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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".