Inter‐individual variability in atrasentan exposure partly explains variability in kidney protection and fluid retention responses: A post hoc analysis of the <scp>SONAR</scp> trial
Bibliographic record
Abstract
AIM: To evaluate whether atrasentan plasma exposure explains between-patient variability in urinary albumin-to-creatinine ratio (UACR) response, a surrogate for kidney protection, and B-type natriuretic peptide (BNP) response, a surrogate for fluid expansion. METHODS: Type 2 diabetic patients with chronic kidney disease (n = 4775) received 0.75 mg atrasentan for 6 weeks in the active run-in period. Individual area under the concentration-time-curve (AUC) was estimated using a population pharmacokinetic model. The association between atrasentan AUC, other clinical characteristics, and UACR and BNP response, was estimated using linear regression. RESULTS: The median atrasentan AUC was 43.8 ng.h/mL with a large variation among patients (2.5th-97.5th percentiles [P]: 12.6 to 197.5 ng.h/mL). Median UACR change at the end of enrichment was -36.0% and median BNP change was 8.7%, which also varied among patients (UACR, 2.5th-97.5th P: -76.2% to 44.5%; BNP, 2.5th-97.5th P: -71.5% to 300.0%). In the multivariable analysis, higher atrasentan AUC was associated with greater UACR reduction (4.88% per doubling in ng.h/mL [95% confidence interval {CI}: 6.21% to 3.52%], P < .01) and greater BNP increase (3.08% per doubling in ng.h/mL [95% CI: 1.12% to 4.11%], P < .01) independent of estimated glomerular filtration rate, haemoglobin or BNP. Caucasian patients compared with black patients had greater UACR reduction (7.06% [95% CI: 1.38% to 13.07%]) and also greater BNP increase (8.75% [95% CI: 1.65% to 15.35%]). UACR response was not associated with BNP response (r = 0.06). CONCLUSION: Atrasentan plasma exposure varied among individual patients and partially explained between-patient variability in efficacy and safety response.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".