[O2–14–03]: TARGETING OF TOXIC AMYLOID‐BETA OLIGOMER SPECIES BY MONOCLONAL ANTIBODY PMN310: PRECISION DRUG DESIGN FOR ALZHEIMER's DISEASE
Bibliographic record
Abstract
Current evidence suggests that progressive neurodegeneration in Alzheimer's disease (AD) may be catalyzed by the prion-like propagation of soluble toxic amyloid-beta oligomers (AβO) rather than plaque burden. Binding of Aβ monomers and/or fibrils by therapeutic antibodies has been associated with suboptimal efficacy and adverse events (e.g. ARIA-E) in clinical trials. These observations suggest that antibodies capable of specifically or selectively neutralizing toxic AβO are needed to achieve improved efficacy and safety. Computational algorithms were developed to identify epitope sequences and conformations likely to be exposed in toxic AβO but not on monomers or fibrils. The binding profile of monoclonal antibodies raised against predicted epitopes was assessed by surface plasmon resonance (SPR) analysis and immunohistochemistry (IHC). Inhibition of oligomer propagation was assessed by thioflavin-T fluorescence in vitro. Neutralization of AβO neurotoxicity by the antibodies was evaluated in cultures of primary neurons in vitro and in wild-type mice injected intracerebroventricularly with AβO in vivo. Screening of IgG clones raised against 5 distinct AβO epitopes yielded antibodies with the desired profile of selective binding to synthetic oligomers vs monomers, preferential binding to native soluble AβO in CSF and brain extracts of AD patients compared to controls by SPR, and lack of plaque reactivity by IHC on unfixed AD brain sections. Activity assays led to the identification of PMN310 as a lead antibody capable of blocking the propagation and neurotoxicity of AβO in vitro and protecting mice against the AβO-induced loss of short term memory formation in vivo. Computational modeling allowed for the identification and generation of monoclonal antibodies against AβO-specific epitopes. Antibody PMN310 was selected as a lead candidate on the basis of its ability to selectively target and neutralize AβO with no significant cross-reactivity to monomers or fibrils. These unique characteristics distinguish PMN310 from Aβ-reactive antibodies currently undergoing clinical trials, and are designed to address observed issues of efficacy and safety.
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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) | 0.001 | 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".