Protein misfolding‐specific epitope identification for passive and active immunotherapy of neurodegenerative diseases
Bibliographic record
Abstract
BACKGROUND: Toxic misfolded proteins underlie the pathogenesis of neurodegenerative diseases such as Alzheimer's and Parkinson's disease (AD&PD), and amyotrophic lateral sclerosis/frontotemporal dementia (ALS/FTD). Generation of therapeutic antibodies selectively targeting only disease-misfolded isoforms, while sparing normal or irrelevant isoforms, has not yet been successfully achieved by conventional immunization strategies. METHOD: ProMIS Neurosciences has developed a computational platform to identify conformational epitopes that are uniquely exposed on toxic misfolded proteins, which can then be used to generate misfolding-specific antibodies or vaccine formulations. RESULT: Application of the ProMIS platform produced PMN310, a clinical-stage, humanized monoclonal antibody highly selective for Abeta oligomers without significant reactivity with Abeta monomers or fibrils, thereby avoiding target distraction by these more abundant species, and reducing the risk of brain edema and microhemorrhages associated with the targeting of vascular/parenchymal amyloid. Similarly, specific epitopes for alpha-synuclein toxic oligomers/soluble fibrils that drive synucleinopathies, and for pathogenic TDP-43 in ALS/FTD have been identified and lead candidate antibodies generated. The small size and precise conformation of these epitopes have been translated into vaccines, enabling the specific targeting of pathogenic molecular species in preclinical models. CONCLUSION: ProMIS has circumvented the specificity limitations of conventional immunizations to enable selective passive and active immunotherapies for neurodegenerative diseases.
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 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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".