Cellular Transport Mechanisms of Cytotoxic Metallodrugs: An Overview beyond Cisplatin
Bibliographic record
Abstract
The field of medicinal inorganic chemistry has grown consistently during the past 50 years; however, metal-containing coordination compounds represent only a minor proportion of drugs currently on the market, indicating that research in this area has not yet been thoroughly realized. Although platinum-based drugs as cancer chemotherapeutic agents have been widely studied, exact knowledge of the mechanisms governing their accumulation in cells is still lacking. However, evidence suggests active uptake and efflux mechanisms are involved; this may be involved also in other experimental metal coordination and organometallic compounds with promising antitumor activities in vitro and in vivo, such as ruthenium and gold compounds. Such knowledge would be necessary to elucidate the balance between activity and toxicity profiles of metal compounds. In this review, we present an overview of the information available on the cellular accumulation of Pt compounds from in vitro, in vivo and clinical studies, as well as a summary of reports on the possible accumulation mechanisms for different families of experimental anticancer metal complexes (e.g., Ru Au and Ir). Finally, we discuss the need for rationalization of the investigational approaches available to study metallodrug cellular transport.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".