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The selective Aurora B kinase inhibitor AZD1152 is a potential new treatment for multiple myeloma

2007· article· en· W2085359619 on OpenAlexaff
Robert P. Evans, Claudia Naber, Tara Steffler, Tamara Checkland, Christopher A. Maxwell, Jonathan J. Keats, Andrew R. Belch, Linda M. Pilarski, Raymond Lai, Tony Reiman

Bibliographic record

VenueBritish Journal of Haematology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsUniversity of Alberta
FundersApplied Materials
KeywordsAurora inhibitorAurora B kinaseAurora kinaseMultiple myelomaPlasma Cell MyelomaAurora A kinaseCancer researchApoptosisKinaseMedicineCell cycleBiologyInternal medicineCellCancerCytokinesisCell biologyCell divisionBiochemistry

Abstract

fetched live from OpenAlex

Aurora kinases are potential targets for cancer therapy. Previous studies have validated Aurora kinase A as a therapeutic target in multiple myeloma (MM), and have demonstrated in vitro anti-myeloma effects of small molecule Aurora kinase inhibitors that inhibit both Aurora A and B. This study demonstrated that Aurora B kinase was strongly expressed in myeloma cell lines and primary plasma cells. The selective Aurora B inhibitor AZD1152-induced apoptotic death in myeloma cell lines at nanomolar concentrations, with a cell cycle phenotype consistent with that reported previously for Aurora B inhibition. In some cases, AZD1152 in combination with dexamethasone showed increased anti-myeloma activity compared with the use of either agent alone. AZD1152 was active against sorted CD138(+) BM plasma cells from myeloma patients but also, as expected, was toxic to CD138(-) marrow cells from the same patients. In a murine myeloma xenograft model, AZD1152-inhibited tumour growth at well-tolerated doses and induced cell death in established tumours, with associated mild, transient leucopenia. AZD1152 shows promise in these preclinical studies as a novel treatment for MM.

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 score0.248
Threshold uncertainty score0.508

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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

Citations61
Published2007
Admission routes1
Has abstractyes

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