Therapeutic drug monitoring of clozapine in human serum by high-throughput paper spray mass spectrometry
Bibliographic record
Abstract
Introduction: Monitoring the atypical antipsychotic drug clozapine is crucial to ensure patient safety. This article showcases a high-throughput analytical method for measuring clozapine and its primary metabolite norclozapine (N-desmethylclozapine) in serum using paper spray mass spectrometry (PS-MS). Objectives: This study aimed to assess the viability of a PS-MS method for the rapid measurement of clozapine and norclozapine in human serum samples as an alternative to liquid chromatography mass spectrometry (LC-MS). Methods: Serum samples were processed by protein precipitation followed by deposition of the supernatant containing labelled internal standards onto paper spray substrates mounted in cartridges. Analytes were then analyzed using a triple quadrupole mass spectrometer equipped with a commercial paper spray ionization source. The results obtained from the patient samples were compared to those from a validated LC-MS assay. Results: > 0.99) over five days. Between-run precision was below 8 %, and within-run precision did not exceed 10 %. When compared to a validated LC-MS method, the mean bias for 39 patient samples was -9% for clozapine and -1% for norclozapine, with no outliers. Mass spectrometry ion ratio comparisons indicated no interference for patient samples above the lower limit of quantification. There was less than 7 % change in the measured concentrations of both analytes over five days for samples dried on paper substrates. Notably, virtually no maintenance of the MS source was required during this study. Conclusion: This study illustrates the potential of PS-MS for serum drug monitoring in the clinical laboratory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".