Generation of anti-human MAdCAM-1 antibodies and bispecifics as gut-targeted anti-inflammatory biologics 3100
Bibliographic record
Abstract
Abstract Description Inflammatory bowel disease (IBD) is a term that encompasses highly prevalent conditions characterized by chronic sites of inflammation present along the gastrointestinal track. The local delivery of biologics to manage symptoms associated with IBD would improve the quality of life of patients by limiting off-target events. Here, we report the development of monoclonal antibodies that target the human Mucosal Addressin-Cell Adhesion Molecule 1 (MAdCAM-1), a cell surface antigen predominantly expressed on endothelial cells of venules located in the lamina propria of the small intestine and colon. We postulate that these antibodies and related bispecifics harboring a therapeutic protein domain represent an attractive strategy to focus biologics to inflamed areas of the gut. As such, mAbs derived from eight hybridoma clones, bound to human MAdCAM-1 with dissociation constants in the low to sub-nanomolar range. Four of these 8 mAbs were able to detect MAdCAM-1 on the venules of human small intestinal tissue by immunohistochemistry and blocked MAdCAM-1 co-stimulation of primary human CD4+ T-cells, causing a shift in their ex vivo differentiation from central memory (CD45RA-, CD27+) cells to a more differentiated CD45RA–CD27- phenotype. These antagonistic antibodies as well as bispecifics linking a scFv derived from one of these mAbs to known biologics may represent potential therapeutic reagents for the localized treatment of gastrointestinal inflammation. Funding Sources Supported by the Canadian Institutes of Health Research grant number PJT 180293 Topic Categories Therapeutic Approaches to Autoimmunity (THER)
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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".