DNA intercalating drugs: Mechanisms of action in cancer treatment
Bibliographic record
Abstract
DNA-intercalating drugs (e.g., doxorubicin) have been used in cancer treatment since the 1960s. Multiple mechanisms have been observed with these drugs. These drugs intercalate into nucleosome-free regions of chromatin, which play a crucial role in regulating gene expression and genome organization. DNA intercalation by these drugs results in a plethora of events, including DNA damage, chromatin damage (histone eviction), erosion of chromatin organization, nucleolar condensation, RNA polymerase I and/or RNA polymerase II degradation, transcription arrest, deubiquitination of histone H2B ubiquitinated at lysine 120, topoisomerase I and/or II inhibition and/or trapping, and disruption of proteins associated with the elongating RNA polymerase II. These events may occur within hours following the addition of these drugs. At later times, changes to the DNA structure (e.g., the formation of Z DNA) occur, and eventually, the cells will die via apoptosis. This review will examine the mechanisms of action of DNA-intercalating drugs, specifically two anthracyclines (doxorubicin and aclarubicin) and a heteroaromatic compound (BMH-21). Doxorubicin and aclarubicin are used clinically to treat cancer, while BMH-21 remains in preclinical development. Reports on plasma pharmacokinetics of these anthracyclines will be tabulated, and the clinical relevance of the observed mechanisms of action for doxorubicin and aclarubicin will be assessed based on this information.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".