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Record W2498632582 · doi:10.18433/j30033

Targeting Angiogenesis in Cancer Treatments: Where do we Stand?

2016· article· en· W2498632582 on OpenAlexvenueno aff
Nenad Petrović

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsAngiogenesisCancerNeovascularizationMedicineVascular endothelial growth factorCancer researchRegeneration (biology)PharmacologyBiologyCell biologyInternal medicineVEGF receptors

Abstract

fetched live from OpenAlex

Since early seventies of the twentieth century, through seminal work of Judah Folkman, angiogenesis, the process of new blood vessel sprouting from the existing vasculature, was recognized as a necessary part of wound healing, development of placenta, tissue growth and regeneration as well as cancer progression. This process is induced by low tissue oxygenation and it is a crucial prerequisite for rapid tissue growth, providing proper oxygen supply and removal of toxic metabolites. Suppression of angiogenesis as a way of slowing down tumor progression continues to be one of the most important areas of cancer research. The angiogenic process is relatively complex and it is regulated by numerous pro- and anti-angiogenic factors. Intensive research in the last twenty years resulted in identification of more than 300 angiogenesis inhibitors, a trend that is expected to continue. Unfortunately, most of these treatments have demonstrated unacceptable toxicities or failed to show activity in clinical studies. Although not yet completely understood, the complex process of tumor angiogenesis involves highly regulated orchestration of multiple activating and inhibiting factors. Vascular endothelial growth factor (VEGF) and its cognate receptors appear to play a central role in angiogenesis activation. Thus, initial efforts to develop anti-angiogenic treatments focused largely on inhibiting VEGF action. Such approaches, however, often lead to transient responses due to multiple pathways able to compensate for a single pathway inhibited. Accordingly, more recent treatments have focused on simultaneous inhibition of multiple signaling pathways. This review concentrates on identifying those anti-angiogenic treatments that made to the clinic by receiving approval by USA Food and Drug Administration (FDA) as treatments for cancer. Regardless of observed problems, it is an imperative that research in angiogenesis regulation continues. Consequently, pharmacological manipulation of angiogenesis may yet to introduce truly new pharmacological therapies into the field of cancer therapy, the field that was rather dormant in the last several decades. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.058
GPT teacher head0.407
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2016
Admission routes1
Has abstractyes

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