Biomarker analysis from the phase 2b randomized placebo-controlled trial of riociguat in early diffuse cutaneous systemic sclerosis
Bibliographic record
Abstract
OBJECTIVE: To examine disease and target engagement biomarkers in the RISE-SSc trial of riociguat in early diffuse cutaneous systemic sclerosis and their potential to predict the response to treatment. METHODS: Patients were randomized to riociguat (n = 60) or placebo (n = 61) for 52 weeks. Skin biopsies and plasma/serum samples were obtained at baseline and week 14. Plasma cyclic guanosine monophosphate (cGMP) was assessed using radio-immunoassay. α-Smooth muscle actin (αSMA) and skin thickness were determined by immunohistochemistry, mRNA markers of fibrosis by qRT-PCR in skin biopsies, and serum CXC motif chemokine ligand 4 (CXCL-4) and soluble platelet endothelial cell adhesion molecule-1 (sPECAM-1) by enzyme-linked immunosorbent assay. RESULTS: By week 14, cGMP increased by 94 (78)% with riociguat and 10 (39)% with placebo (P < 0.001, riociguat vs placebo). Serum sPECAM-1 and CXCL-4 decreased with riociguat vs placebo (P = 0.004 and P = 0.008, respectively). There were no differences in skin collagen markers between the two groups. Higher baseline serum sPECAM-1 or the detection of αSMA-positive cells in baseline skin biopsies was associated with a larger reduction of modified Rodnan skin score from baseline at week 52 with riociguat vs placebo (interaction P-values 0.004 and 0.02, respectively). CONCLUSION: Plasma cGMP increased with riociguat, suggesting engagement with the nitric oxide-soluble guanylate cyclase-cGMP pathway. Riociguat was associated with a significant reduction in sPECAM-1 (an angiogenic biomarker) vs placebo. Elevated sPECAM-1 and the presence of αSMA-positive skin cells may help to identify patients who could benefit from riociguat in terms of skin fibrosis. TRIAL REGISTRATION: Clinicaltrials.gov, NCT02283762.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".