Investigational gene expression inhibitors for the treatment of idiopathic pulmonary fibrosis
Bibliographic record
Abstract
INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing interstitial lung disease of unknown cause that occurs primarily in older adults and is associated with poor quality of life and substantial healthcare utilization. IPF has a dismal prognosis. Indeed, first-line therapy, which includes nintedanib and pirfenidone, does not stop disease progression and is often associated with tolerability issues. Therefore, there remains a high medical need for more efficacious and better tolerated treatments. AREAS COVERED: Gene therapy is a relatively unexplored field of research in IPF that has the potential to mitigate a range of profibrotic pathways by introducing genetic material into cells. Here, we summarize and critically discuss publications that have explored the safety and efficacy of gene therapy in experimentally-induced pulmonary fibrosis in animals, as clinical studies in humans have not been published yet. EXPERT OPINION: The application of gene therapy in pulmonary fibrosis requires further investigation to address several technical and biological hurdles, improve vectors' design, drug delivery, and target selection, mitigate off-target effects and develop markers of gene penetration into target cells. Long-term clinical data are needed to bring gene therapy in IPF one step closer to practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".