Comparative hydrolysis and plasma protein binding of cis-platin and carboplatin in human plasma in vitro
Bibliographic record
Abstract
Platinum-based anti-cancer drugs are widely used to treat cancer in patients, but they also exhibit severe toxic side-effects. Considering that cis-platin and carboplatin are intravenously administered, their biotransformations in the bloodstream are likely to be directly involved in determining their toxic side-effects, but they are poorly understood. We added pharmacologically relevant doses of cis-platin or carboplatin to human plasma from healthy male or female volunteers in vitro at 37 °C and determined the platinum-distribution in plasma after 5 min, 3 h and 24 h using size exclusion chromatography-inductively coupled plasma atomic emission spectrometry (SEC-ICP-AES). The results revealed a negligible inter-individual variation of the platinum-distribution between males and females and faster hydrolysis of cis-platin than carboplatin. Related to this, 95% of platinum was protein-bound 24 h after the addition of cis-platin to plasma, whereas 40% of platinum was protein-bound in the case of carboplatin. Interestingly, cis-platin and carboplatin-derived platinum species appeared to bind to the same 3 plasma proteins at the 3 h time point and thereafter. The analysis of cis-platin and carboplatin-spiked phosphate buffered saline (PBS) revealed a common platinum-containing hydrolysis product that was also detected in plasma. Since cis-platin is associated with more toxic side-effects in patients than carboplatin (even though it is administered at lower doses), our in vitro data suggest that the toxic side-effects of the investigated platinum-drugs may be predominantly determined by the indiscriminate translocation of the parent drugs to malignant and healthy cells. This information may help to mitigate the toxic side-effects of platinum-containing drugs by devising strategies to delay the influx of the parent drugs into non-target tissues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".