N-Acetyl-l-cysteine modulates the metabolism of cis-platin in human plasma in vitro
Bibliographic record
Abstract
Cis-Platin (CP) is a remarkably effective Pt-based anticancer drug, but it also exhibits severe toxic side-effects, including nephrotoxicity and ototoxicity. Previous studies conducted with mammalian model organisms have clearly demonstrated that the intravenous administration of N-acetyl-L-cysteine (NAC) or sodium thiosulfate (STS) along with CP can significantly reduce these toxic side effects. A molecular understanding of the biochemical events that unfold in the bloodstream when these 'ameliorating agents' and CP are co-administered, therefore, constitutes an important first step in devising novel strategies to ultimately improve the quality of life of patients undergoing treatment with CP. We have employed size exclusion chromatography (SEC) coupled on-line to an inductively coupled plasma atomic emission spectrometer (ICP-AES) to visualize how NAC affects the metabolism of CP in human plasma (obtained from healthy male volunteers) in vitro. Clinically relevant doses of CP and NAC were added to plasma at various NAC : CP molar ratios and the Pt-distribution was determined after 10 and 50 min. The results revealed that a putative Pt-NAC complex was formed in plasma with NAC : CP molar ratios ≥ 50 : 1 and that plasma protein binding of CP-derived Pt-species was marginally affected by NAC. In addition, the anti-tumor active CP remained in plasma for more than 50 min. Furthermore, NAC (but not CP) adversely affected the integrity of Fe and Zn plasma metalloproteins in a dose and a time dependent manner. Based on these in vitro data, NAC appears to be a less ideal ameliorating agent to mitigate CP toxicity compared to STS.
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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".