Drug repurposing approach for the discovery of therapeutic agents for Crohn’s disease-associated intestinal fibrosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Intestinal fibrosis in Crohn's disease (CD) frequently leads to stricture formation, with current treatment options limited to endoscopic balloon dilation and surgery. This underscores the urgent need for anti-fibrotic therapies. Our objective was to identify therapeutic targets and compounds capable of reversing the fibrotic gene expression profile of mucosal fibroblasts in CD. METHODS: We derived a fibrotic gene signature via fibroblasts isolated from stricturing CD tissue and conducted a meta-regression analysis across three publicly available transcriptomic datasets, to identify key differentially expressed genes (DEGs) in fibrostenotic CD. Drug repurposing platforms (iLINCS, L1000, CLUE-io) were implemented to screen compounds with high druggability, for their potential to reverse this pro-fibrotic profile. Transcription factors, microRNAs, and drugs targeting the fibrostenotic signature were identified using the TRRUST, miRWalk, and DGIdb databases, ultimately forming a drug-gene interaction network. The STITCH platform was used to predict compound-protein binding affinities. Promising compounds were subsequently evaluated in vitro, using mucosal fibroblasts derived from fibrostenotic CD patients, and the effect on the expression of selected protein targets was measured via ELISA and immunofluorescence staining. RESULTS: The top upregulated DEGs included fibroblast activation protein (FAP), IL-7 receptor, and transcription factor AP-2 gamma. The drug-gene interaction network analysis identified IL-6 among the most druggable targets. Of 6783 pharmaceutical agents, PI3K inhibitors and histone deacetylase blockers were the most effective in reversing the fibrotic signature via a FAP- and IL-6-dependent mechanism. CONCLUSION: This integrative approach identified potential anti-fibrotic compounds and molecular targets in CD-associated fibrostenosis, supporting future development of effective therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".