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Record W4406542041 · doi:10.26434/chemrxiv-2025-ffb6q

A tale of two mechanisms: The p53 modulator COTI-2 is a Zn metallochaperone

2025· preprint· en· W4406542041 on OpenAlexafffund
İrem Şimşek, Farsheed Shahbazi‐Raz, Azam Mohammadzadeh, Maryam Kosar, Peihan Xu, Samra Khan, Olena I. Tykhoniuk, Ashley DaDalt, Deya'a Almasri, Lara K. Watanabe, Kaitlyn Breault, John J. Hayward, Fraser S. Pick, Ruoya Ho, Jeremy M. Rawson, John F. Trant

Bibliographic record

VenueChemRxiv · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryBiophysicsPhysicsBiology

Abstract

fetched live from OpenAlex

ABSTRACT Mutations in, or misregulation of, Tp53 are found in approximately 50% of all cancers. p53 functions by ensuring that cells with irretrievably damaged DNA undergo apoptosis. Tp53 mutations often induce conformational changes that inhibit activity; however, small‐molecule chaperones could theoretically restore conformation and activity. COTI‐2, a thiosemicarbazone with orphan‐drug status for ovarian cancer, has proven an effective cytotoxic agent against various cancer cell lines in vitro , exhibited efficacy in vivo , and has demonstrated a good safety profile in Phase 1b human clinical trials. The proposed mechanism, direct engagement and refolding of mutant p53, has been supported by a combination of cell‐based assays and transcriptomics data. Through a combination of experimental and computational approaches, we demonstrate that this is an unlikely mechanism of action, and that COTI ‐2 instead likely acts as a selective, well‐tolerated, zinc chaperone to replace zinc ions lost to p53 mutants' deficient zinc‐binding.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0060.014
Open science0.0020.004
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0090.003

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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