Doxorubicin-loaded iron oxide nanoparticles for glioblastoma therapy: a combinational approach for enhanced delivery of nanoparticles
Bibliographic record
Abstract
Although doxorubicin (DOX) is an effective anti-cancer drug with cytotoxicity in a variety of different tumors, its effectiveness in treating glioblastoma multiforme (GBM) is constrained by insufficient penetration across the blood-brain barrier (BBB). In this study, biocompatible magnetic iron oxide nanoparticles (IONPs) stabilized with trimethoxysilylpropyl-ethylenediamine triacetic acid (EDT) were developed as a carrier of DOX for GBM chemotherapy. The DOX-loaded EDT-IONPs (DOX-EDT-IONPs) released DOX within 4 days with the capability of an accelerated release in acidic microenvironments. The DOX-loaded EDT-IONPs (DOX-EDT-IONPs) demonstrated an efficient uptake in mouse brain-derived microvessel endothelial, bEnd.3, Madin-Darby canine kidney transfected with multi-drug resistant protein 1 (MDCK-MDR1), and human U251 GBM cells. The DOX-EDT-IONPs could augment DOX's uptake in U251 cells by 2.8-fold and significantly inhibited U251 cell proliferation. Moreover, the DOX-EDT-IONPs were found to be effective in apoptotic-induced GBM cell death (over 90%) within 48 h of treatment. Gene expression studies revealed a significant downregulation of TOP II and Ku70, crucial enzymes for DNA repair and replication, as well as MiR-155 oncogene, concomitant with an upregulation of caspase 3 and tumor suppressors i.e., p53, MEG3 and GAS5, in U251 cells upon treatment with DOX-EDT-IONPs. An in vitro MDCK-MDR1-GBM co-culture model was used to assess the BBB permeability and anti-tumor activity of the DOX-EDT-IONPs and DOX treatments. While DOX-EDT-IONP showed improved permeability of DOX across MDCK-MDR1 monolayers compared to DOX alone, cytotoxicity in U251 cells was similar in both treatment groups. Using a cadherin binding peptide (ADTC5) to transiently open tight junctions, in combination with an external magnetic field, significantly enhanced both DOX-EDT-IONP permeability and cytotoxicity in the MDCK-MDR1-GBM co-culture model. Therefore, the combination of magnetic enhanced convective diffusion and the cadherin binding peptide for transiently opening the BBB tight junctions are expected to enhance the efficacy of GBM chemotherapy using the DOX-EDT-IONPs. In general, the developed approach enables the chemotherapeutic to overcome both BBB and multidrug resistance (MDR) glioma cells while providing site-specific magnetic targeting.
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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".