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Record W4312086640 · doi:10.1002/alz.067371

Distinguishing between amyloid‐beta‐directed antibodies: Ability of PMN310 to target toxic oligomers despite competing species

2022· article· en· W4312086640 on OpenAlexaff
Ebrima Gibbs, Juliane Coutts, Andrei Roman, Beibei Zhao, Johanne Kaplan, Neil R. Cashman

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsAmorfix (Canada)University of British Columbia
Fundersnot available
KeywordsOligomerMonomerAntibodyChemistrySurface plasmon resonanceAmyloid (mycology)Amyloid betaFibrilBiochemistryBiophysicsPeptideBiologyImmunologyPolymerPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract Background A large body of evidence indicates that the most pathogenic species of Abeta in Alzheimer’s disease (AD) consist of soluble toxic oligomers as opposed to insoluble fibrils and monomers. The ability of a therapeutic antibody to target toxic Abeta oligomers without being diverted by binding to competing non‐toxic species is expected to result in greater efficacy. This is supported by clinical results to date showing poor efficacy of non‐selective antibodies but improved success with partially selective antibodies that bind oligomers and plaque. As a next generation antibody, PMN310 is a therapeutic candidate designed to more selectively target toxic oligomers. Avoiding interaction with plaque and vascular deposits has the additional advantage of potentially decreasing the incidence of amyloid‐related imaging abnormalities (ARIA). In this study, PMN310 was compared to other Abeta antibodies for selectivity and ability to maintain interaction with toxic oligomers in the presence of competing monomers. Methods The binding of multiple Abeta‐directed antibodies (PMN310 and biosimilars donanemab, aducanumab, lecanemab, crenezumab, solanezumab) to synthetic oligomers, with and without pre‐exposure to competing monomers, was evaluated by surface plasmon resonance (SPR). Binding of the antibodies to a toxic oligomer‐enriched low molecular fraction of brain extract from AD patients was similarly evaluated by SPR, with and without monomer competition. Binding to insoluble Abeta deposits was examined by immunohistochemistry (IHC) of AD brain sections. Results PMN310 showed little or no interaction with monomers and was among the least impacted by excess monomer competition in binding to synthetic oligomers or naturally occurring toxic oligomers in AD brain extract. This characteristic was shared by other Abeta antibodies that have shown positive clinical outcomes. Non‐selective antibodies that failed in the clinic were strongly inhibited by monomer competition. In contrast to other Abeta antibodies, PMN310 additionally avoided interaction with plaque and vascular deposits as determined by IHC. Conclusions PMN310 distinguishes itself from other Abeta antibodies by its enhanced selectivity for toxic oligomers (negligible binding to monomers and plaque) along with its ability to withstand competition by abundant monomers. Additionally, the avoidance of interaction with plaque and vascular deposits by PMN310 could potentially reduce the risk of ARIA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.312
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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