A randomised phase I study of etrolizumab (rhuMAb β7) in moderate to severe ulcerative colitis
Bibliographic record
Abstract
OBJECTIVE: Etrolizumab (rhuMAb β7, anti-β7, PRO145223) is a humanised monoclonal antibody targeting the β7 subunit of the heterodimeric integrins α4β7 and αEβ7, which are implicated in leucocyte migration and retention in ulcerative colitis (UC). This randomised phase I study evaluated the safety and pharmacology of etrolizumab in patients with moderate to severe UC. DESIGN: In the single ascending dose (SAD) stage, etrolizumab (0.3, 1.0, 3.0, 10 mg/kg intravenous, 3.0 mg/kg subcutaneous (SC) or placebo) was administered 4:1 (n=25) in each cohort. In the multiple dose (MD) stage, new patients received monthly etrolizumab (0.5 mg/kg SC (n=4), 1.5 mg/kg SC (n=5), 3.0 mg/kg SC (n=4), 4.0 mg/kg intravenous (n=5)) or placebo (n=5). The pharmacokinetics was studied and Mayo Clinic Score evaluated at baseline, day 29 (SAD), and days 43 and 71 (MD). RESULTS: In the SAD stage, there were no dose limiting toxicities, infusion or injection site reactions. Two impaired wound healing serious adverse events occurred in two patients receiving etrolizumab. In the MD stage, there were no dose limiting toxicities, and no infusion or injection site reactions. Headache was the most common adverse event, occurring more often in etrolizumab patients. Antietrolizumab antibodies were detected in two subjects. The duration of β7 receptor full occupancy was dose related. A clinical response was observed in 12/18 patients, and clinical remission in 3/18 patients treated with etrolizumab in the MD stage, compared with 4/5 and 1/5 placebo patients, respectively. CONCLUSION: Etrolizumab is well tolerated in moderate to severe UC. Further investigation is warranted.
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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) | 0.003 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".