Method development aspects for the quantitation of pharmaceutical compounds in human plasma with a matrix‐assisted laser desorption/ionization source in the multiple reaction monitoring mode
Bibliographic record
Abstract
The present work investigates various method development aspects for the quantitative analysis of pharmaceutical compounds in human plasma using matrix-assisted laser desorption/ionization and multiple reaction monitoring (MALDI-MRM). Talinolol was selected as a model analyte. Liquid-liquid extraction (LLE) and protein precipitation were evaluated regarding sensitivity and throughput for the MALDI-MRM technique and its applicability without and with chromatographic separation. Compared to classical electrospray liquid chromatography/mass spectrometry (LC/ESI-MS) method development, with MALDI-MRM the tuning of the analyte in single MS mode is more challenging due to interfering matrix background ions. An approach is proposed using background subtraction. With LLE and using a 200 microL human plasma aliquot acceptable precision and accuracy could be obtained in the range of 1 to 1000 ng/mL without any LC separation. Approximately 3 s were required for one analysis. A full calibration curve and its quality control samples (20 samples) can be analyzed within 1 min. Combining LC with the MALDI analysis allowed improving the linearity down to 50 pg/mL, while reducing the throughput potential only by two-fold. Matrix effects are still a significant issue with MALDI but can be monitored in a similar way to that used for LC/ESI-MS analysis.
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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 | 0.005 | 0.004 |
| 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".