In Vitro and In Vivo Studies of Non-Platinum-Based Halogenated Compounds as Potent Antitumor Agents for Natural Targeted Chemotherapy of Cancers
Bibliographic record
Abstract
Based on a molecular-mechanism-based anticancer drug discovery program enabled by an innovative femtomedicine approach, we have found a previously unknown class of non-platinum-based halogenated molecules (called FMD compounds) as potent antitumor agents for effective treatment of cancers. Here, we present in vitro and in vivo studies of the compounds for targeted chemotherapy of cervical, breast, ovarian, and lung cancers. Our results show that these FMD agents led to DNA damage, cell cycle arrest in the S phase, and apoptosis in cancer cells. We also observed that such a FMD compound caused an increase of reduced glutathione (GSH, an endogenous antioxidant) levels in human normal cells, while it largely depleted GSH in cancer cells. We correspondingly found that these FMD agents exhibited no or little toxicity toward normal cells/tissues, while causing significant cytotoxicity against cancer cells, as well as suppression and delay in tumor growth in mouse xenograft models of cervical, ovarian, breast and lung cancers. These compounds are therefore a previously undiscovered class of potent antitumor agents that can be translated into clinical trials for natural targeted chemotherapy of multiple cancers.
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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) | 0.002 | 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".