Ezabenlimab (BI 754091), an anti-PD-1 antibody, in patients with advanced solid tumours
Bibliographic record
Abstract
BACKGROUND: Ezabenlimab (BI 754091) is a humanised monoclonal antibody targeting programmed cell death protein-1. We report results from open-label, dose-escalation/expansion, Phase I trials that evaluated the safety, maximum tolerated dose (MTD), pharmacokinetics and antitumour activity of ezabenlimab at the recommended Phase II dose in patients with selected advanced solid tumours. STUDY DESIGN: Study 1381.1 (NCT02952248) was conducted in Canada, the United Kingdom and the United States. Study 1381.4 (NCT03433898) was conducted in Japan. Study 1381.3 (NCT03780725) was conducted in the Netherlands. The primary endpoints were: number of patients experiencing dose-limiting toxicities (DLTs) in the first cycle (dose escalation parts), number of patients with DLTs during the entire treatment period and objective response (dose expansion part of Study 1381.1). RESULTS: Overall, 117 patients received ezabenlimab intravenously every 3 weeks (80 mg, n = 3; 240 mg, n = 111; 400 mg, n = 3). No DLTs were observed and the MTD was not reached. Fifty-eight patients (52.3%) had grade ≥ 3 adverse events, most commonly anaemia (10.8%) and fatigue (2.7%). In 111 assessed patients treated with ezabenlimab 240 mg, disease control rate was 56.8% and objective response rate was 16.2%. Three patients had complete response; at data cut-off (November 2021) one remained in response and was still receiving ongoing treatment (duration of response [DoR]: 906 days). Partial responses occurred across several tumour types; DoR ranged from 67 to 757 days. CONCLUSIONS: Ezabenlimab was well tolerated and associated with durable antitumour activity in multiple solid tumours, comparable to other immune checkpoint inhibitors in similar patient populations and treatment settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".