Inhibition of Wnt/β-catenin/CREB binding protein (CBP) signaling reverses pulmonary fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF)/usual interstitial pneumonia is a ravaging condition of progressive lung scarring and destruction. Anti-inflammatory therapies including corticosteroids have limited efficacy in this ultimately fatal disorder. An important unmet need is to identify new agents that interact with key molecular pathways involved in the pathogenesis of pulmonary fibrosis to prevent progression or reverse fibrosis in these patients. Because aberrant activation of the Wnt/beta-catenin signaling cascade occurs in lungs of patients with IPF, we have targeted this pathway for intervention in pulmonary fibrosis using ICG-001, a small molecule that specifically inhibits T-cell factor/beta-catenin transcription in a cyclic AMP response-element binding protein binding protein (CBP)-dependent fashion. ICG-001 selectively blocks the beta-catenin/CBP interaction without interfering with the beta-catenin/p300 interaction. We report here that ICG-001 (5 mg/kg per day) significantly inhibits beta-catenin signaling and attenuates bleomycin-induced lung fibrosis in mice, while concurrently preserving the epithelium. Administration of ICG-001 concurrent with bleomycin prevents fibrosis, and late administration is able to reverse established fibrosis and significantly improve survival. Because no effective treatment for IPF exists, selective inhibition of Wnt/beta-catenin-dependent transcription suggests a potential unique therapeutic approach for pulmonary fibrosis.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".