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Targeting Wee1-like protein kinase to treat cancer

2010· review· en· W163730063 on OpenAlexaff
Anastasios Stathis, Amit M. Oza

Bibliographic record

VenueDrug News & Perspectives · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsWee1Cancer researchCHEK1DNA damageKinaseDNA repairTyrosine kinaseG2-M DNA damage checkpointCell cycleCancerCancer cellCell cycle checkpointBiologySignal transductionCell biologyCyclin-dependent kinase 1DNAGenetics

Abstract

fetched live from OpenAlex

New anticancer agents are needed in order to overcome the resistance of cancer cells to standard chemotherapy. At present, many of the molecular events that drive the malignant transformation and progression have been identified and there is optimism that the development of agents that specifically target such events will improve treatment outcomes. Cancer cells present common alterations in components of pathways that are involved in the normal cell cycle regulation and in mechanisms of DNA damage repair. Wee1-like protein kinase is a tyrosine kinase with a key role as an inhibitory regulator of the G2/M checkpoint that precedes entry into mitosis. Abrogation of this checkpoint through Wee1 inhibition may result in increased antitumor activity of agents that cause DNA damage such as radiation therapy or some cytotoxic agents. This has been confirmed in preclinical studies and results of clinical studies evaluating a Wee1 inhibitor are awaited to establish its activity in combination with chemotherapy. Here we review the role of Wee1 tyrosine kinase in the control of the G2/M checkpoint and the effects of G2/M checkpoint abrogation through Wee1 inhibition. We present results of preclinical studies with Wee1 inhibitors and the results of the first clinical trial recently reported, evaluating MK-1775, a small-molecule inhibitor of Wee1.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.304
Teacher spread0.292 · 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.

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

Citations41
Published2010
Admission routes1
Has abstractyes

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