A class of iron chelators with a wide spectrum of potent antitumor activity that overcomes resistance to chemotherapeutics
Bibliographic record
Abstract
Novel chemotherapeutics with marked and selective antitumor activity are essential to develop, particularly those that can overcome resistance to established therapies. Iron (Fe) is critical for cell-cycle progression and DNA synthesis and potentially represents a novel molecular target for the design of new anticancer agents. The aim of this study was to evaluate the antitumor activity and Fe chelation efficacy of a new class of Fe chelators using human tumors. In this investigation, the ligands showed broad antitumor activity and could overcome resistance to established antitumor agents. The in vivo efficacy of the most effective chelator identified, di-2-pyridylketone-4,4,-dimethyl-3-thiosemicarbazone (Dp44mT), was assessed by using a panel of human xenografts in nude mice. After 7 weeks, net growth of a melanoma xenograft in Dp44mT-treated mice was only 8% of that in mice treated with vehicle. In addition, no differences in these latter animals were found in hematological indices between Dp44mT-treated mice and controls. No marked systemic Fe depletion was observed comparing Dp44mT- and vehicle-treated mice, probably because of the very low doses required to induce anticancer activity. Dp44mT caused up-regulation of the Fe-responsive tumor growth and metastasis suppressor Ndrg1 in the tumor but not in the liver, indicating a potential mechanism of selective anticancer activity. These results indicate that the novel Fe chelators have potent and broad antitumor activity and can overcome resistance to established chemotherapeutics because of their unique mechanism of action.
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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) | 0.000 | 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".