Simultaneous enantiomeric analysis of pharmacologically active compounds in environmental samples by chiral LC–MS/MS with a macrocyclic antibiotic stationary phase
Bibliographic record
Abstract
This paper presents a multi‐residue method for direct enantioselective separation of chiral pharmacologically active compounds in environmental matrices. The method is based on chiral liquid chromatography and tandem mass spectrometry detection. Simultaneous chiral discrimination was achieved with a macrocyclic glycopeptide‐based column with antibiotic teicoplanin as a chiral selector working under reverse phase mode. For the first time, enantioresolution was reported for metabolites of ibuprofen: carboxyibuprofen and 2‐hydroxyibuprofen with this chiral stationary phase. Moreover, enantiomers of chloramphenicol, ibuprofen, ifosfamide, indoprofen, ketoprofen, naproxen and praziquantel were also resolved. The overall performance of the method was satisfactory in terms of linearity, precision, accuracy and limits of detection. The method was successfully applied for monitoring of pharmacologically active compounds at enantiomeric level in influent and effluent wastewater and in river water. In addition, the chiral recognition and analytical performance of the teicoplanin‐based column was critically compared with that of the α1‐acid glycoprotein chiral stationary phase. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".