Antioxidant Therapy in Oxidative Stress-Induced Neurodegenerative Diseases: Role of Nanoparticle-Based Drug Delivery Systems in Clinical Translation
Bibliographic record
Abstract
Free radicals are formed as a part of normal metabolic activities but are neutralized by the endogenous antioxidants present in cells/tissue, thus maintaining the redox balance. This redox balance is disrupted in certain neuropathophysiological conditions, causing oxidative stress, which is implicated in several progressive neurodegenerative diseases. Following neuronal injury, secondary injury progression is also caused by excessive production of free radicals. Highly reactive free radicals, mainly the reactive oxygen species (ROS) and reactive nitrogen species (RNS), damage the cell membrane, proteins, and DNA, which triggers a self-propagating inflammatory cascade of degenerative events. Dysfunctional mitochondria under oxidative stress conditions are considered a key mediator in progressive neurodegeneration. Exogenous delivery of antioxidants holds promise to alleviate oxidative stress to regain the redox balance. In this regard, natural and synthetic antioxidants have been evaluated. Despite promising results in preclinical studies, clinical translation of antioxidants as a therapy to treat neurodegenerative diseases remains elusive. The issues could be their low bioavailability, instability, limited transport to the target tissue, and/or poor antioxidant capacity, requiring repeated and high dosing, which cannot be administered to humans because of dose-limiting toxicity. Our laboratory is investigating nanoparticle-mediated delivery of antioxidant enzymes to address some of the above issues. Apart from being endogenous, the main advantage of antioxidant enzymes is their catalytic mechanism of action; hence, they are significantly more effective at lower doses in detoxifying the deleterious effects of free radicals than nonenzymatic antioxidants. This review provides a comprehensive analysis of the potential of antioxidant therapy, challenges in their clinical translation, and the role nanoparticles/drug delivery systems could play in addressing these challenges.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".