Ruthenium Anticancer Compounds with Biologically‐derived Ligands
Bibliographic record
Abstract
Ruthenium chemotherapeutics are rapidly becoming a major area of drug development. This has been motivated partly by the clinical successes of the RuIII complexes NAMI-A, KP1019, and KP1339, but also recently by numerous reports of promising in vitro and in vivo activity from a diverse range of ruthenium complexes. RuII arene complexes have become a major focus of such studies, with other compounds such as polypyridyl, nitrosyl, and multinuclear ruthenium compounds also contributing to the development of this field. For the majority of ruthenium anticancer complexes the origin of their activity remains unclear, and this continues to be a key area of ongoing research. However, many reports have shown that the in vitro and in vivo behaviour of ruthenium compounds can be rationally modified through ligand design. Biologically-derived ligands are particularly attractive in such designs since they can provide a variety of ways to influence the activity of metal-based drugs including: i) unique coordination modes, ii) specific interactions with biological species, iii) increased cellular uptake, and iv) synergistic activity enhancement between the ligand and the metal centre. This potential is now being realised with the development of ruthenium anticancer compounds where a wide variety of bio-relevant ligands have been installed as essential components of complex design. These ligands include amino acids, peptides, proteins, carbohydrates, purines and oligonucleotides, and other biological species and natural products. This is a burgeoning area of research which provides almost unlimited potential for the rational design of new ruthenium anticancer complexes targeting novel transport modes and mechanisms 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".