A Multi‐Scale Mechanistic Model of Ulcerative Colitis to Investigate the Effects of Selective Suppression of <scp>IL</scp> ‐6 Trans‐Signaling
Bibliographic record
Abstract
Interleukin 6 (IL-6) has previously been identified as playing a role in ulcerative colitis (UC) by activating the signal-transducing element gp130 through ligation of either the membrane-bound or soluble IL-6 receptor (termed classic and trans-signaling respectively). It has been proposed that selective inhibition of trans-IL-6 signaling could ameliorate the deleterious, pro-inflammatory effects of IL-6, while preserving the homeostatic activity of classic IL-6 signaling. We developed an in silico, mechanistic model of UC in two stages to compare the biological effects that result from inhibition of classic and trans-IL-6 signaling. In the first stage, we developed a limited-scope model of IL-6 signaling to establish the quantitative properties of classic and trans-signaling pathways on a short timescale following stimulation with IL-6. The model included both a pan-inhibitor of IL-6 classic and trans-signaling and a soluble gp130-Fc that selectively inhibited trans-signaling. In the second stage, we developed a multi-scale model of UC to study the pharmacodynamic effects of cytokine signaling inhibition and optimize treatment regimens. Across three virtual experiments, both selective and global suppression of IL-6 signaling were associated with a transition away from an inflammatory state in patients with moderate to severe inflammatory activity. In our multi-scale model, we identified a dose-response relationship between selective inhibition of trans-IL-6 signaling and tissue regeneration. Moreover, selective inhibition of trans-IL-6 signaling effectively suppressed inflammation and induced faster gut tissue healing than global IL-6 suppression. These findings suggest that global suppression of IL-6 signaling could negatively affect IL-6-induced regeneration activity, whereas this effect is less likely for selective inhibition.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".