Understanding the impact of risankizumab on keratinocyte-derived IL-23A in a novel organotypic 3D skin model containing IL-23A responsive and IL-17A producing γδ-T-cells
Bibliographic record
Abstract
PURPOSE: To study the effects of the anti-IL-23A antibody risankizumab on the IL-36γ/IL-23A/IL-17A signalling cascade we used a newly developed 3D skin model consisting of primary human keratinocytes, fibroblasts and γδ-T-cells. METHODS: study we developed new full-thickness 3D skin models containing normal human epidermal keratinocytes (NHEK), normal human dermal fibroblasts (NHDF) and IL-23A responsive and IL-17A producing γδ-T-cells. The effects of IL-36γ stimulation with and without risankizumab treatment on IL-23A and IL-17A expression were examined at the RNA and protein levels. RESULTS: In preliminary monolayer experiments stimulation of γδ-T-cells with IL-23A promoted the IL-17A expression that was inhibited after risankizumab treatment. Using 3D skin models containing γδ-T-cells, we found that stimulation with IL-36γ significantly increased not only IL-23A but also IL-17A expression. These effects were inhibited by concomitant treatment with risankizumab. CONCLUSIONS: Our results showed that blockade of IL-23A has inhibitory effects on the IL-36γ/IL-23A feedforward loop. Our newly developed 3D skin model containing IL-23A responsive and IL-17A producing γδ-T-cells enables molecular analysis of targeted therapies aimed at the IL-36γ/IL-23A/IL-17A signalling cascade in psoriasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".