Salinomycin-Loaded Iron Oxide Nanoparticles for Glioblastoma Therapy
Bibliographic record
Abstract
Salinomycin is an antibiotic introduced recently as a new and effective anticancer drug. In this study, magnetic iron oxide nanoparticles (IONPs) were utilized as a drug carrier for salinomycin for potential use in glioblastoma (GBM) chemotherapy. The biocompatible polyethylenimine (PEI)-polyethylene glycol (PEG)-IONPs (PEI-PEG-IONPs) exhibited an efficient uptake in both mouse brain-derived microvessel endothelial (bEnd.3) and human U251 GBM cell lines. The salinomycin (Sali)-loaded PEI-PEG-IONPs (Sali-PEI-PEG-IONPs) released salinomycin over 4 days, with an initial release of 44% ± 3% that increased to 66% ± 5% in acidic pH. The Sali-IONPs inhibited U251 cell proliferation and decreased their viability (by approximately 70% within 48 h), and the nanoparticles were found to be effective in reactive oxygen species-mediated GBM cell death. Gene studies revealed significant activation of caspases in U251 cells upon treatment with Sali-IONPs. Furthermore, the upregulation of tumor suppressors (i.e., p53, Rbl2, Gas5) was observed, while TopII, Ku70, CyclinD1, and Wnt1 were concomitantly downregulated. When examined in an in vitro blood–brain barrier (BBB)-GBM co-culture model, Sali-IONPs had limited penetration (1.0% ± 0.08%) through the bEnd.3 monolayer and resulted in 60% viability of U251 cells. However, hyperosmotic disruption coupled with an applied external magnetic field significantly enhanced the permeability of Sali-IONPs across bEnd.3 monolayers (3.2% ± 0.1%) and reduced the viability of U251 cells to 38%. These findings suggest that Sali-IONPs combined with penetration enhancers, such as hyperosmotic mannitol and external magnetic fields, can potentially provide effective and site-specific magnetic targeting for GBM chemotherapy.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".