A phase Ib study of combined angiogenesis blockade with REGN910 (SAR307746), a selective monoclonal antibody (MAb) against angiopoietin-2 (Ang2) and ziv-aflibercept in patients with advanced solid tumor malignancies.
Bibliographic record
Abstract
TPS2618 Background: REGN910 is a selective, fully human Angiopoietin-2 (ANG-2) MAb, which potently blocks signaling through the Tie2 receptor. Ziv-aflibercept (ZAFL) is a recombinant human fusion protein that acts as a decoy receptor for vascular endothelial growth factor (VEGF)-A, VEGF-B, and placental growth factor (PlGF), thereby preventing the interaction of these ligands with their receptors. In several mouse xenograft models, combination of the 2 anti-angiogenic compounds, REGN910 and ZAFL, demonstrated significantly enhanced tumor growth inhibition relative to either agent alone, suggesting that dual angiogenic blockade is worth exploring in cancer patients. Methods: This phase 1b study employs a standard 3+3 dose escalation design exploring 5 different combination treatment dose levels of REGN910 and ZAFL. Once the recommended phase 2 dose (RD) of the combination treatment is determined, additional patients will be enrolled in a safety expansion cohort, for a planned total enrollment of up to 40 patients. The primary study objectives are to evaluate the safety and determine the RD of the 2 drugs in combination when both are administered IV every 2 weeks in patients with advanced solid tumors. Secondary endpoints include characterization of the PK and potential immunogenicity of REGN910 and ZAFL when given in combination, evaluation of correlative PD biomarkers related to REGN910 and ZAFL, and identification of antitumor activity. Enrollment to cohorts 1 and 2 has been completed without DLT. Enrollment to cohort 3 opened in December 2012. Updated enrollment status will be presented. Reference: ClinicalTrials.gov Identifier: NCT01688960. Clinical trial information: NCT01688960.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".