Formulation, Characterization and Cytotoxicity Effects of Novel Thymoquinone-PLGA-PF68 Nanoparticles
Bibliographic record
Abstract
Thymoquinone has anti-cancer properties. However, its application for clinical use is limited due to its volatile characteristics. The current study aims to develop a polymeric nanoformulation with PLGA-PEG and Pluronics F68 as encapsulants to conserve thymoquinone’s (TQ) biological activity before reaching the target sites. Synthesis of nanoparticles was successfully completed by encapsulating TQ with polymeric poly (D, L-lactide-co-glycolide)-block-poly (ethylene glycol) and Pluronics F68 (TQ-PLGA-PF68) using an emulsion–solvent evaporation technique. The size and encapsulation efficiency of TQ-PLGA-PF68 nanoparticles were 76.92 ± 27.38 nm and 94%, respectively. TQ released from these encapsulants showed a biphasic released pattern. Cytotoxicity activity showed that tamoxifen-resistant (TamR) MCF-7 breast cancer cells required a higher concentration of TQ-PLGA-PF68 nanoparticles than the parental MCF-7 cells to achieve IC50 (p < 0.05). The other two resistant subtypes (TamR UACC732 inflammatory breast carcinoma and paclitaxel-resistant (PacR) MDA-MB 231 triple-negative breast cell line) required a lower concentration of TQ-PLGA-PF68 nanoparticles compared to their respective parental cell lines (p < 0.05). These findings suggest that TQ encapsulation with PLGA-PEG and Pluronics F68 is a promising anti-cancer agent in mitigating breast cancer resistance to chemotherapeutics. In future studies, the anti-cancer activity of TQ-PLGA-PF68 with the standard chemotherapeutic drugs used for breast cancer treatment is recommended.
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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) | 0.000 | 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 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".