic potential of chemotherapeutic agents. In this study, we report the construction of a multifunctional drug delivery system, termed folic acid modified and TAT peptide conjugated PEGylated polymeric liposomes (FA-TATp-PLs), which is originally derived from octadecyl-quaternized lysine modified chitosan and cholesterol. Our data revealed that FA-TATp-PLs have a particle size of about 60 nm with a zeta potential of about 30 mV, a low burst release effect within the first day, a sustained release for the next 14 days in vitro as well as an instant cellular uptake by folate receptor-overexpressing KB human nasopharyngeal carcinoma cells. In vitro cytotoxicity of paclitaxel-loaded FA-TATp-PLs in KB cells was superior to that of Taxol(®). Furthermore, a comparable antitumor efficacy of paclitaxel-loaded FA-TATp-PLs and Taxol(®) was observed at the same doses in murine models bearing nasopharyngeal carcinoma. These results demonstrate that the paclitaxel formulation not only exhibits a higher antitumor activity but also significantly reduces the toxicity and improves the bioavailability as compared to that of free paclitaxel for the treatment of nasopharyngeal carcinoma. Taken together, our findings indicate that paclitaxel-loaded FA-TATp-PLs are a promising nano-sized drug formulation for future cancer therapy.
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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) | 0.000 | 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".