Paclitaxel incorporated in hydrophobically derivatized hyperbranched polyglycerols for intravesical bladder cancer therapy
Bibliographic record
Abstract
OBJECTIVE: To develop paclitaxel incorporated into unimolecular micelles based on hydrophobically derivatized hyperbranched polyglycerols (dHPGs) for use as mucoadhesive intravesical agents against non-muscle-invasive bladder cancer. MATERIALS AND METHODS: Two different types of dHPGs (HPG- C10-polyethylene glycol (PEG) and polyethyleneimine (PEI)-C18-HPG) were synthesized and paclitaxel was loaded into these using a solvent evaporation method. After physicochemical characterization of the resulting nanoparticles, four human bladder cancer cell lines were incubated with various concentrations of paclitaxel incorporated in dHPGs and the results were compared with those of paclitaxel formulated in Cremophor-EL (Taxol(R), Bristol-Myers-Squibb). In vivo, nude mice with orthotopic KU7-luc tumours were intravesically instilled with phosphate buffered saline, Taxol, or paclitaxel/HPG-C10-PEG. RESULTS: dHPGs are mucoadhesive nanoparticles with hydrodynamic radii of <10 nm and incorporation of paclitaxel did not affect their size. The release profiles of paclitaxel from dHPGs were characterized by a rapid-release phase followed by a slower sustained-release phase. While the PEI-C18-HPG formulation released only approximately 40% of the initially incorporated paclitaxel, up to 80% was released from HPG-C10-PEG. Moreover, only paclitaxel/HPG-C10-PEG was stable in acidic urine. In vitro, all paclitaxel formulations potently decreased bladder cancer proliferation although paclitaxel/HPG-C10-PEG was slightly less cytotoxic than standard Taxol. By contrast, in vivo, the mucoadhesive HPG-C10-PEG formulation of paclitaxel was significantly more effective in reducing orthotopic tumour growth than Taxol and was well tolerated. CONCLUSION: Intravesical administration of mucoadhesive nanoparticulate formulations of paclitaxel might be a promising approach for instillation therapy of patients with non-muscle-invasive bladder cancer.
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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".