MétaCan
Menu
Back to cohort
Record W1660187606 · doi:10.1515/dmdi.2007.22.4.201

On the Synergistic Effect of Doxorubicin and Mitomycin C Against Breast Cancer Cells

2007· article· en· W1660187606 on OpenAlexafffund
Adam J. Shuhendler, Peter J. O’Brien, Andrew M. Rauth, Xiao Yu Wu

Bibliographic record

VenueDrug metabolism and drug interactions · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsDoxorubicinMitomycin CIn vivoChemistryIn vitroDNATopoisomeraseEffluxDNA damageCancer cellGlutathionePharmacologyCancer researchBiochemistryMolecular biologyCancerBiologyChemotherapyEnzymeGenetics

Abstract

fetched live from OpenAlex

The combination of doxorubicin and mitomycin C has been shown previously to result in supra-additive tumor cell killing in vitro in both murine and human breast cancer cells and in vivo against murine breast cancer cells. Median effect analysis was used to determine the significance and degree of interaction. The origin of this synergy was sought by evaluating the contribution of membrane efflux pump modulation, formaldehyde production, reactive oxygen species, DNA cross-linking, and DNA double-strand breaks to this effect. The interaction of mitomycin C and doxorubicin in vitro was found to be a true synergy whose mechanism was efflux pump-independent. DNA cross-links were only found to increase additively with co-administration of the drugs; however, a supra-additive increase in DNA double-strand breaks was observed. The results suggest that poisoning of topoisomerase IIalpha by doxorubicin may interact with drug-induced DNA cross-links to enhance the formation of DNA double-strand breaks. This interaction, together with glutathione depletion and mitomycin C-derived formaldehyde, may be the underlying mechanism(s) of the synergy observed between mitomycin C and doxorubicin.

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 score0.031
Threshold uncertainty score0.410

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 head0.004
GPT teacher head0.252
Teacher spread0.248 · 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

Citations30
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueDrug metabolism and drug interactionsSame topicCancer therapeutics and mechanismsFrench-language works237,207