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Record W2083169847 · doi:10.1158/1535-7163.targ-09-b98

Abstract B98: Induction of metabolic and oxidative stresses by the novel inhibitor of NAD+ biosynthesis, GMX 1778, for specific killing of human glioblastomas

2009· article· en· W2083169847 on OpenAlexaff
Stephen Yoo, David Cerna, Donna J. Carter, Siobhan Flaherty, Hongyun Li, Naoko Takebe, Mark H. Watson

Bibliographic record

VenueMolecular Cancer Therapeutics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsXenon Pharmaceuticals (Canada)
Fundersnot available
KeywordsNAD+ kinaseNicotinamide adenine dinucleotideBiosynthesisCancerBiochemistryNicotinamideIn vivoEnzymeNicotinamide phosphoribosyltransferaseCancer cellOxidative phosphorylationIn vitroBiologyCancer researchChemistryGenetics

Abstract

fetched live from OpenAlex

Abstract Cancer therapy strategies that selectively target tumors while sparing normal tissues would provide enormous clinical benefit to cancer patients. Central to developing these strategies is identification of tumor specific vulnerabilities and molecular targeted agents targeting these vulnerabilities. GMX1777/1778, a novel inhibitor of NAD+ biosynthesis, is a potent and specific inhibitor of the nicotinamide adnine dinucleotide (NAD+) biosynthesis enzyme, phosphoribosyl transferase (NAMPRT). Since cancer cells have a high rate of NAD+ turnover, modulation of NAD+ biosynthesis would be an attractive target for GMX 1777/1778. In addition to the depletion of NAD+ level, we present evidence of inducing other metabolic and oxidative stresses in a tumor specific manner with minimal damage to normal tissues in vitro and in vivo. Taken together, the cancer therapeutic strategy presented here would provide a novel way of specific killing of tumors. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):B98.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.030
GPT teacher head0.309
Teacher spread0.279 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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