Overcoming cisplatin resistance: design of novel hydrophobic platinum compounds.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".