Determination of Sirolimus Blood Concentration Using High-Performance Liquid Chromatography with Ultraviolet Detection
Bibliographic record
Abstract
BACKGROUND: Different HPLC methods have been developed and used to determined sirolimus blood concentrations. These methods show different performance characteristics, mostly related to peak interference, recovery, assay sensitivity, and turnaround times. OBJECTIVE: We adapted, improved, and validated an HPLC method with UV detection for measurement of sirolimus in whole blood clinical samples. METHODS: The standards, quality controls, or patient samples (0.25 or 0.5 mL) and internal standard (desmethoxysirolimus) were extracted with 1-chlorobutane. After evaporation, the extract was reconstituted in a 70% acetonitrile/water mixture and analyzed onto a reverse-phase C18 column at 50 degrees C under a flow rate of 1.0 mL/min in the HPLC system. Ultraviolet detection was performed at 278 nm, with sensitivity setting of 0.010 AUFS. Identification of peaks of interest was by retention time; quantification of sirolimus was based on a peak area ratio. RESULTS: Analytic recovery ranging from 96 to 120% (CV = 3.7 to 16.8%; bias = -4.2 to 16.7%) was observed throughout the assay's linear range (2.5-150.0 ng/mL). The lower limit of quantification for both sample volumes (0.25 or 0.5 mL) was 2.5 ng/mL (CV = 12 and 15%, bias = -1.2 and 4%, respectively). The intra- and interassay imprecision ranged from 6.2 to 14.4% and from 9.1 to 18.6%, with bias ranging from 1.3 to 12.9% and -1.8% to 7.1, for quality control levels of 3, 10, and 20 ng/mL. Whole blood and extracted samples are stable at room temperature and at 4 and -20 degrees C for 1 week and 3 days, respectively. Chromatograms showed good separation free of interfering peaks. A set of 45 samples can be extracted in 2 h, allowing results within 24 h. CONCLUSION: This HPLC-UV method shows good and reproducible performance, satisfying all requirements of an assay designated to be applied in therapeutic drug monitoring strategies after organ transplantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".