Analysis of ketoprofen enantiomers in human and rat plasma by hollow-fiber-based liquid-phase microextraction and chiral mobile-phase additive HPLC
Bibliographic record
Abstract
A simple, inexpensive, and efficient preconcentration and cleanup three-phase hollow fiber liquid-phase microextraction method (HF-LPME) was developed for the extraction of the anti-inflammatory ketoprofen (KTP) from human and rat plasma and HPLC enantioseparation of its enantiomers using vancomycin as a chiral mobile-phase additive with an achiral C8 column. The effects of different parameters influencing the efficiency of extraction were optimized for aqueous samples. Under optimized conditions, KTP enantiomers were extracted from 0.5 mL of plasma diluted to 5 mL with salinated and acidified deionized water (pH = 2) with 25 μL of alkalinized acceptor phase (pH = 11) during 30 min at room temperature. The mean recoveries of (−)-(R)- and (+)-(S)-KTP were 72.8% and 70.9%, respectively. The quantification limit was 20 ng/mL with linear response over the 20–2000 ng/mL concentration range for both enantiomers. Assay precision was studied within-day and between-day using 100 ng/mL KTP solutions. For both KTP enantiomers, relative standard deviations were lower than 12%. The proposed microextraction method was applied for the extraction of KTP enantiomers from human and rat plasma samples after oral administration of pure (±)-KTP in 4 mg/kg dosage for rats and 50 mg dosage for humans to assess the enantiospecific bioavailability of KTP enantiomers in their plasma. In vivo inversion studies revealed that the bioavailability of S-KTP is higher than that of the R enantiomers in rat, but they are similar in human plasma. The developed method showed that HF-LPME is a promising technique for sample preparation for the analyses of chiral drugs in biological samples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 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".