Phosphodiesterase 4B inhibition: a potential novel strategy for treating pulmonary fibrosis
Bibliographic record
Abstract
Patients with interstitial lung disease can develop a progressive fibrosing phenotype characterised by an irreversible, progressive decline in lung function despite treatment. Current therapies slow, but do not reverse or stop, disease progression and are associated with side-effects that can cause treatment delay or discontinuation. Most crucially, mortality remains high. There is an unmet need for more efficacious and better-tolerated and -targeted treatments for pulmonary fibrosis. Pan-phosphodiesterase 4 (PDE4) inhibitors have been investigated in respiratory conditions. However, the use of oral inhibitors can be complicated due to class-related systemic adverse events, including diarrhoea and headaches. The PDE4B subtype, which has an important role in inflammation and fibrosis, has been identified in the lungs. Preferentially targeting PDE4B has the potential to drive anti-inflammatory and antifibrotic effects via a subsequent increase in cAMP, but with improved tolerability. Phase I and II trials of a novel PDE4B inhibitor in patients with idiopathic pulmonary fibrosis have shown promising results, stabilising pulmonary function measured by change in forced vital capacity from baseline, while maintaining an acceptable safety profile. Further research into the efficacy and safety of PDE4B inhibitors in larger patient populations and for a longer treatment period is needed.
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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 | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".