The Oral Tissue Transglutaminase Inhibitor Zed1227 Prevents Gluten-Induced Enteropathy In A Humanized Mouse Model Of Celiac Disease
Bibliographic record
Abstract
Einleitung Celiac disease (CeD) is triggered by gluten peptides that escape intestinal digestion and are bound to HLA-DQ2 or -DQ8 in the small intestinal lamina propria. The CeD autoantigen tissue transglutaminase (TG2) converts certain glutamines in these peptides, which improves their binding to both HLAs, enhancing the gluten-specific T cell response, resulting in villous atrophy and intraepithelial (cytotoxic) lymphocytosis (IEL). Ziele We developed and tested ZED1227, an oral inhibitor of TG2, in a mouse model of small intestinal inflammation induced by poly-IC and in humanized transgenic CeD mice model (NOD-DQ8 mice) that develop mild features of human CeD. Methodik Mice received intraperitoneal poly-IC together with 50 or 150 mg oral ZED1227/kg vs vehicle 2 h before sacrifice. Gluten-sensitized NOD DQ8 mice fed a gluten-containing or gluten-free diet for 3 weeks received daily oral gavages of ZED1227 vs vehicle for the last week. Ergebnis ZED1227 completely blocked TG2 activity in the poly:IC model. In NOD-DQ8 mice, ZED1227 prevented gluten-induced villous atrophy, IEL, increases in CD45, CD3, CD8, CD68 and Ki67 positive cells, and decreased pro-inflammatory transcripts and serum levels of CeD specific antibodies. Schlussfolgerung 1.Oral ZED1227 effectively blocked TG2 activity in vivo and attenuated CeD in our transgenic NOD-DQ8 mouse model. 2.The NOD-DQ8 model predicted therapeutic efficacy of ZED1227 that was later demonstrated in a phase 2a clinical trial of 160 CeD patients in remission who were challenged with gluten (Schuppan et al, NEJM 2021). Publication History Article published online: 19 August 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".