Brain Drug Delivery- An Updated Review Article for Mechanism and Recent Technologies
Bibliographic record
Abstract
Background: Effective brain drug delivery remains a significant challenge due to the restrictive nature of the blood-brain barrier (BBB). This review article examines the mechanisms and recent advancements in brain drug delivery systems, addressing the challenges and factors influencing successful therapeutic interventions. Objective: To provide a comprehensive overview of current technologies and strategies for enhancing drug delivery to the brain, with a focus on nasal-to-brain delivery systems. Methods: We analyzed recent literature on various drug delivery systems, including nanoparticle-based formulations, liposomal technologies, and chemical permeation enhancers. We also evaluated the impact of factors such as drug properties, formulation characteristics, and physiological conditions on delivery efficacy. Results: Advances in drug delivery systems have demonstrated significant progress in overcoming the BBB. Nanoparticle-based systems and liposomal formulations have shown enhanced permeability and targeted delivery capabilities. The nasal-to-brain route has emerged as a promising non-invasive strategy, utilizing the olfactory and trigeminal nerve pathways to facilitate direct access to the central nervous system. Formulations utilizing mucoadhesive polymers have improved drug solubility, stability, and residence time in the nasal cavity, leading to increased therapeutic effectiveness. Conclusion: Understanding the mechanisms influencing brain drug delivery is crucial for developing effective treatments for neurological disorders. This review highlights the critical advancements in delivery systems, emphasizing the need for ongoing research to optimize strategies that enhance drug penetration across the BBB. Future studies should focus on refining these technologies to improve patient outcomes and address the growing burden of central nervous system diseases
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".