Revolutionizing Neurological Therapies: The Multifaceted Potential of Zein-Based Nanoparticles for Brain-Targeted Drug Delivery
Bibliographic record
Abstract
Zein nanoparticles (ZNPs) have gained significant attention as biocompatible and biodegradable nanocarrier systems for brain-targeted drug delivery. Their ability to encapsulate hydrophobic drugs and undergo surface modifications enables effective blood-brain barrier (BBB) penetration through receptor-mediated transcytosis. Functionalization approaches, such as PEGylation and ligand conjugation, have been explored to enhance BBB transport and improve drug bioavailability. Additionally, ZNPs hold the potential for theranostic applications, integrating drug delivery with real-time diagnostics to facilitate personalized treatment strategies. This review provides a comprehensive evaluation of ZNPs, discussing their fabrication techniques, surface modifications, and transport mechanisms across the BBB. A comparative analysis with other nanocarrier systems (liposomes, polymeric NPs, dendrimers, lipid-based NPs, and carbon nanotubes) highlights their superior biodegradability, lower toxicity, and potential for clinical translation. Furthermore, we explore the latest advancements in ZNP-based drug delivery for neurological disorders, including glioblastoma, Parkinson's, and Alzheimer's. Despite their advantages, the clinical translation of ZNPs remains challenging due to scalability issues, batch-to-batch variability, and regulatory constraints. Future research should optimize functionalization strategies, enhance drug release kinetics, and conduct long-term safety evaluations. With continued advancements in nanotechnology and pharmaceutical engineering, ZNPs represent a promising, sustainable platform for improving brain-targeted therapeutic interventions.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".