Two first-in-human studies of xentuzumab, a humanised insulin-like growth factor (IGF)-neutralising antibody, in patients with advanced solid tumours
Bibliographic record
Abstract
BACKGROUND: Xentuzumab, an insulin-like growth factor (IGF)-1/IGF-2-neutralising antibody, binds IGF-1 and IGF-2, inhibiting their growth-promoting signalling. Two first-in-human trials assessed the maximum-tolerated/relevant biological dose (MTD/RBD), safety, pharmacokinetics, pharmacodynamics, and activity of xentuzumab in advanced/metastatic solid cancers. METHODS: These phase 1, open-label trials comprised dose-finding (part I; 3 + 3 design) and expansion cohorts (part II; selected tumours; RBD [weekly dosing]). Primary endpoints were MTD/RBD. RESULTS: Study 1280.1 involved 61 patients (part I: xentuzumab 10-1800 mg weekly, n = 48; part II: 1000 mg weekly, n = 13); study 1280.2, 64 patients (part I: 10-3600 mg three-weekly, n = 33; part II: 1000 mg weekly, n = 31). One dose-limiting toxicity occurred; the MTD was not reached for either schedule. Adverse events were generally grade 1/2, mostly gastrointestinal. Xentuzumab showed dose-proportional pharmacokinetics. Total plasma IGF-1 increased dose dependently, plateauing at ~1000 mg/week; at ≥450 mg/week, IGF bioactivity was almost undetectable. Two partial responses occurred (poorly differentiated nasopharyngeal carcinoma and peripheral primitive neuroectodermal tumour). Integration of biomarker and response data by Bayesian Logistic Regression Modeling (BLRM) confirmed the RBD. CONCLUSIONS: Xentuzumab was well tolerated; MTD was not reached. RBD was 1000 mg weekly, confirmed by BLRM. Xentuzumab showed preliminary anti-tumour activity. CLINICAL TRIAL REGISTRATION: NCT01403974; NCT01317420.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".