Macitentan reduces progression of TGF-β1-induced pulmonary fibrosis and pulmonary hypertension
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive disease with an unknown cause. Two drugs, nintedanib and pirfenidone, have been shown to slow, but not stop, disease progression. Pulmonary hypertension (PH) is a frequent complication in IPF patients and is associated with poor prognosis. Macitentan is a dual endothelin receptor antagonist that is approved for pulmonary arterial hypertension treatment. We hypothesised that using macitentan to treat animals with pulmonary fibrosis induced by adenoviral vector encoding biologically active transforming growth factor-β1 (AdTGF-β1) would improve the PH caused by chronic lung disease and would limit the progression of fibrosis. Rats (Sprague Dawley) which received AdTGF-β1 were treated by daily gavage of macitentan (100 mg·kg−1·day−1), pirfenidone (0.5% food admix) or a combination from day 14 to day 28. Pulmonary artery pressure (PAP) was measured before the rats were killed, and fibrosis was subsequently evaluated by morphometric measurements and hydroxyproline analysis. AdTGF-β1 induced pulmonary fibrosis associated with significant PH. Macitentan reduced the increase in PAP and both macitentan and pirfenidone stopped fibrosis progression from day 14 to day 28. Macitentan protected endothelial cells from myofibroblast differentiation and apoptosis whereas pirfenidone only protected against fibroblast-to-myofibroblast differentiation. Both drugs induced apoptosis of differentiated myofibroblastsin vitroandin vivo. Our results demonstrate that dual endothelin receptor antagonism was effective in both PH and lung fibrosis whereas pirfenidone only affected 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".