Evaluation of the biodistribution and preliminary safety profile of a novel brain-targeted manganese dioxide-based nanotheranostic system for Alzheimer’s disease
Bibliographic record
Abstract
A novel brain-targeted and reactive oxygen species-activatable manganese dioxide containing nanoparticle system functionalized with anti-amyloid-β antibody (named aAβ-BTRA-NC) developed by our group has shown great promise as a highly selective magnetic resonance imaging (MRI) contrast agent for early detection and multitargeted disease-modifying treatment of Alzheimer's disease (AD). To further evaluate the suitability of the formulation for future clinical application, we investigated the safety, biodistribution, and pharmacokinetic profile of aAβ-BTRA-NC in a transgenic TgCRND8 mouse AD model, wild type (WT) littermate, and CD-1 mice. Dose-ascending studies demonstrated that aAβ-BTRA-NC was well-tolerated by the animals up to 300 μmol Mn/kg body weight [b.w.], 3 times the efficacious dose for early AD detection without apparent adverse effects; Histopathological, hematological, and biochemical analyses indicated that a single dose of aAβ-BTRA-NC did not cause any toxicity in major organs. Immunotoxicity data showed that aAβ-BTRA-NC was safer than commercially available gadolinium-based MRI contrast agents at an equivalent dose of 100 μmol/kg b.w. of metal ions. Intravenously administered aAβ-BTRA-NC was taken up by main organs with the order of liver, kidneys, intestines, spleen, followed by other organs, and cleared after one day to one week post injection. Pharmacokinetic analysis indicated that the plasma concentration profile of aAβ-BTRA-NC followed a 2-compartmental model with faster clearance in the AD mice than in the WT mice. The results suggest that aAβ-BTRA-NC exhibits a strong safety profile as a nanotheranostic agent which warrants more robust preclinical development for future clinical 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 | 0.000 | 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 | 0.000 |
| 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 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".