Nanoformulation of paclitaxel to enhance cancer therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it