Human Serum Albumin Nanoparticles for Use in Cancer Drug Delivery: Process Optimization and In Vitro Characterization
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
Human serum albumin nanoparticles (HSA-NPs) are widely-used drug delivery systems with applications in various diseases, like cancer. For intravenous administration of HSA-NPs, the particle size, surface charge, drug loading and in vitro release kinetics are important parameters for consideration. This study focuses on the development of stable HSA-NPs containing the anti-cancer drug paclitaxel (PTX) via the emulsion-solvent evaporation method using a high-pressure homogenizer. The key parameters for the preparation of PTX-HSA-NPs are: the starting concentrations of HSA, PTX and the organic solvent, including the homogenization pressure and its number cycles, were optimized. Results indicate a size of 143.4 ± 0.7 nm and 170.2 ± 1.4 nm with a surface charge of −5.6 ± 0.8 mV and −17.4 ± 0.5 mV for HSA-NPs and PTX-HSA-NPs (0.5 mg/mL of PTX), respectively. The yield of the PTX-HSA-NPs was ~93% with an encapsulation efficiency of ~82%. To investigate the safety and effectiveness of the PTX-HSA-NPs, an in vitro drug release and cytotoxicity assay was performed on human breast cancer cell line (MCF-7). The PTX-HSA-NPs showed dose-dependent toxicity on cells of 52%, 39.3% and 22.6% with increasing concentrations of PTX at 8, 20.2 and 31.4 μg/mL, respectively. In summary, all parameters involved in HSA-NPs’ preparation, its anticancer efficacy and scale-up are outlined in this research article.
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 | 0.001 |
| 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