Fast Liquid Chromatography-Tandem Mass Spectrometry Method for Routine Assessment of Irinotecan Metabolic Phenotype
Bibliographic record
Abstract
Irinotecan (CPT-11) is an anticancer drug with a complex in vivo metabolism widely used in the treatment of colon cancer. The assessment of the CPT-11 metabolic phenotype is an important clinical component for personalizing the administered dose. In this study, we report the development of a rapid and sensitive liquid chromatography-tandem mass spectroscopy method for the simultaneous quantitation of CPT-11 and its major metabolites generated by hydrolysis (SN-38, active metabolite) or through oxidative (NPC and APC) and glucuronidation (SN-38G) pathways. The method used a small volume of plasma and is based on simple protein plasma precipitation with two volumes of acetonitrile containing camptothecine as an internal standard. Analytes extracted in the supernatant fraction were converted to the lactone form and further separated by an acetonitrile gradient on a Kinetex C18 (50 × 2 mm) column in the presence of 0.01% formic acid. The quantitative measurement was performed by an API 4000/Qtrap operating in the triple-quadruple mode. The Multiple Reaction Monitoring transitions were: m/z 587→167 for CPT-11, m/z 393→349 for SN-38, m/z 619→393 for APC, m/z 519→393 for NPC, and m/z 569→393 for SN-38G. The mean overall recovery in plasma was in the range of 78% to 86% with a low matrix suppression effect (9.0% or less). The lower limit of quantitation was 0.2 ng/mL for NPC and SN-38 and 0.5 ng/mL for SN-38G, APC, and CPT-11. Linearity was checked up to 2000 ng/mL, whereas the intra- and interday accuracy and precision for both the parent drug and its metabolites was -3.7% to 13.8% and 3.7% to 13.1%, respectively. The method proved to be robust and suitable for therapeutic drug monitoring as well as for pharmacogenetic-pharmacokinetic correlation studies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".