Evaluation of Novel B1R/B2R Agonists Containing TRIOZAN™ Nanoparticles for Targeted Brain Delivery of Antibodies in a Mouse Model of Alzheimer Disease
Bibliographic record
Abstract
The blood-brain barrier (BBB) is a major obstacle to the development of effective therapeutics for central nervous system (CNS) disorders, including Alzheimer's disease (AD). This has been particularly true in the case of monoclonal antibody (mAbs) therapeutic candidates, due to their large size. To tackle this issue, we developed new nanoformulations, comprising bio-based Triozan polymers along with kinin B1 and B2 receptor (B1R and B2R) peptide agonist analogues, as potent BBB-permeabilizers to enhance brain delivery of a new anti-C1q mAb for AD (ANX005). The prepared B1R/B2R-TRIOZAN™ nanoparticles (NPs) displayed aqueous solubility, B1R/B2R binding capacity and uniform sizes (~130-165 nm). The relative biodistribution profiles of the mAb loaded into these NPs versus the naked mAb were assessed in vivo through two routes of administrations (intravenous (IV), intranasal (IN)) in the Tg-SwDI mouse model of AD. At 24 h post-administration, brain levels of the encapsulated mAb were significantly increased (up to 12-fold (IV) and 5-fold (IN), respectively) compared with free mAb in AD brain affected regions, entorhinal cortex and hippocampus of aged mice. Liver uptakes remained relatively low with similar values for the nanoformulations and free mAb. Our findings demonstrate the potential of B1R/B2R-TRIOZAN™ NPs for the targeted delivery of new CNS drugs, which could maximize their therapeutic effectiveness.
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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 | 0.001 | 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".