Understanding the metabolism of the anticancer drug Triapine: electrochemical oxidation, microsomal incubation and in vivo analysis using LC-HRMS
Bibliographic record
Abstract
α-N-Heterocyclic thiosemicarbazones are among the most promising ribonucleotide reductase inhibitors identified so far. Triapine, the most prominent representative of this class of substances, has been investigated in multiple phase I and II clinical trials. With regard to clinical practice, Triapine showed activity against hematological diseases, but ineffectiveness against a variety of solid tumors. However, the reasons are still vague and the amount of ADME (absorption, distribution, metabolism and excretion) data for Triapine available in the literature is very limited. Therefore, different analytical tools were used to investigate the metabolism of Triapine including electrochemical oxidations, liver microsomes and in vivo samples from mice. The main metabolic reactions, observed by all three methods, were dehydrogenation and hydroxylations, confirming that electrochemistry, as a purely instrumental approach, can be applied for the simulation of metabolic pathways. The dehydrogenated metabolite M1 was identified as a thiadiazole ring-closed oxidation product of Triapine. From a biological point of view, M1, as a key metabolite, is of interest since the crucial chemical property of α-N-heterocyclic thiosemicarbazones to bind metal ions is lost and cytotoxicity studies showed no anticancer activity of M1. The in vivo data of the urine samples revealed very high levels of the metabolites and Triapine itself already 15 min after treatment. This clearly indicates that Triapine is rapidly metabolised and excreted, which represents an important step forward to understand the possible reason for the inefficiency of Triapine against solid tumors.
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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) | 0.000 | 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) | 0.000 | 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".