Quantitative disease progression model of α‐1 proteinase inhibitor therapy on computed tomography lung density in patients with α‐1 antitrypsin deficiency
Bibliographic record
Abstract
Aims Early‐onset emphysema attributed to α‐1 antitrypsin deficiency (AATD) is frequently overlooked and undertreated. RAPID‐RCT/RAPID‐OLE, the largest clinical trials of purified human α‐1 proteinase inhibitor (A1‐PI; 60 mg kg–1 week–1) therapy completed to date, demonstrated for the first time that A1‐PI is clinically effective in slowing lung tissue loss in AATD. A posthoc pharmacometric analysis was undertaken to further explore dose, exposure and response. Methods A disease progression model was constructed, utilizing observed A1‐PI exposure and lung density decline rates (measured by computed tomography) from RAPID‐RCT/RAPID‐OLE, to predict effects of population variability and higher doses on A1‐PI exposure and clinical response. Dose–exposure and exposure–response relationships were characterized using nonlinear and linear mixed effects models, respectively. The dose–exposure model predicts summary exposures and not individual concentration kinetics; covariates included baseline serum A1‐PI, forced expiratory volume in 1 s and body weight. The exposure–response model relates A1‐PI exposure to lung density decline rate at varying exposure levels. Results A dose of 60 mg kg–1 week–1 achieved trough serum levels >11 μmol l–1 (putative ‘protective threshold’) in ≥98% patients. Dose–exposure–response simulations revealed increasing separation between A1‐PI and placebo in the proportions of patients achieving higher reductions in lung density decline rate; improvements in decline rates ≥0.5 g l–1 year–1 occurred more often in patients receiving A1‐PI: 63 vs. 12%. Conclusion Weight‐based A1‐PI dosing reliably raises serum levels above the 11 μmol l–1 threshold. However, our exposure–response simulations question whether this is the maximal, clinically effective threshold for A1‐PI therapy in AATD. The model suggested higher doses of A1‐PI would yield greater clinical effects.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".