Plasma Cabozantinib Level Measurement in Patients with Renal Cell and Hepatocellular Carcinomas Using a Simple HPLC–UV Method Suitable for Clinical Application
Bibliographic record
Abstract
Cabozantinib, which is used to treat renal cell and hepatocellular carcinomas, is often associated with dose-dependent adverse events. Monitoring the levels of cabozantinib in the blood may maximize the therapeutic effect and prevent serious adverse events. In this study, we developed a high-performance liquid chromatography–ultraviolet (HPLC–UV) method of measuring plasma cabozantinib concentration. Human plasma samples (50 µL) were processed by simple deproteinization with acetonitrile, followed by chromatographic separation on a reversed-phase column with an isocratic mobile phase of 0.5% KH₂PO4 (pH 4.5) and acetonitrile (43:57, v/v) at a flow rate of 1.0 mL/min, with a 250 nm ultraviolet detector. The calibration curve was linear over the concentration range (0.05–5 µg/mL) with a coefficient of determination of 0.99999. The accuracy of the assay ranged from −4.35% to 0.98%, and recovery was >96.04%. The measurement time was 9 min. These findings confirm the effectiveness of this HPLC–UV method for cabozantinib quantification in human plasma, which is sufficiently simple for use for monitoring patients in clinical settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".