P4–245: Nanobodies™ targeting amyloid beta as potential therapeutics for Alzheimer's disease
Bibliographic record
Abstract
Active and passive immunization strategies targeting amyloid beta (Aβ) are currently being developed as a potential therapy to treat Alzheimer's disease (AD). Both approaches have shown efficacy in animal models, while passive vaccination is expected to be safer in the clinical context. We have explored the use of Aβ targeting Nanobodies for passive immunotherapy of AD. Nanobodies are 12–15 kDa protein domains derived from camelid Heavy–Chain antibodies with unique characteristics that offer many advantages over the use of conventional monoclonal antibodies. By immunizing llamas with Aβ peptides and cloning the variable regions from the Heavy–Chain antibody pool of these animals, we have isolated Nanobodies that bind to soluble as well as to aggregated Aβ peptides. Since these small protein molecules are rapidly cleared in animals, they were genetically fused to an anti–murine albumin–binding Nanobody to increase the biological half life to approximately 2 days in mice. Since for therapeutic efficacy it is likely to be required that the brain Aβ is targeted as well, we have made multi–specific molecules that have the capability of crossing the blood brain barrier (BBB) (Muruganandam et al, 2002). In this way, systemically injected Nanobodies that are actively transported across the BBB and diffuse into the brain parenchyma may recognize amyloid plaques and/or soluble Aβ in the brain. Different versions of Aβ targeting Nanobodies fused to BBB crossing and/or albumin binding Nanobodies in bi– and trispecific formats are being evaluated in APP–[V717] transgenic mice for their efficacy in lowering brain amyloid peptide levels and in enhancement of cognition. In this program we have demonstrated the flexible use of the Nanobody platform to combine multiple functions into the same molecule that can be readily expressed in microbial systems. The monomeric Aβ Nanobodies can serve as building blocks to design new formats that ultimately can be developed for AD therapy and/or diagnosis. Comparison of these different Nanobodies will yield insight into whether enhancing the BBB–crossing of AD immunotherapeutics increases their efficacy.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".