Emerging pharmacotherapies for brain metastases: a spotlight on angiogenesis inhibitors
Bibliographic record
Abstract
INTRODUCTION: Brain metastases represent a major cause of morbidity and mortality in cancer patients and are challenging to manage due to the protective blood-brain barrier and the aggressive nature of intracranial disease. Angiogenesis, particularly mediated by vascular endothelial growth factor (VEGF) and integrin pathways, plays a critical role in the growth and progression of brain metastases. Inhibiting angiogenesis has emerged as a therapeutic strategy to control tumor progression, reduce edema, and improve clinical outcomes. AREA COVERED: This review summarizes the biological mechanisms underpinning angiogenesis in brain metastases, with a focus on VEGF and integrins as therapeutic targets. The role of bevacizumab, a monoclonal anti-VEGF antibody, is discussed in detail, particularly its established use in managing radiation necrosis. The review further explores FDA-approved angiogenesis inhibitors, emerging therapies targeting alternative pathways such as angiopoietin-2 and integrins, and the latest clinical trials assessing their efficacy. Combination strategies, particularly with immune checkpoint inhibitors and radiation, are highlighted as promising avenues for improving intracranial disease control. EXPERT OPINION: Anti-angiogenic therapies, while already well integrated into the management of radiation necrosis, are poised to expand into active treatment paradigms for brain metastases. Future advances are likely to focus on biomarker-driven patient selection, novel drug delivery technologies, and rational combination regimens. Angiogenesis inhibitors are expected to become a standard component of multimodal treatment approaches, moving beyond symptomatic control toward improving progression-free and overall survival in patients with brain metastases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".