Radiation-Mediated Tumor Growth Inhibition Is Significantly Enhanced with Redox-Active Compounds That Cycle with Ascorbate
Bibliographic record
Abstract
Aims: We aim here to demonstrate that radiation (RT) enhances tumor sensitization by only those Mn complexes that are redox active and cycle with ascorbate (Asc), thereby producing H 2 O 2 and utilizing it subsequently in protein S -glutathionylation in a glutathione peroxidase (GPx)-like manner. In turn, such compounds affect cellular redox environment, described by glutathione disulfide (GSSG)/glutathione (GSH) ratio, and tumor growth. To achieve our goal, we tested several Mn complexes of different chemical and physical properties in cellular and animal flank models of 4T1 breast cancer cell. Four other cancer cell lines were used to substantiate key findings. Results: Joint administration of cationic Mn porphyrin (MnP)-based redox active compounds, MnTE-2-PyP 5+ or MnTnBuOE-2-PyP 5+ with RT and Asc contributes to high H 2 O 2 production in cancer cells and tumor, which along with high MnP accumulation in cancer cells and tumor induces the largest suppression of cell viability and tumor growth, while increasing GSSG/GSH ratio and levels of total S -glutathionylated proteins. Redox-inert MnP, MnTBAP 3− and two other different types of redox-active Mn complexes (EUK-8 and M40403) were neither efficacious in the cellular nor in the animal model. Such outcome is in accordance with their inability to catalyze Asc oxidation and mimic GPx. Innovation: We provided here the first evidence how structure-activity relationship between the catalytic potency and the redox properties of Mn complexes controls their ability to impact cellular redox environment and thus enhance the radiation and ascorbate-mediated tumor suppression. Conclusions: The interplay between the accumulation of cationic MnPs and their potency as catalysts for oxidation of Asc, protein cysteines, and GSH controls the magnitude of their anticancer therapeutic effects.
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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".