Fortification of blood plasma from cancer patients with human serum albumin decreases the concentration of cisplatin-derived toxic hydrolysis products in vitro
Bibliographic record
Abstract
While cisplatin (CP) is still one of the world's bestselling anticancer drugs, its intravenous administration is inherently associated with severe, dose limiting toxic side-effects. Although the molecular basis of the latter are not well understood, biochemical transformations of CP in blood and the interaction of the generated platinum species with plasma proteins likely play a critical role since these processes will ultimately determine which platinum-species reach the intended tumor cells as well as non-target cells. Compared to healthy subjects, cancer patients often have decreased plasma human serum albumin (HSA) concentrations. Little, however, is known about how the plasma HSA concentration will affect the metabolism of CP. To gain insight, we obtained blood plasma from healthy adults (n = 20, 42 ± 4 g HSA per L) and pediatric cancer patients (n = 11, 26 ± 7 g HSA per L). After the incubation of plasma at 37 °C, a pharmacologically relevant dose of CP was added and the Pt-distribution therein was determined by size-exclusion chromatography coupled on-line to an inductively coupled plasma atomic emission spectrometer. At the 2 h time point, a 5.9% increase of toxic CP-derived hydrolysis products was detected in pediatric cancer patient plasma, while 9.8% less platinum was protein bound compared to plasma from healthy controls. These in vitro results suggest that the elevated concentration of highly reactive free CP-derived hydrolysis products in plasma may cause the toxic side-effects in cancer patients. More importantly, the deliberate increase of the plasma HSA concentration in cancer patients prior to CP treatment would represent a simple strategy to possibly alleviate the fraction of patients that suffer from drug induced toxic side-effects.
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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".