Rationale and protocol of the Study Of diabetic Nephropathy with AtRasentan (SONAR) trial: A clinical trial design novel to diabetic nephropathy
Bibliographic record
Abstract
AIMS: Individuals with diabetes and chronic kidney disease (CKD) are at high risk for renal events. Recent trials of novel treatments have been negative, possibly because of variability in response to treatment of the target risk factor. Atrasentan is a selective endothelin A receptor antagonist that reduces urinary albumin-to-creatinine ratio (UACR), with a large variability between patients. We are assessing its effect on renal outcomes in the Study Of diabetic Nephropathy with AtRasentan (SONAR; NCT01858532) with an enrichment design (>30% lowering of albuminuria) to select patients most likely to benefit. MATERIALS AND METHODS: SONAR is a randomized, double-blind, placebo-controlled trial with approximately 3500 participants who have stage 2-4 CKD and macroalbuminuria and are receiving a maximum tolerated dose of a renin-angiotensin system inhibitor. RESULTS: After 6 weeks of exposure to atrasentan 0.75 mg once daily (enrichment period), participants with ≥30% UACR decrease and no tolerability issues (responders) were randomly assigned to placebo or atrasentan 0.75 mg/day. The responder group will be used for primary efficacy and safety analyses. Approximately 1000 participants with <30% UACR reduction (non-responders) were also randomized to placebo or atrasentan. The primary endpoint is a composite of a sustained doubling of serum creatinine or end-stage renal disease. The original power calculation indicated that a total of 425 primary renal events in the responder group provides 90% power to detect a 27% reduction in relative risk (alpha level of .05). CONCLUSION: SONAR aims to determine whether atrasentan added to guideline-recommended therapies safely reduces the risk of CKD progression and delays the onset of end-stage renal disease in patients with type 2 diabetes and nephropathy. SONAR also aims to establish whether the enrichment of patients based on their initial "surrogate" response to atrasentan will deliver a trial design in accord with personalized treatment of diabetic kidney disease.
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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 | 0.072 | 0.051 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.048 | 0.021 |
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".