<i>In vivo</i>pharmacokinetics, biodistribution and anti-tumor effect of paclitaxel-loaded targeted chitosan-based polymeric micelle
Bibliographic record
Abstract
A water-insoluble anti-tumor agent, paclitaxel (PTX) was successfully incorporated into novel-targeted polymeric micelles based on tocopherol succinate-chitosan-polyethylene glycol-folic acid (PTX/TS-CS-PEG-FA). The aim of the present study was to evaluate the pharmacokinetics, tissue distribution and efficacy of PTX/TS-CS-PEG-FA in comparison to Anzatax® in tumor bearing mice. The micellar formulation showed higher in vitro cytotoxicity against mice breast cancer cell line, 4T1, due to the folate receptor-mediated endocytosis. The IC50 value of PTX, a concentration at which 50% cells are killed, was 1.17 and 0.93 µM for Anzatax® and PTX/TS-CS-PEG-FA micelles, respectively. The in vivo anti-tumor efficacy of PTX/TS-CS-PEG-FA, as measured by reduction in tumor volume of 4T1 mouse breast cancer injected in Balb/c mice was significantly greater than that of Anzatax®. Pharmacokinetic study in tumor bearing mice revealed that the micellar formulation prolonged the systemic circulation time of PTX and the AUC of PTX/TS-CS-PEG-FA was obtained 0.83-fold lower than Anzatax®. Compared with Anzatax®, the Vd, T1/2ß and MRT of PTX/TS-CS-PEG-FA was increased by 2.76, 2.05 and 1.68-fold, respectively. As demonstrated by tissue distribution, the PTX/TS-CS-PEG-FA micelles increased accumulation of PTX in tumor, therefore, resulted in anti-tumor effects enhancement and drug concentration in the normal tissues reduction. Taken together, our evaluations show that PTX/TS-CS-PEG-FA micelle is a potential drug delivery system of PTX for the effective treatment of the tumor and systematic toxicity reduction, thus, the micellar formulation can provide a useful alternative dosage form for intravenous administration of PTX.
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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".