Sparsentan: A First-in-Class Dual Endothelin and Angiotensin II Receptor Antagonist
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the guidelines on the management of immunoglobulin A nephropathy (IgAN) and focal segmental glomerulosclerosis (FSGS), review the evidence for sparsentan, and discuss its place in therapy. DATA SOURCES: A literature search was conducted using MEDLINE, EMBASE, and clinicaltrials.gov using the search terms "sparsentan" and "RE-021" up to the end of Jun 2023. STUDY SELECTION AND DATA EXTRACTION: English studies were included if they evaluated the pharmacology, pharmacokinetics, efficacy, and safety of sparsentan in human subjects. Information from the Food and Drug Administration (FDA) and manufacturer's monograph were also extracted. DATA SYNTHESIS: In comparison with irbesartan, sparsentan reduced urine protein-to-creatinine ratio (UPCR) in both IgAN (-49.8% vs -15.1% at interim 36 weeks) and FSGS (-44.8% vs -18.5% at 8 weeks). Hypotension and edema were the most common adverse events in the sparsentan groups. Hepatotoxicity appears to be comparable between sparsentan and irbesartan in short-term results. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE IN COMPARISON WITH EXISTING DRUGS: Sparsentan provides a new option for patients with IgAN who are otherwise at high risk of progressive kidney disease. Continued FDA approval is dependent on long-term study results on renal function decline and safety. CONCLUSION: Sparsentan reduces proteinuria in IgAN and FSGS, and has expedited approval by the FDA for IgAN in patients at risk of rapid disease progression, generally at urine protein-to-creatinine ratio (UPCR) ≥1.5 g/g. Interim results from PROTECT and results from DUET showed promise for improving proteinuria in IgAN and FSGS. Long-term renal function benefit and safety data are pending.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".