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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".