A novel human anti-interleukin-1β neutralizing monoclonal antibody showing in vivo efficacy
Bibliographic record
Abstract
The pro-inflammatory cytokine interleukin (IL)-1β is a clinical target in many conditions involving dysregulation of the immune system; therapeutics that block IL-1β have been approved to treat diseases such as rheumatoid arthritis (RA), neonatal onset multisystem inflammatory diseases, cryopyrin-associated periodic syndromes, active systemic juvenile idiopathic arthritis. Here, we report the generation and engineering of a new fully human antibody that binds tightly to IL-1β with a neutralization potency more than 10 times higher than that of the marketed antibody canakinumab. After affinity maturation, the derived antibody shows a>30-fold increased affinity to human IL-1β compared with its parent antibody. This anti-human IL-1β IgG also cross-reacts with mouse and monkey IL-1β, hence facilitating preclinical development. In a number of mouse models, this antibody efficiently reduced or abolished signs of disease associated with IL-1β pathology. Due to its high affinity for the cytokine and its potency both in vitro and in vivo, we propose that this novel fully human anti-IL-1β monoclonal antibody is a promising therapeutic candidate and a potential alternative to the current therapeutic arsenal.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".