A Validated Enantioselective Assay for the Determination of Ibuprofen in Human Plasma Using Ultra Performance Liquid Chromatography with Tandem Mass Spectrometry (UPLC-MS/MS)
Bibliographic record
Abstract
A modified ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for the quantitation of ibuprofen enantiomers in human plasma. Ibuprofen and flurbiprofen (internal standard) were extracted from human plasma at acidic pH, using a single-step liquid-liquid extraction with methyl-tert-butyl ether. The enantiomers of ibuprofen and flurbiprofen were derivatized to yield the corresponding diastereomers. Chromatographic separation was achieved using a phenyl column with a run time of 20 min. (R)- and (S)-ibuprofen were quantitated at the multiple reaction monitoring (MRM) transition of m/z 360.2 ? 232.1, and (R)- and (S)-flurbiprofen were monitored at the MRM transition of m/z 398.3 ? 270.1. The method was validated for accuracy, precision, linearity, range, limit of quantitation (LOQ), limit of detection (LOD), selectivity, absolute recovery, matrix effect, dilution integrity, and evaluation of carry-over. Accuracy for (R)-ibuprofen ranged between –11.8% and 11.2%, and for (S)-ibuprofen between –8.6% and –0.3%. Precision for (R)-ibuprofen was ≤ 11.2%, and for (S)-ibuprofen ≤ 7.0%. The calibration curves were weighted (1/X2, n = 7) and were linear with r2 for (R)-ibuprofen ≥ 0.988 and for (S)-ibuprofen ≥ 0.990. The range of the method was 50 to 5000 ng/mL with the LOQ of 50 ng/mL, and LOD of 1 ng/mL, for (R)- and (S)-ibuprofen requiring 100 µL of sample. The method was applied successfully to a pharmacokinetic study with the administration of a single oral dose of ibuprofen capsules to human subjects.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".