Superparamagnetic iron oxide nanoparticles for delivery of therapeutic agents: opportunities and challenges
Bibliographic record
Abstract
INTRODUCTION: Bearing in mind that many promising drug candidates have the problem of reaching their target site, the concept of advanced drug delivery can play a significant complementary role in shaping modern medicine. Among other nanoscale drug carriers, superparamagnetic iron oxide nanoparticles (SPIONs) have shown great potential in nanomedicine. The intrinsic properties of SPIONs, such as inherent magnetism, broad safety margin and the availability of methods for fabrication and surface engineering, pave the way for diverse biomedical applications. SPIONs can achieve the highest drug targeting efficiency among carriers, since an external magnetic field locally applied to the target organ enhances the accumulation of magnetic nanoparticles in the drug site of action. Moreover, theranostic multifunctional SPIONs make simultaneous delivery and imaging possible. In spite of these favorable qualities, there are some toxicological concerns, such as oxidative stress, unpredictable cellular responses and induction of signaling pathways, alteration in gene expression profiles and potential disturbance in iron homeostasis, that need to be carefully considered. Besides, the protein corona at the surface of the SPIONs may induce few shortcomings such as reduction of SPIONs targeting efficacy. AREAS COVERED: In this review, we will present recent developments of SPIONs as theranostic agents. The article will further address some barriers on drug delivery using SPIONs. EXPERT OPINION: One of the major success determinants in targeted in vivo drug delivery using SPIONs is the adequacy of magnetic gradient. This can be partially achieved by using superconducting magnets, local implantation of magnets and application of magnetic stents. Other issues that must be considered include the pharmacokinetics and in vivo fate of SPIONs, their biodegradability, biocompatibility, potential side effects and the crucial impact of protein corona on either drug release profile or mistargeting. Surface modification of SPIONs can open up the possibility of drug delivery to intracellular organelles, drug delivery across the blood-brain barrier, modifying metabolic diseases and a variety of other multimodal and/or theranostic applications.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".