Guselkumab binding to CD64+ IL-23–producing myeloid cells enhances potency for neutralizing IL-23 signaling
Bibliographic record
Abstract
IL-23 is implicated in the pathogenesis of immune-mediated inflammatory diseases, and myeloid cells that express Fc gamma receptor 1 (FcγRI or CD64) on their surface have been recently identified as a primary source of IL-23 in inflamed tissue. Our complementary analyses of transcriptomic datasets from psoriasis and IBD showed increased expression of CD64 and IL-23 transcripts in inflamed tissue, and greater abundance of cell types with co-expression of CD64 and IL-23. These findings led us to explore potential implications of CD64 binding on the function of IL-23–targeting monoclonal antibodies (mAbs). Guselkumab and risankizumab are mAbs that target the IL-23p19 subunit. Guselkumab has a native Fc domain while risankizumab contains mutations that diminish binding to FcγRs. In flow cytometry assays, guselkumab, but not risankizumab, showed Fc-mediated binding to CD64 on IFNγ-primed monocytes. Guselkumab bound CD64 on IL-23–producing inflammatory monocytes and simultaneously captured IL-23 secreted from these cells. Guselkumab binding to CD64 did not induce cytokine production. In live-cell confocal imaging of CD64 + macrophages, guselkumab, but not risankizumab, mediated IL-23 internalization to low-pH intracellular compartments. Guselkumab and risankizumab demonstrated similar potency for inhibition of IL-23 signaling in cellular assays with exogenous addition of IL-23. However, in a co-culture of IL-23–producing CD64 + THP-1 cells with an IL-23–responsive reporter cell line, guselkumab demonstrated Fc-dependent enhanced potency compared to risankizumab for inhibiting IL-23 signaling. These in vitro data highlight the potential for guselkumab binding to CD64 in inflamed tissue to contribute to the potent neutralization of IL-23 at its cellular source.
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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) | 0.002 | 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".