Nanoformulation of paclitaxel to enhance cancer therapy
Bibliographic record
Abstract
UNLABELLED: Paclitaxel is a microtubule inhibitor causing mitotic arrest and is widely used in cancer chemotherapy. However, its poor water solubility restricts its direct clinical applications. In this article, we report paclitaxel-loaded nanoparticles that are water soluble and that can improve the drug's bio-distribution and therapeutic efficacy. Paclitaxel-loaded nanoparticles were synthesized by using Pluronic copolymers (F-68 and P-123) and surfactant (Span 40) as nanocarrier. The toxicity and cellular uptake of paclitaxel-loaded nanoparticles were evaluated. The paclitaxel-loaded nanoparticles can completely disperse into phosphate buffer saline to produce a clear aqueous suspension. Based on HPLC analysis, the drug-loading rate is 9.0 ± 0.1% while drug encapsulation efficiency is 99.0 ± 1.0%. The cytotoxicity assay was performed using breast cancer MCF-7 and cervical cancer Hela cells. For MCF-7 cells, the half maximal inhibitory concentrations (IC50) of paclitaxel-loaded nanoparticles and paclitaxel are 8.5 ± 0.3 and 14.0 ± 0.7 ng/mL at 48 hours and 3.5 ± 0.4 and 5.2 ± 0.5 ng/mL at 72 hours across several runs. IC50 of paclitaxel-loaded nanoparticles and paclitaxel for Hela cells are 5.0 ± 0.3 and 8.0 ± 0.3 ng/mL at 48 hours and 2.0 ± 0.1 and 6.5 ± 0.3 ng/mL at 72 hours. In-vitro studies show that the drug's nanoformulation gives obvious enhancements in the drug's efficiency at killing cancer cells over paclitaxel alone. Materials of the nanocarrier used for nanoformulation are approved with low toxicity according to the result of cell studies. CONCLUSION: paclitaxel-loaded nanoparticles greatly improved the physicochemical properties of paclitaxel without modifying its chemical structure, allowing for deep-site cancer drug delivery and enhancing the drug therapeutic efficiency.
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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".