Targeting Angiogenesis With Integrative Cancer Therapies
Bibliographic record
Abstract
An integrative approach for managing a patient with cancer should target the multiple biochemical and physiological pathways that support tumor development while minimizing normal tissue toxicity. Angiogenesis is a key process in the promotion of cancer. Many natural health products that inhibit angiogenesis also manifest other anticancer activities. The authors will focus on natural health products (NHPs) that have a high degree of antiangiogenic activity but also describe some of their many other interactions that can inhibit tumor progression and reduce the risk of metastasis. NHPs target various molecular pathways besides angiogenesis, including epidermal growth factor receptor (EGFR), the HER-2/neu gene, the cyclooxygenase-2 enzyme, the NF-kB transcription factor, the protein kinases, Bcl-2 protein, and coagulation pathways. The herbalist has access to hundreds of years of observational data on the anticancer activity of many herbs. Laboratory studies are confirming the knowledge that is already documented in traditional texts. The following herbs are traditionally used for anticancer treatment and are antiangiogenic through multiple interdependent processes that include effects on gene expression, signal processing, and enzyme activities: Artemisia annua (Chinese wormwood), Viscum album (European mistletoe), Curcuma longa (turmeric), Scutellaria baicalensis (Chinese skullcap), resveratrol and proanthocyanidin (grape seed extract), Magnolia officinalis (Chinese magnolia tree), Camellia sinensis (green tea), Ginkgo biloba, quercetin, Poria cocos, Zingiber officinale (ginger), Panax ginseng, Rabdosia rubescens (rabdosia), and Chinese destagnation herbs. Quality assurance of appropriate extracts is essential prior to embarking on clinical trials. More data are required on dose response, appropriate combinations, and potential toxicities. Given the multiple effects of these agents, their future use for cancer therapy probably lies in synergistic combinations. During active cancer therapy, they should generally be evaluated in combination with chemotherapy and radiation. In this role, they act as biological response modifiers and adaptogens, potentially enhancing the efficacy of the so-called conventional therapies. Their effectiveness may be increased when multiple agents are used in optimal combinations. New designs for trials to demonstrate activity in human subjects are required. Although controlled trials might be preferred, smaller studies with appropriate end points and surrogate markers for antiangiogenic response could help prioritize agents for the larger resource-intensive phase 3 trials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".