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Neovastat—a novel antiangiogenic drug for cancer therapy

2003· review· en· W2011397767 on OpenAlexaff
Denis Gingras, Dominique Boivin, Christophe Deckers, Sébastien P. Gendron, Chantal Barthomeuf, Richard Béliveau

Bibliographic record

VenueAnti-Cancer Drugs · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à Montréal
Fundersnot available
KeywordsAngiogenesisMedicineDrugClinical trialMatrix metalloproteinaseMatrix metalloproteinase inhibitorMultiple myelomaPharmacologyCancerLung cancerApoptosisCancer researchVascular endothelial growth factorOncologyInternal medicineBiologyVEGF receptors

Abstract

fetched live from OpenAlex

Neovastat (AE-941) is an antiangiogenic drug isolated from marine cartilage. It interferes with several steps associated with the development of angiogenesis through its ability to induce endothelial cell apoptosis, and to inhibit matrix metalloproteinase activities and vascular endothelial growth factor-mediated signaling pathways, suggesting that Neovastat behaves as a multifunctional antiangiogenic drug. Neovastat is orally bioavailable, and shows significant antitumor and antimetastatic properties in animal models. An excellent safety profile with few side effects has been monitored in more than 800 patients who have been exposed to Neovastat, some of whom for more than 4 years. This indicates that Neovastat is suitable for long-term use, either alone or in combination with other anticancer therapies. Accordingly, Neovastat is currently under evaluation in three pivotal clinical studies with two phase III clinical trials in patients with lung and renal carcinoma, and a phase II clinical trial in patients with multiple myeloma is ongoing.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.057
GPT teacher head0.373
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations59
Published2003
Admission routes1
Has abstractyes

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