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Record W4408160286 · doi:10.1016/j.redox.2025.103582

Anti-Cancer Potential of a new Derivative of Caffeic Acid Phenethyl Ester targeting the Centrosome

2025· article· en· W4408160286 on OpenAlexaff
Catello Giordano, Jonatan Kendler, Maximilian Sexl, Sebastian Kollman, Maxim Varenicja, Boglárka Szabó, Gerald Timelthaler, Dominik Kirchhofer, Oldamur Hollóczki, Suzanne D. Turner, Richard Moriggl, Lukas Kenner, Mohamed Touaibia, Olaf Merkel

Bibliographic record

VenueRedox Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicSirtuins and Resveratrol in Medicine
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsCaffeic acid phenethyl esterCancerCentrosomeCaffeic acidChemistryCancer researchBiochemistryCell biologyBiologyApoptosisGeneticsCell cycleAntioxidant

Abstract

fetched live from OpenAlex

Anaplastic Large Cell Lymphoma (ALCL) is an aggressive T-cell lymphoma affecting children and young adults. About 30% of patients develop therapy resistance therefore new precision medicine drugs are highly warranted. Multiple rounds of structure-activity optimization of Caffeic Acid Phenethyl Ester have resulted in CM14. CM14 causes upregulation of genes involved in oxidative stress response and downregulation of DNA replication genes leading to G2/M arrest and subsequent apoptosis induction. In accordance with this, an unbiased proteomics approach, confocal microscopy and molecular modeling showed that TUBGCP2, member of the centrosomal γ-TuRC complex, is a direct interaction partner of CM14. CM14 overcomes ALK inhibitor resistance in ALCL and is also active in T-cell Acute Lymphoblastic Leukemia and Acute Myeloid Leukemia. Interestingly, CM14 also induced cell death in docetaxel-resistant prostate cancer cells thus suggesting an unexpected role in solid cancers. Thus, we synthesized and thoroughly characterized a novel TUBGCP2 targeting drug that is active in ALCL but has also potential for other malignancies.

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 score0.068
Threshold uncertainty score0.251

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 head0.010
GPT teacher head0.304
Teacher spread0.294 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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