AMG 386: profile of a novel angiopoietin antagonist in patients with ovarian cancer
Bibliographic record
Abstract
INTRODUCTION: ovarian cancer is the second most common gynecologic malignancy in the world with the majority of women presenting with advanced disease; whilst chemotherapeutic advances have improved progression-free survival, the increases in overall survival have been marginal. Novel biologic agents, including those designed to disrupt tumor angiogenesis, have demonstrated promising antitumor activity. AREAS COVERED: this review evaluates AMG 386, a novel investigational angiopoietin antagonist peptide-Fc fusion protein (peptibody), which potently and selectively inhibits angiopoietin-1 and angiopoietin-2 binding to the Tie2 tyrosine kinase receptor. Preclinical and clinical studies for AMG 386 are summarized, highlighting data pertaining to ovarian cancer. The role of angiopoietins in regulating physiologic and tumorigenic angiogenesis is addressed, as well as a brief discussion of non-angiopoietin anti-angiogenic strategies, followed by a review of preclinical, Phase I and II data and ongoing clinical studies for AMG 386, all in the context of ovarian cancer. EXPERT OPINION: AMG 386 has clinical activity and an acceptable safety profile both as monotherapy and in combination with chemotherapy. Of note, as the toxicity profiles of AMG 386 and inhibitors of the VEGF axis do not substantially overlap, AMG 386 could potentially be combined with other anti-angiogenic compounds to maximize disruption of malignant vascularization in ovarian cancer and other solid tumors.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".