Potential of nintedanib in treatment of progressive fibrosing interstitial lung diseases
Bibliographic record
Abstract
A proportion of patients with fibrosing interstitial lung diseases (ILDs) develop a progressive phenotype characterised by decline in lung function, worsening quality of life and early mortality. Other than idiopathic pulmonary fibrosis (IPF), there are no approved drugs for fibrosing ILDs and a poor evidence base to support current treatments. Fibrosing ILDs with a progressive phenotype show commonalities in clinical behaviour and in the pathogenic mechanisms that drive disease worsening. Nintedanib is an intracellular inhibitor of tyrosine kinases that has been approved for treatment of IPF and has recently been shown to reduce the rate of lung function decline in patients with ILD associated with systemic sclerosis (SSc-ILD). In vitro data demonstrate that nintedanib inhibits several steps in the initiation and progression of lung fibrosis, including the release of pro-inflammatory and pro-fibrotic mediators, migration and differentiation of fibrocytes and fibroblasts, and deposition of extracellular matrix. Nintedanib also inhibits the proliferation of vascular cells. Studies in animal models with features of fibrosing ILDs such as IPF, SSc-ILD, rheumatoid arthritis-ILD, hypersensitivity pneumonitis and silicosis demonstrate that nintedanib has anti-fibrotic activity irrespective of the trigger for the lung pathology. This suggests that nintedanib inhibits fundamental processes in the pathogenesis of fibrosis. A trial of nintedanib in patients with progressive fibrosing ILDs other than IPF (INBUILD) will report results in 2019.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".