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Record W2413861093

Overcoming cisplatin resistance: design of novel hydrophobic platinum compounds.

2000· article· en· W2413861093 on OpenAlexaff
Gesche Tallen, Christian Mock, Suman B. Gangopadhyay, Bill Kangarloo, Bernt Krebs, Johannes Wolff

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsUniversity of CalgaryWiLAN (Canada)Alberta Children's Hospital
Fundersnot available
KeywordsCisplatinCytotoxicityPlatinumChemistryHydrophobic effectDNA damageStereochemistryBiochemistryCombinatorial chemistryDNAIn vitroBiologyChemotherapyGenetics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The anticancer activity of cisplatin derives from its ability to crosslink DNA. Cisplatin-resistance is partially caused by enhanced nucleotide excision repair (NER). Major 1,2-intrastrand crosslinks can create a hydrophobic notch at the damage site, which can be specifically bound by damage-recognition proteins, thus shielded from NER-activity. We aimed at preventing resistance by enhancing this mechanism using more hydrophobic platinum compounds. METHODS: We synthesized three platinum analogs with increased hydrophobic characteristics. Performing MTT-assays, the efficacy of cisplatin and the novel agents was compared in a fibroblast and eight brain tumour cell lines. RESULTS: Among the novel compounds, the most hydrophobic molecule, methylpyridineplatinum, was most cytotoxic (LC50 = 5.84 x 10(-5) M), followed by methylpyrazineplatinum, the second most hydrophobic (LC50 = 1.79 x 10(-4) M), and pyridineplatinum (LC50 = 2.76 x 10(-4) M). Overall, cisplatin revealed highest cytotoxicity (LC50 = 8.77 x 10(-6) M). CONCLUSIONS: Comparison of the novel compounds supports the hypothesis that increased hydrophobicity contributes to higher antitumour-activity. Other advantageous characteristics of cisplatin might relate to its remaining highest efficacy.

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 score0.172
Threshold uncertainty score0.658

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 head0.037
GPT teacher head0.242
Teacher spread0.206 · 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

Citations27
Published2000
Admission routes1
Has abstractyes

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