Determination of the active and inactive metabolites of prasugrel in human plasma by liquid chromatography/tandem mass spectrometry
Bibliographic record
Abstract
Two fast and sensitive liquid chromatography/tandem mass spectrometry (LC/MS/MS)-based bioanalytical assays were developed and validated to quantify the active and three inactive metabolites of prasugrel. Prasugrel is a novel thienopyridine prodrug that is metabolized to the pharmacologically active metabolite in addition to three inactive metabolites, which directly relate to the formation and elimination of the active metabolite. After extraction and separation, the analytes were detected and quantified using a triple quadrupole mass spectrometer using positive electrospray ionization. The validated concentration range for the inactive metabolites assay was from 1 to 500 ng/mL for each of the three analytes. Additionally, a 5x dilution factor was validated. The interday accuracy ranged from -10.5% to 12.5% and the precision ranged from 2.4% to 6.6% for all three analytes. All results showed accuracy and precision within +/-20% at the lower limit of quantification and +/-15% at other levels. The validated concentration range for the active metabolite assay was from 0.5 to 250 ng/mL. Additionally, a 10x dilution factor was validated. The interbatch accuracy ranged from -7.00% to 5.98%, while the precision ranged from 0.98% to 3.39%. Derivatization of the active metabolite in blood with 2-bromo-3'-methoxyacetophenone immediately after collection was essential to ensure the stability of the metabolite during sample processing and storage. These methods have been applied to determine the concentrations of the active and inactive metabolites of prasugrel in human plasma.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".