Anticancer Activity and Catalytic Potential of Ruthenium(II)–Arene Complexes with N,O-Donor Ligands
Bibliographic record
Abstract
The special ability of organometallic complexes to catalyze various transformations might offer new effective mechanisms for the treatment of cancer. Studies that report both the biological properties and the ability of metallic complexes to promote therapeutically relevant catalytic reactions are limited. Herein, we report the anticancer activity and catalytic potential of some ruthenium(II)–arene complexes bearing bidentate Schiff base ligands ( 2a and 2b ) and their reduced analogues ( 5a and 5b, respectively). In comparison to their Schiff base counterparts 2a and 2b, we demonstrate that amine complexes 5a and 5b display (i) a higher in vitro antiproliferative activity on different human cancer cell lines, (ii) a lower rate of hydrolysis, and (iii) an improved initial catalytic rate for the reduction of NAD + to NADH. In contrast to their imine analogues 2a and 2b, we also show that amine complexes 5a and 5b induce the generation of intracellular reactive oxygen species (ROS) in MCF-7 breast cancer cells. Our results highlight the impact that a simple ligand modification such as the reduction of an imine moiety can have on both the catalytic and biological activities of metal complexes. Moreover, the ruthenium complexes reported here display some antiproliferative activity against T47D breast cancer cells, known for their cis -platin resistance.
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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".