Synthetic Modification of Carboxymethylcellulose and Use Thereof to Prepare a Nanoparticle Forming Conjugate of Docetaxel for Enhanced Cytotoxicity against Cancer Cells
Bibliographic record
Abstract
A nanoparticle formulation of docetaxel (DTX) was designed to address the strengths and limitations of current taxane delivery systems: PEGylation, high drug conjugation efficiency (>30 wt %), a slow-release mechanism, and a well-defined and stable nanoparticle identity were identified as critical design parameters. The polymer conjugate was synthesized with carboxymethylcellulose (CMC), an established pharmaceutical excipient characterized by a high density of carboxylate groups permitting increased conjugation of a drug. CMC was chemically modified through acetylation to eliminate its gelling properties and to improve solvent solubility, enabling high yield and reproducible conjugation of DTX and poly(ethylene glycol) (PEG). The optimal conjugate formulation (Cellax) contained 37.1 ± 1.5 wt % DTX and 4.7 ± 0.8 wt % PEG, exhibited a low critical aggregation concentration of 0.6 μg/mL, and formed 118-134 nm spherical nanoparticles stable against dilution. Conjugate compositions with a DTX degree of substitution (DS) outside the 12.3-20.8 mol % range failed to form discrete nanoparticles, emphasizing the importance of hydrophobic and hydrophilic balance in molecular design. Cellax nanoparticles released DTX in serum with near zero order kinetics (100% in 3 weeks), was internalized in murine and human cancer cells, and induced significantly higher toxic effects against a panel of tumor cell lines (2- to 40-fold lower IC50 values) compared to free DTX.
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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) | 0.001 | 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 | 0.001 |
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".