Human Serum Albumin Nanoparticles for Use in Cancer Drug Delivery: Process Optimization and In Vitro Characterization
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.
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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 | 0.001 | 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 | 0.000 |
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".