Treatment of idiopathic pulmonary fibrosis: an update on emerging drugs in phase II & III clinical trials
Bibliographic record
Abstract
INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a progressive, debilitating lung disease with poor prognosis. Although two antifibrotics have been approved in the past decade there are no curative therapies. AREAS COVERED: This review highlights the current landscape of IPF research in the development of novel compounds for the treatment of IPF while also evaluating repurposed medications and their role in the management of IPF. The literature search includes studies found on PubMed, conference abstracts, and press releases until March 2024. EXPERT OPINION: Disease progression in IPF is driven by a dysregulated cycle of microinjury, aberrant wound healing, and propagating fibrosis. Current drug development focuses on attenuating fibrotic responses via multiple pathways. Phosphodiesterase 4 inhibitors (PDE4i), lysophosphatidic acid (LPA) antagonists, dual-selective inhibitor of αvβ6 and αvβ1 integrins, and the prostacyclin agonist Treprostinil have had supportive phase II clinical trial results in slowing decline in forced vital capacity (FVC) in IPF. Barriers to drug development specific to IPF include the lack of a rodent model that mimics IPF pathology, the nascent understanding of the role of genetics affecting development of IPF and response to treatment, and the lack of a validated biomarker to monitor therapeutic response in patients with IPF. Successful treatment of IPF will likely include a multi-targeted approach anchored in precision medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".