Current and Future Treatment Landscape for Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) remains a disease with poor survival. The pathogenesis is complex and encompasses multiple molecular pathways. The first-generation antifibrotics pirfenidone and nintedanib, approved more than 10 years ago, have been shown to reduce the rate of progression, increase the length of life for patients with IPF, and work for other fibrotic lung diseases. In the last two decades, most clinical trials on IPF have failed to meet the primary endpoint and an urgent unmet need remains to identify agents or treatment strategies that can stop disease progression. The pharmacotherapeutic landscape for IPF is moving forward with a number of new drugs currently in clinical development, mostly in phase I and II trials, while only a few phase III trials are running. Since our understanding of IPF pathogenesis is still limited, we should keep focusing our efforts to deeper understand the mechanisms underlying this complex disease and their reflection on clinical phenotypes. This review discusses the key pathogenetic concepts for the development of new antifibrotic agents, presents the newest data on approved therapies, and summarizes new compounds currently in clinical development. Finally, future directions in antifibrotics development are discussed.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".