Specific targeting of the IL-23 receptor, using a novel small peptide noncompetitive antagonist, decreases the inflammatory response
Bibliographic record
Abstract
IL-23 is part of the IL-12 family of cytokines and is composed of the p19 subunit specific to IL-23 and the p40 subunit shared with IL-12. IL-23 specifically contributes to the inflammatory process of multiple chronic inflammatory autoimmune disorders, including psoriasis, multiple sclerosis, inflammatory bowel disease, and rheumatoid arthritis. So far, one antibody targeting the shared p40 subunit of IL-12 and IL-23, Ustekinumab, is approved clinically to treat psoriasis. However, there are no treatments inhibiting specifically the IL-23 proinflammatory response. We have developed small IL-23R-specific antagonists by designing all D-peptides arising from flexible regions of IL-23R. Of these peptides, we selected 2305 (teeeqqly), since in addition to its soluble properties, it inhibited IL-23-induced STAT3 phosphorylation in spleen cells. Peptide 2305 specifically binds to IL-23R/IL-12Rβ1-expressing HEK-293 cells and not to cells devoid of the receptor. Peptide 2305 showed functional selectivity by modulating IL-23-induced gene expression in IL-23R/IL-12Rβ1-expressing cells and in Jurkat cells; 2305 does not inhibit IL-12-induced cytokine expression in IL-12Rβ-IL-12Rβ2-HEK-293 cells. Finally, compared with anti-p40 treatment, 2305 effectively and selectively inhibits IL-23-induced inflammation in three in vivo mouse models: IL-23-induced ear inflammation, anti-CD40-induced systemic inflammatory response, and collagen-induced arthritis. We, hereby, describe the discovery and characterization of a potent IL-23R small-peptide modulator, 2305 (teeeqqly), that is effective in vivo. 2305 may be more convenient, less cumbersome, less costly, and most importantly, more specific than current biologics for the treatment of inflammatory conditions, and conceivably complement the actual therapies for these chronic and debilitating inflammatory diseases.
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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) | 0.001 | 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".