ENTERPRET: A Randomized Controlled Trial of Vedolizumab Dose Optimization in Patients With Ulcerative Colitis Who Have Early Nonresponse
Bibliographic record
Abstract
BACKGROUND & AIMS: Patients with ulcerative colitis (UC) may experience nonresponse to biologics, possibly as a result of low drug exposure. This trial assessed the efficacy of dose optimization in patients with UC who have early nonresponse to vedolizumab and high drug clearance. METHODS: ENTERPRET was a phase 4, open-label, randomized, controlled trial that included patients with moderate to severe UC who had high drug clearance at week 5 (serum concentration, <50 μg/mL) and nonresponse to standard vedolizumab treatment at week 6. At week 6, eligible patients were randomized 1:1 to receive standard dosing (300 mg every 8 weeks) or dose-optimized vedolizumab (600 mg at week 6, then 300 mg every 4 weeks; or 600 mg at week 6, then 600 mg every 4 weeks [based on week 5 serum concentration]). The primary end point was endoscopic improvement at week 30. RESULTS: Of 278 enrolled patients, 132 (47.5%) had a clinical response at week 6. From week 6, 108 patients received standard (n = 53) or dose-optimized vedolizumab (n = 55); among patients with nonresponse at week 6, 86.5% had high drug clearance. At week 30, 10 patients (18.9%) who received standard vedolizumab had endoscopic improvement vs 8 patients (14.5%) who received dose-optimized vedolizumab. Five patients (9.4%) who received standard vedolizumab had clinical remission at week 30 vs 5 patients (9.1%) who received dose-optimized vedolizumab; clinical response was observed in 17 (32.1%) and 17 patients (30.9%), respectively. Safety event rates were similar among treatment groups. CONCLUSIONS: In patients with early nonresponse and high drug clearance, vedolizumab dose optimization is probably not required. A proportion of patients benefited from continued treatment irrespective of the dose received. CLINICALTRIALS: gov: NCT03029143.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".