The Effect of Etrasimod on Fecal Calprotectin and High-sensitivity C-reactive Protein: Results From the ELEVATE UC Clinical Program
Bibliographic record
Abstract
BACKGROUND: Biomarkers offer potential alternatives to endoscopies in monitoring ulcerative colitis (UC) progression and therapeutic response. This post hoc analysis of the ELEVATE UC clinical program assessed potential predictive values of fecal calprotectin (fCAL) and high-sensitivity C-reactive protein (hsCRP) as biomarkers and associated responses to etrasimod, an oral, once-daily, selective sphingosine 1-phosphate (S1P)1,4,5 receptor modulator for the treatment of moderately to severely active UC, in 2 phase 3 clinical trials. METHODS: In ELEVATE UC 52 and ELEVATE UC 12, patients were randomized 2:1 to 2 mg of etrasimod once daily or placebo for 52 or 12 weeks, respectively. Fecal calprotectin/hsCRP differences between responders and nonresponders for efficacy end points (clinical remission, clinical response, endoscopic improvement-histologic remission [EIHR]) were assessed by Wilcoxon P-values. Sensitivity and specificity were presented as receiver operating characteristics (ROC) curves with area under the curve (AUC). RESULTS: In ELEVATE UC 52 and ELEVATE UC 12, 289 and 238 patients received etrasimod and 144 and 116 received placebo, respectively. Baseline fCAL/hsCRP concentrations were generally balanced. Both trials had lower week-12 median fCAL levels in week-12 responders vs nonresponders receiving etrasimod for clinical remission, clinical response, and EIHR (all P < .001), with similar trends for hsCRP levels (all P < .01). For etrasimod, AUCs for fCAL/hsCRP and EIHR were 0.85/0.74 (week 12; ELEVATE UC 52), 0.83/0.69 (week 52; ELEVATE UC 52), and 0.80/0.65 (week 12; ELEVATE UC 12). CONCLUSIONS: Fecal calprotectin/hsCRP levels decreased with etrasimod treatment; ROC analyses indicated a prognostic correlation between fCAL changes during induction and short-/long-term treatment response.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".