Inhalation innovation: phase 2b study design of inhaled pirfenidone in the treatment of progressive pulmonary fibrosis
Bibliographic record
Abstract
Introduction: Progressive Pulmonary Fibrosis (PPF) is an increasingly recognized condition, defined in 2022 to address the progression of pulmonary fibrosis in patients with interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF). Oral pirfenidone has been studied in non-IPF ILDs but never achieved a statistically significant change in primary endpoint. Trends seen in secondary endpoints support efficacy in PPF. Objective: Data from the AP01-002 (ATLAS) Study of inhaled pirfenidone in IPF demonstrated efficacy and improved safety compared to that seen with oral pirfenidone. The AP01-007 (MIST Study) is designed to study the efficacy and safety of AP01 (aerosolized pirfenidone) in patients with PPF. Patients will remain on background immunosuppression and up to 30% of patients will remain on background nintedanib therapy. Methods: The primary objective is to observe the change in the annual rate of decline in FVC in the AP01 treatment groups as compared to placebo. In addition, efficacy will be measured in secondary endpoints of time to progression, change in 6MWT difference, change in fibrotic scores via quantitative HRCT, and change from baseline QoL. Safety outcomes will be assessed as well. Cough will be analyzed through cough counts and cough questionnaires, which should allow differentiation of cough related to PPF vs. the nebulization procedure. Conclusion: MIST will study the safety and efficacy of AP01 (aerosolized pirfenidone) in patients with PPF. In addition to the safety and efficacy endpoints, MIST will carefully examine the presence of cough in this population of patients.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".