Antibody Capture of Soluble Aβ Does Not Reduce Cortical Aβ Amyloidosis in the PDAPP Mouse
Bibliographic record
Abstract
BACKGROUND: In vivo administration of antibodies against the amyloid-beta (Abeta) peptide has been shown to reduce and reverse the progressive amyloidosis that develops in a variety of mouse models of Alzheimer's disease (AD). This work has been extended to clinical trials where subsequent autopsy cases of AD subjects immunized against Abeta showed similar reductions in parenchymal amyloid plaques, suggesting this approach to reduce neuropathology in man is feasible. OBJECTIVE: Multiple hypotheses have been advanced to explain how anti-Abeta antibodies may lower amyloid burden. In this report, we compare approaches utilizing either plaque-binding or peptide-capturing anti-Abeta antibodies for effectiveness in reducing amyloidosis in a mouse model of AD. METHODS: A plaque-binding monoclonal antibody (3D6) and an Abeta peptide-capturing monoclonal antibody (266) were compared in chronic treatment and prevention paradigms using a transgenic mouse model of AD. The effects of antibody therapy on plaque burden and plasma clearance of Abeta were investigated by quantitative imaging and clearance studies of intravenously injected (125)I-Abeta. RESULTS: The plaque-binding antibody 3D6 was highly effective in either treatment or prevention of amyloidosis. In these studies, the peptide-capture antibody 266 showed no reduction in amyloidosis in either paradigm and showed trends towards increasing amyloidosis. Antibody 266 was also found to greatly prolong (>180-fold) the normally rapid peripheral clearance of Abeta, in contrast to that found with 3D6 (>24-fold). CONCLUSION: Reversing and preventing Alzheimer's type amyloidosis is most effectively accomplished with anti-amyloid antibodies that avidly bind plaque.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".