PBI-compound, a novel first-in-class anti-fibrotic compound, reduces lung fibrosis in the bleomycin-induced lung fibrosis model: A comparative study with pirfenidone
Bibliographic record
Abstract
Background : The pathobiological mechanisms underlying the development of idiopathic pulmonary fibrosis (IPF) are highly complex. Aims : To compare the effect of PBI-Compound, pirfenidone, and combination of both compounds on inflammatory/fibrotic mRNA expression of key mediators (inflammation/pro-fibrotic: TGF-β, CTGF, IL 6, IL23p19; fibrotic: collagen I, fibronectin; and remodeling: SPARC, MMP-2), and on histological lesions in the bleomycin-induced lung fibrosis model. Methods : Intratracheal instillation of bleomycin was administered on day 0. Mice were treated with PBI-Compound, or pirfenidone, or combination of both compounds starting on day 7 to 21. Results : Bleomycin induced a significant increase in mRNA expression of all key mediators in the lung. PBI-Compound and combination therapy significantly decreased TGF-β, CTGF, IL-6 and IL23p19 expression in lung, while pirfenidone had no effect on CTGF. All treatments induced a significant reduction of collagen I and fibronectin expression to the control level (no bleomycin). PBI-Compound and combination therapy induced a significant reduction of SPARC while pirfenidone demonstrated no effect. PBI-Compound and pirfenidone induced a weak inhibition of MMP-2 while the combination therapy induced a significant reduction. Based on Ashcroft’s score, PBI-Compound and combination therapy demonstrated significant reduction of lung fibrosis while pirfenidone alone demonstrated weak but no significant activity at the histological level. Conclusions : PBI-Compound or combination therapy with pirfenidone may be an efficacious treatment in IPF.
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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 | 0.000 |
| 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) | 0.002 | 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".