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Record W2733163186 · doi:10.1111/bcp.13358

Quantitative disease progression model of α‐1 proteinase inhibitor therapy on computed tomography lung density in patients with α‐1 antitrypsin deficiency

2017· article· en· W2733163186 on OpenAlexaff
Michael A. Tortorici, James A. Rogers, Oliver Vit, Martin Bexon, Robert A. Sandhaus, Jonathan Burdon, Joanna Chorostowska‐Wynimko, Philip J. Thompson, James Stocks, Noel G. McElvaney, Kenneth R. Chapman, Jonathan M. Edelman

Bibliographic record

VenueBritish Journal of Clinical Pharmacology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsUniversity of Toronto
FundersCSL Behring
KeywordsMedicinePlaceboLungInternal medicineGastroenterologyDose–response relationshipRandomized controlled trialQuantitative computed tomographyAlpha 1-antitrypsin deficiencyPopulationClinical trialPathologyBone density

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.033
GPT teacher head0.371
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2017
Admission routes1
Has abstractyes

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