Mode of action of nintedanib in the treatment of idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal disease characterised by fibrosis of the lung parenchyma and loss of lung function. Although the pathogenic pathways involved in IPF have not been fully elucidated, IPF is believed to be caused by repetitive alveolar epithelial cell injury and dysregulated repair, in which there is uncontrolled proliferation of lung fibroblasts and differentiation of fibroblasts into myofibroblasts, which excessively deposit extracellular matrix (ECM) proteins in the interstitial space. A number of profibrotic mediators including platelet-derived growth factor (PDGF), fibroblast growth factor (FGF) and transforming growth factor-β are believed to play important roles in the pathogenesis of IPF. Nintedanib is a potent small molecule inhibitor of the receptor tyrosine kinases PDGF receptor, FGF receptor and vascular endothelial growth factor receptor. Data from in vitro studies have shown that nintedanib interferes with processes active in fibrosis such as fibroblast proliferation, migration and differentiation, and the secretion of ECM. In addition, nintedanib has shown consistent anti-fibrotic and anti-inflammatory activity in animal models of lung fibrosis. These data provide a strong rationale for the clinical efficacy of nintedanib in patients with IPF, which has recently been demonstrated in phase III clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".