The mechanism of GM-CSF inhibition by human GM-CSF auto-antibodies suggests novel therapeutic opportunities
Bibliographic record
Abstract
Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a hematopoietic growth factor that can stimulate a variety of cells, but its overexpression leads to excessive production and activation of granulocytes and macrophages with many pathogenic effects. This cytokine is a therapeutic target in inflammatory diseases, and several anti-GM-CSF antibodies have advanced to Phase 2 clinical trials in patients with such diseases, e.g., rheumatoid arthritis. GM-CSF is also an essential factor in preventing pulmonary alveolar proteinosis (PAP), a disease associated with GM-CSF malfunction arising most typically through the presence of GM-CSF neutralizing auto-antibodies. Understanding the mechanism of action for neutralizing antibodies that target GM-CSF is important for improving their specificity and affinity as therapeutics and, conversely, in devising strategies to reduce the effects of GM-CSF auto-antibodies in PAP. We have solved the crystal structures of human GM-CSF bound to antigen-binding fragments of two neutralizing antibodies, the human auto-antibody F1 and the mouse monoclonal antibody 4D4. Coordinates and structure factors of the crystal structures of the GM-CSF:F1 Fab and the GM-CSF:4D4 Fab complexes have been deposited in the RCSB Protein Data Bank under the accession numbers 6BFQ and 6BFS, respectively. The structures show that these antibodies bind to mutually exclusive epitopes on GM-CSF; however, both prevent the cytokine from interacting with its alpha receptor subunit and hence prevent receptor activation. Importantly, identification of the F1 epitope together with functional analyses highlighted modifications to GM-CSF that would abolish auto-antibody recognition whilst retaining GM-CSF function. These results provide a framework for developing novel GM-CSF molecules for PAP treatment and for optimizing current anti-GM-CSF antibodies for use in treating inflammatory disorders.
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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 | 0.001 |
| 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".