Vilobelimab to improve clinical outcomes in moderate-to-severe hidradenitis suppurativa through an adjunctive effect on draining tunnels: results of the SHINE double-blind placebo-controlled randomized trial
Bibliographic record
Abstract
BACKGROUND: The results of a previous phase IIa trial suggested efficacy of the anti-C5a compound vilobelimab in a small number of patients with severe hidradenitis suppurativa (HS) refractory to adalimumab treatment. OBJECTIVES: To study the efficacy of vilobelimab in a larger-scale randomized phase IIb trial (SHINE) of patients with moderate-to-severe HS. METHODS: In total, 177 patients with moderate-to-severe HS were treated either with placebo or one of four doses of vilobelimab, a monoclonal antibody that targets the complement split product C5a. Hidradenitis Suppurativa Clinical Response (HiSCR) at 16 weeks was the primary endpoint. Those who achieved HiSCR switched to open-label low-dose vilobelimab; those who did not were switched to medium-dose vilobelimab. The trial was registered with the EU Clinical Trials Register (EudraCT number 2017-004501-40) and ClinicalTrials.gov (NCT03487276). RESULTS: The study did not meet the primary endpoint, perhaps due, in part, to an unexpectedly high HiSCR placebo response rate of 47.1%. HS flares decreased and post hoc analysis showed that high-dose vilobelimab significantly decreased the median draining tunnel counts and IHS4 score by 63%. Following the switch to medium-dose vilobelimab at week 16, 45.5% of those who did not respond to treatment achieved HiSCR at week 40. CONCLUSIONS: Compared with placebo, vilobelimab treatment did not result in significant HiSCR improvements in patients with moderate-to-severe HS. Post hoc analysis revealed significant effects for the highest tested dose on draining tunnel reduction and IHS4 score, which captures reductions in draining tunnels. These findings warrant further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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) | 0.004 | 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".