Vitamin C Antagonizes the Cytotoxic Effects of Antineoplastic Drugs
Bibliographic record
Abstract
Vitamin C is an antioxidant vitamin that has been hypothesized to antagonize the effects of reactive oxygen species-generating antineoplastic drugs. The therapeutic efficacy of the widely used antineoplastic drugs doxorubicin, cisplatin, vincristine, methotrexate, and imatinib were compared in leukemia (K562) and lymphoma (RL) cell lines with and without pretreatment with dehydroascorbic acid, the commonly transported form of vitamin C. The effect of vitamin C on viability, clonogenicity, apoptosis, P-glycoprotein, reactive oxygen species (ROS), and mitochondrial membrane potential was determined. Pretreatment with vitamin C caused a dose-dependent attenuation of cytotoxicity, as measured by trypan blue exclusion and colony formation after treatment with all antineoplastic agents tested. Vitamin C given before doxorubicin treatment led to a substantial reduction of therapeutic efficacy in mice with RL cell-derived xenogeneic tumors. Vitamin C treatment led to a dose-dependent decrease in apoptosis in cells treated with the antineoplastic agents that was not due to up-regulation of P-glycoprotein or vitamin C retention modulated by antineoplastics. Vitamin C had only modest effects on intracellular ROS and a more general cytoprotective profile than N-acetylcysteine, suggesting a mechanism of action that is not mediated by ROS. All antineoplastic agents tested caused mitochondrial membrane depolarization that was inhibited by vitamin C. These findings indicate that vitamin C given before mechanistically dissimilar antineoplastic agents antagonizes therapeutic efficacy in a model of human hematopoietic cancers by preserving mitochondrial membrane potential. These results support the hypothesis that vitamin C supplementation during cancer treatment may detrimentally affect therapeutic response.
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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".