Role of Drug Metabolism in the Cytotoxicity and Clinical Efficacy of Anthracyclines
Bibliographic record
Abstract
Many clinical studies involving anti-tumor agents neglect to consider how these agents are metabolized within the host and whether the creation of specific metabolites alters drug therapeutic properties or toxic side effects. However, this is not the case for the anthracycline class of chemotherapy drugs. This review describes the various enzymes involved in the one electron (semi-quinone) or two electron (hydroxylation) reduction of anthracyclines, or in their reductive deglycosidation into deoxyaglycones. The effects of these reductions on drug antitumor efficacy and toxic side effects are also discussed. Current evidence suggests that the one electron reduction of anthracyclines augments both their tumor toxicity and their toxicity towards the host, in particular their cardiotoxicity. In contrast, the two electron reduction (hydroxylation) of anthracyclines strongly reduces their ability to kill tumor cells, while augmenting cardiotoxicity through their accumulation within cardiomyocytes and their direct effects on excitation/contraction coupling within the myocytes. The reductive deglycosidation of anthracyclines appears to inactivate the drug and only occurs under rare, anaerobic conditions. This knowledge has resulted in the identification of important new approaches to improve the therapeutic index of anthracyclines, in particular by inhibiting their cardiotoxicity. The true utility of these approaches in the management of cancer patients undergoing anthracycline-based chemotherapy remains unclear, although one such agent (the iron chelator dexrazoxane) has recently been approved for clinical use.
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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 | 0.001 |
| 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 | 0.001 | 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".