Interaction of Oxaliplatin, Cisplatin, and Carboplatin with Hemoglobin and the Resulting Release of a Heme Group
Bibliographic record
Abstract
Oxaliplatin, carboplatin, and cisplatin are widely used to treat a number of cancers. While their DNA adducts are believed to cause cell death, the involvement of their protein adducts in the toxicity and action of these drugs is unclear. Here, we report the interactions of hemoglobin (Hb) with the three platinum (Pt) drugs, demonstrating the formation of Hb-Pt complexes and the release of a heme group from Hb. Oxaliplatin (0.05 microM) was able to form three major complexes with Hb (3-10 microM) after 1 h of incubation at room temperature, and these complexes accounted for approximately 60% of the total oxaliplatin. Cisplatin and carboplatin formed one major and two minor complexes only after 24 and 96 h of incubation, respectively. Incubation of these Pt drugs (0.05-10 microM) with whole blood of healthy volunteers and the analysis of red blood cells confirmed the relative ability of these Pt drugs binding to Hb. For the whole blood samples incubated with oxaliplatin and cisplatin for 24 h, only protein complexes were detected in red blood cells, indicating a complete binding of oxaliplatin and cisplatin to the protein. In contrast, carboplatin was partially bound; both the free and the protein-bound carboplatin species were detected in red blood cells. The binding of the Pt drugs to Hb was accompanied by the release of a heme group from Hb, which was monitored by size fractionation, chromatographic separation, and selective detection of both Pt- and iron (Fe)-containing molecular species. The released heme was further identified by size fractionation and nanospray mass spectrometry. The findings of the Pt drug interaction with Hb and the dissociation of heme from Hb are potentially useful for a better understanding of the toxicity and side effects of these chemotherapeutic drugs.
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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) | 0.000 | 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 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".