Study of DNA Interactions with Steroidal and Nonsteroidal Estrogen–Platinum (II)–Based Anticancer Drugs
Bibliographic record
Abstract
The interactions of the steroidal and nonsteroidal estrogen-platinum (Pt) (II)-based anticancer drugs 16beta-hydroxymethyl-16alpha-[8-(2-pyridin-2-yl-ethylamino)-3,6-dioxaoctyl]-1,3,5(10)-estratrien-3,17betadiol dichloroplatinum (II) (JPM-39), 4-[6-(2'-pyridylethylamino)-butyloxy)-phenyl]-7-methoxy-2,2-dimethyl-3-phenyl-chroman dichloroplatinum (II) (ATG-99), and 1-[(2-aminoethyl)amino]-9,10,10-tris(4-hydroxyphenyl)-9-decene dichloroplatinum (II) (GEB-28) with calf-thymus DNA in vitro using constant DNA concentration and various drug levels were studied. Fourier transform infrared (FTIR) and circular dichroism (CD) were studied with calf-thymus DNA in vitro using constant DNA concentration and various drug levels. FTIR, UV-visible, and CD spectroscopic methods were used to characterize the drug binding mode, the binding constant, and structural variations of DNA in aqueous solution. Spectroscopic evidence showed that the various Pt-based drugs bind indirectly to the major and minor grooves of DNA duplex with some degree of drug-phosphate interaction. The overall binding constants for JPM-39, GEB-28, and ATG-99 are K(JPM-39) = 4.2 (+/-0.75) x 10(3) M(-1), K(GEB-28) = 3.4 (+/-0.65) x 10(3) M(-1), and K(ATG-99) = 2.1 (+/-0.45) x 10(3) M(-1). DNA aggregation occurs at high drug concentration, while DNA remains in the B-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".