Docetaxel Conjugate Nanoparticles That Target α-Smooth Muscle Actin–Expressing Stromal Cells Suppress Breast Cancer Metastasis
Bibliographic record
Abstract
Docetaxel-conjugate nanoparticles, known as Cellax, were synthesized by covalently conjugating docetaxel and polyethylene glycol to acetylated carboxymethylcellulose via ester linkages, yielding a polymeric conjugate that self-assembled into 120 nm particles suitable for intravenous administration. In 4T1 and MDA-MB-231 orthotopic breast tumor models, Cellax therapy reduced α-smooth muscle actin (α-SMA) content by 82% and 70%, respectively, whereas native docetaxel and nab-paclitaxel (albumin-paclitaxel nanoparticle, Abraxane) exerted no significant antistromal activity. In Cellax-treated mice, tumor perfusion was increased by approximately 70-fold (FITC-lectin binding), tumor vascular permeability was enhanced by more than 30% (dynamic contrast-enhanced magnetic resonance imaging), tumor matrix was decreased by 2.5-fold (immunohistochemistry), and tumor interstitial fluid pressure was suppressed by approximately 3-fold after Cellax therapy compared with the control, native docetaxel, and nab-paclitaxel groups. The antistromal effect of Cellax treatment corresponded to a significantly enhanced antimetastatic effect: lung nodules were reduced by 7- to 24-fold by Cellax treatment, whereas native docetaxel and nab-paclitaxel treatments were ineffective. Studies of the 4T1 tumor showed that more than 85% of the Cellax nanoparticles were delivered to the α-SMA+ stroma. Significant tumor stromal depletion occurred within 16 hours (∼50% depletion) postinjection, and the α-SMA+ stroma population was almost undetectable (∼3%) by 1 week. The 4T1 tumor epithelial cell population was not significantly reduced in the week after Cellax injection. These data suggest that Cellax targets tumor stroma and performs more efficaciously than docetaxel and nab-paclitaxel.
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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".