Selective anticancer copper(<scp>ii</scp>)-mixed ligand complexes: targeting of ROS and proteasomes
Bibliographic record
Abstract
Copper compounds can be alternatives to platinum-based anticancer drugs. This study investigated the effects of a series of ternary copper(II) complexes, [Cu(phen)(aa)(H2O)]NO3·xH2O 1-4 (phen = 1,10-phenanthroline; aa = gly (1), DL-ala (2), sar (3), C-dmg (4)), on metastatic and cisplatin-resistant MDA-MB-231 breast cancer cells and MCF10A non-cancerous breast cells, and some aspects of the mechanisms. These complexes were distinctively more antiproliferative towards and induced greater apoptotic cell death in MDA-MB-231 than in MCF10A cells. 2 and 4 could induce cell cycle arrest only in cancer cells. Further evidence from DCFH-DA assay showed higher induction of reactive oxygen species (ROS) in treated cancer cells but minimal ROS increase in normal cells. DNA double-strand breaks, via a γ-H2AX assay, were only detected in cancer cells treated with 5 μM of the complexes. These complexes poorly inhibited chymotrypsin-like activity in the 20S rabbit proteasome while they did not inhibit the three proteolytic sites of MDA-MB-231 cells at 10 μM. However, the complexes could inhibit degradation of ubiquinated proteins of MDA-MB-231 cells. In addition, compound 4 was found to be effective against cervical (Hela), ovarian (SKOV3), lung (A549, PC9), NPC (Hone1, HK1, C666-1), breast (MCF7, T47D), lymphoma and leukemia (Nalmawa, HL60) and colorectal (SW480, SW48, HCT118) cancer cell lines with IC50 values (24 h) in the 1.7-19.0 μM range. Single dose NCI60 screening of 4 showed the complex to be highly cytotoxic to most cancer cell types and more effective than cisplatin.
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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".