Assessing the Impact of Losmapimod on Proteinuria in Idiopathic Focal Segmental Glomerulosclerosis
Bibliographic record
Abstract
INTRODUCTION: Idiopathic focal segmental glomerulosclerosis (FSGS) is a leading cause of nephrotic syndrome and end-stage renal disease. In preclinical models and biopsies of human FSGS kidneys, p38 mitogen-activated protein kinase (MAPK) has demonstrated enhanced activity; and p38 MAPK inhibition has improved disease markers. This proof-of-concept trial aimed to assess efficacy, safety, tolerability, and pharmacokinetics of losmapimod, an oral p38 MAPK inhibitor, in humans with FSGS. METHODS: ; blood pressure <140/90 mm Hg. Collapsing and genetic forms of FSGS were excluded. The primary endpoint was number of patients with ≥50% proteinuria reduction and eGFR ≥70% of baseline after receiving losmapimod twice-daily for 16 to 24 weeks. RESULTS: Seventeen patients received ≥1 losmapimod dose. No patients achieved the primary endpoint; therefore, the study was terminated following a prespecified interim analysis. At week 24, proteinuria reductions between 20% and <50% were observed in 4 patients and proteinuria increases >20% in 3 patients. One patient achieved a proteinuria response (≥50% reduction) at week 2 but subsequently relapsed. Losmapimod pharmacokinetics were consistent with prior studies. No serious adverse events (AEs) were reported. CONCLUSION: p38 MAPK inhibition with losmapimod did not result in ≥50% reduction of proteinuria in patients with FSGS. However, study population heterogeneity may have contributed to our negative findings and therefore this does not eliminate the potential to demonstrate benefit in a population more sensitive to p38 MAPK inhibition if identifiable in the future by precision-medicine methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".