The kidney injury biomarker profile of patients with lupus nephritis remains unchanged with the second-generation calcineurin inhibitor voclosporin
Bibliographic record
Abstract
Objectives Kidney injury in patients with lupus nephritis (LN) results in pro-fibrotic biomarker expression, a manifestation also observed with calcineurin inhibitor (CNI) therapy. The second-generation CNI, voclosporin, is approved in the United States and Europe for the treatment of patients with active LN in combination with background immunosuppression, based on successful outcomes from the global phase 2 AURA-LV and phase 3 AURORA 1 studies, which demonstrated the efficacy of voclosporin across diverse racial and ethnic populations, and encompassing multiple biopsy classes of LN, alongside a favorable safety profile. This post hoc analysis examined changes from baseline levels of serum and urinary biomarkers, including pro-fibrotic biomarkers, in a cohort of patients from the parent AURORA 1 study Methods Samples were analyzed from a cohort of patients in AURORA 1 treated with voclosporin (23.7 mg twice daily, n=57) or placebo (n=59) in combination with mycophenolate mofetil (MMF) and low-dose glucocorticoids, including in a subgroup of patients that experienced a ≥30% decline from baseline in estimated glomerular filtration rate (voclosporin, n=26; placebo, n=20). Results The addition of voclosporin to MMF and low-dose glucocorticoids for the treatment of LN did not result in significant differences in normalized urinary concentrations of KIM-1, TGF-β1, MCP-1, or NGAL, biomarkers indicative of renal fibrosis and kidney damage, when compared to MMF and low-dose glucocorticoids alone. Conclusion These findings further support the safety of voclosporin for the treatment of LN in adult patients. Clinical Trial Registration ClinicalTrials.gov , identifier NCT03021499; EudraCT, identifier 2016-004045-81.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".