Quantification of imatinib and related compounds using capillary electrophoresis‐tandem mass spectrometry with field‐amplified sample stacking
Bibliographic record
Abstract
A quantification method for imatinib (IM), its major metabolite N-desmethyl imatinib (NDI), and a degradation by-product was developed using CE-MS combined with an online concentration technique. The use of multiple reaction monitoring (MRM)-MS/MS further improved the sensitivity of this technology. Liquid-liquid extraction (LLE) using tertiary butyl methyl ether yielded high recovery and reproducibility for the pretreatment of serum samples. The recovery rate exceeded 83% for all three analytes, and was 90% for IM. To improve quantification results, a conductivity-induced online analyte concentration technique, field-amplified sample stacking (FASS), was used. The S/N ratios were improved at least 10-fold when compared with conventional capillary zone electrophoresis. The detection limits were 0.2 ng/mL for IM, 0.4 ng/mL for NDI, and 4 ng/mL for the degradation by-product. These results are superior to those previously obtained by other reported methods. The new method was validated in terms of its selectivity, intra- and interday repeatability and accuracy, and sample storage stability, following the guidelines issued by the European Medicines Agency. Considering the convenient pretreatment procedure (LLE), superior sensitivity, and fast analysis speed (<15 min), this method can be useful in the determination of imatinib levels in blood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".