Application of a Novel Metallomics Tool to Probe the Fate of Metal-Based Anticancer Drugs in Blood Plasma: Potential, Challenges and Prospects
Bibliographic record
Abstract
Although metallodrugs are used to treat a variety of human disorders and exhibit a remarkable diversity of therapeutic properties, they constitute only a tiny minority of all medicinal drugs that are currently on the market. This undesirable situation must be partially attributed to our general lack of understanding the fate of metallodrugs in the extremely ligand-rich environment of the bloodstream. The challenge of gaining insight into these bioinorganic processes can be overcome by the application of 'metallomics tools', which involve the analysis of biological fluids (e.g., blood plasma) with a separation method in conjunction with multi-element specific detectors. To this end, we have developed a metallomics tool that is based on size-exclusion chromatography (SEC) hyphenated to an inductively coupled plasma atomic emission spectrometer (ICP-AES). After the successful application of SEC-ICPAES to analyze plasma for endogenous copper, iron and zinc-metalloproteins, it was subsequently applied to probe the metabolism of a variety of metal-based anticancer drugs in plasma. The versatility of this metallomics tool is exemplified by the fact that it has provided insight into the metabolism of individual Pt-based drugs, the modulation of the metabolism of cisplatin by sulfur-containing compounds, the metabolism of two metal-based drugs that contain different metals as well as a bimetallic anticancer drug, which contained two different metals. After adding pharmacologically relevant doses of metallodrugs to plasma, the temporal analysis of aliquots by SEC-ICP-AES allows to observe metal-protein adducts, metallodrug-derived degradation products and the parent metallodrug(s). This unique capability allows to obtain comprehensive insight into the fate of metal-based drugs in plasma and can be extended to in vivo studies. Thus, the application of this metallomics tool to probe the fate of novel metalcomplexes that exert the desired biological activity in plasma has the potential to advance more of these to animal/preclinical studies to fully explore the potential that metallodrugs inherently offer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".