Bioactivity of Hybrid Polymeric Magnetic Nanoparticles and Their Applications in Drug Delivery
Bibliographic record
Abstract
BACKGROUND: Engineered magnetic nanoparticles (MNPs) possess unique properties and hold great potential in biomedicine and clinical applications. With their magnetic properties and their ability to work at cellular and molecular level, MNP have been applied both in-vitro and in-vivo in targeted drug delivery and imaging. Focusing on Iron Oxide Superparamagnetic nanoparticles (SPIONs), this paper elaborates on the recent advances in development of hybrid polymeric-magnetic nanoparticles. Their main applications in drug delivery include Chemotherapeutics, Hyperthermia treatment, Radio-therapeutics, Gene delivary, and Biotheraputics. Physiochemical properties such as size, shape, surface and magnetic properties are key factors in determining their behavior. Additionally tailoring SPIONs surface is often vital for desired cell targetting and improved efficiency. Polymer coating is specifically reviewed with brief discussion of SPIONs administration routes. Commonly used drug release models for describing release mechanisms and the nanotoxicity aspects are also discussed. METHODS: This review focus on superparamagnetic nanoparticles coated with different types of polymers starting with the key physiochemical features that dominate their behavior. The importance of surface modification is addressed. Subsequently, the major classes of polymer modified iron oxide nanoparticles is demonstrated according to their clinical use and application. Clinically approved nanoparticles are then addressed and the different routes of administration are mentioned. Lastly, mathematical models of drug release profile of the common used nanoparticles are addressed. RESULTS: MNPs emerging in recent medicine are remarkable for both imaging and therapeutics, particularly, as drug carriers for their great potential in targeted delivery and cancer treatment. Targeting ability and biocompatibility can be improved though surface coating which provides a mean to alter the surface features including physical characteristics and chemical functionality. The use of biocompatible polymers can prevent aggregation, increase colloidal stability, evades nanoparticles uptake by RES, and can provide a surface for conjugation of targeting ligands such as peptide and biomolecules with high affinity to target cells. CONCLUSION: Great efforts to bring MNPs from lab testing stage to clinic are needed to understand their physicochemical properties and how they behave in vivo, which resulted in few of them to exist in the market today. Although magnetic nanoparticles have not yet fully reached their optimal safety and efficiency due to the challenges they face in vivo, their shortcomings can be overcome through improvement of magnetictargeted carrier by pre-clinical trials and continuous studies.
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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 | 0.001 |
| 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 teacher head, 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".