QbD-driven Formulation Development and Evaluation of Genistein Nanoparticles for Prostate Cancer
Bibliographic record
Abstract
Background: Genistein (GEN) shows significant anticancer potential, particularly against prostate cancer. However, its clinical application is limited by poor water solubility, rapid metabolism and excretion, low bioavailability, and lack of targeted delivery to cancer cells, hindering its effectiveness as a chemopreventive or therapeutic agent. Objective: In this study, poly-ε-caprolactone (PCL) nanoparticles incorporating polyvinyl alcohol (PVA) as a stabilizer were engineered to encapsulate genistein (GEN) effectively. Utilizing a Quality by Design (QbD) methodology, the development and optimization of these nanoparticles were systematically approached. Methods: GEN-loaded PCL nanoparticles (NPs) were prepared using the Solvent Evaporation Technique, ideal for encapsulating hydrophobic drugs. A Plackett–Burman design (PBD) identified key factors, followed by a Box–Behnken design (BBD) to optimize nanoparticle quality. The NPs were evaluated for particle size, zeta potential (ZP), polydispersity index (PDI), morphology, encapsulation efficiency (EE), in vitro drug release, and cytotoxicity. Results: The optimized formulation containing PCL, PVA, and Volume of organic solvent as 43.7 mg, 6.2 mg, and 10.0 ml, respectively was chosen because it showed EE (%) of 94.0%, average particle size of 150 nm, PDI of 0.10, ZP of -28.0 and exhibited sustained release of GEN for around four days. The antiproliferative activities of GEN PCL NPs were confirmed by the MTT test in vitro on malignant prostate carcinoma cell lines (PC3). Flow cytometric analysis showed that the inhibition of cell proliferation of more potent GEN PCL NPs is comparable with the effects of free GEN. Conclusion: The findings indicate that genistein-loaded PCL nanoparticles have the potential to augment the anticancer efficacy of genistein, both in vitro and in vivo. This suggests their promise as a viable candidate for prostate cancer treatment.
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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".