<i>In vivo</i> Evaluation of Mucoadhesive Nanoparticulate Docetaxel for Intravesical Treatment of Non–Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
PURPOSE: The present work describes the development and in vitro and in vivo evaluation of a mucoadhesive nanoparticulate docetaxel (DTX) formulation for intravesical bladder cancer therapy. EXPERIMENTAL DESIGN: Mucoadhesive formulations based on hyperbranched polyglycerols (HPG), hydrophobically derivatized with C(8)/C(10) alkyl chains in the core and modified with methoxy-polyethylene glycol (MePEG) and amine groups in the shell (HPG-C(8/10)-MePEG-NH(2)) were synthesized and DTX was loaded into these by a solvent evaporation method. Both low-grade (RT4, MGHU3) and high-grade (UMUC3) human urothelial carcinoma cell lines were treated with various concentrations of DTX formulations in vitro. KU7 cells that stably express firefly luciferase (KU7-luc) were inoculated in female nude mice by intravesical instillation and quantified using bioluminescence imaging. Mice with established KU7-luc tumors were given a single intravesical instillation with PBS, Taxotere (DTX from Sanofi-aventis), and DTX-loaded HPG-C(8/10)-MePEG and/or HPG-C(8/10)-MePEG-NH(2). Drug uptake was conducted using LC/MS-MS (liquid chromatography/tandem mass spectrometry) and tumor microenvironment and uptake of rhodamine labeled HPGs was assessed. RESULTS: In vitro, all DTX formulations potently inhibited bladder cancer proliferation. However, in vivo, DTX-loaded HPG-C(8/10)-MePEG-NH(2) (mucoadhesive DTX) was the most effective formulation to inhibit tumor growth in an orthotopic model of bladder cancer. Furthermore, mucoadhesive DTX significantly increased drug uptake in mouse bladder tissues. In addition, rhodamine labeled HPG-C(8/10)-MePEG-NH(2) showed enhanced uptake of these nanoparticles in bladder tumor tissues. CONCLUSIONS: Our data show promising in vivo antitumor efficacy and provide preclinical proof of principle for the intravesical application of mucoadhesive nanoparticulate DTX formulation in the treatment of 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".