Magnetic Nanocarriers of Doxorubicin Coated with Poly(ethylene glycol) and Folic Acid: Relation between Coating Structure, Surface Properties, Colloidal Stability, and Cancer Cell Targeting
Bibliographic record
Abstract
We report the efficient one-step synthesis and detailed physicochemical evaluation of novel biocompatible nanosystems useful for cancer therapeutics and diagnostics (theranostics). These systems are the superparamagnetic iron oxide nanoparticles (SPIONs) carrying the anticancer drug doxorubicin and coated with the covalently bonded biocompatible polymer poly(ethylene glycol) (PEG), native and modified with the biological cancer targeting ligand folic acid (PEG-FA). These multifunctional nanoparticles (SPION-DOX-PEG-FA) are designed to rationally combine multilevel mechanisms of cancer cell targeting (magnetic and biological), bimodal cancer cell imaging (by means of MRI and fluorescence), and bimodal cancer treatment (by targeted drug delivery and by hyperthermia effect). Nevertheless, for these concepts to work together, the choice of ingredients and particle structure are critically important. Therefore, in the present work, a detailed physicochemical characterization of the organic coating of the hybrid nanoparticles is performed by several surface-specific instrumental methods, including surface-enhanced Raman scattering (SERS) spectroscopy, X-ray photoelectron spectrometry (XPS), and time-of-flight secondary ion mass spectrometry (ToF-SIMS). We demonstrate that the anticancer drug doxorubicin is attached to the iron oxide surface and buried under the polymer layers, while folic acid is located on the extreme surface of the organic coating. Interestingly, the moderate presence of folic acid on the particle surface does not increase the particle surface potential, while it is sufficient to increase the particle uptake by MCF-7 cancer cells. All of these original results contribute to the better understanding of the structure-activity relationship for hybrid biocompatible nanosystems and are encouraging for the applications in cancer theranostics.
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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".