Transfer RNA Bindings to Antitumor Estradiol-Platinum(II) Hybrid and Cisplatin
Bibliographic record
Abstract
The anticancer platinum (Pt) drugs exert their antitumor activity by direct or indirect Pt-DNA binding. It has been shown that Pt drugs can induce major DNA damage and minor RNA damage during cancer treatment. A recent report showed that a new anticancer estradiol-Pt(II) hybrid molecule (CD-37) binds DNA bases indirectly, while being more effective than cis-diaminedichloroplatinum(II) (cisplatin) against several types of cancer. In this report, we examine the bindings of CD-37 and cisplatin drugs with transfer RNA (tRNA) in vitro and compare the results to those of the corresponding Pt-DNA complexes. Solutions containing various CD-37 or cisplatin concentrations were reacted with tRNA at physiological pH. Using Fourier transform infrared (FTIR), UV-visible, and circular dichroism spectroscopic methods, the drug binding mode, the binding constant, and RNA structural variations are determined for Pt-tRNA complexes in aqueous solution. Structural analysis showed direct binding of cisplatin drug to guanine and adenine N7 sites, while both direct and indirect interactions of CD-37 with tRNA bases and the backbone phosphate group were observed. The overall binding constants estimated were K(CD-37) = 2.77 (+/-0.90) x 10(4) M(1) and K(cisplatin) = 1.72 (+/-0.50) x 10(4) M(1). Major aggregation of tRNA occurs at high CD-37 concentrations, while RNA remains in the A-family structure.
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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) | 0.001 | 0.000 |
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.
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".