Quantification of Epoxyeicosatrienoic acids Enantiomers: The development of reliable and practical liquid chromatography mass Spectrometry assay
Bibliographic record
Abstract
• Validated assay for arachidonic acid (AA)-underived chiral epoxy-metabolites. • A reliable and reproducible chiral analysis for epoxy-metabolites of AA. • Enantioselective analysis of epoxy-metabolites formed by human liver microsomes. • The assay showed acceptable linearity, precision, and accuracy. Epoxyeicosatrienoic acids (EETs) are increasingly recognized as key metabolites in the arachidonic acid (AA) metabolic pathway. EETs are epoxy derivatives of AA with two chiral centers formed by cytochrome P450 (CYP) enzymes. EETs have reported biological activities as racemates; however, knowledge on specific optical isomers of EET is lacking. A main reason is the absence of practical assay to quantify EETs isomers associated with specific pathological conditions and enzymes. The reported underivatized chiral LC-MS/MS assays utilize different mobile phases and flow rates or required long run times to achieve separation of EET stereoisomers. Others incorporated a derivatization step before the separation of EETs in their assays. Therefore, the objective of this study was to develop and validate a stereoselective assay for the simultaneous quantitation of underivatized EET enantiomers using Liquid Chromatography Mass Spectrometry (LC-MS/MS) with an optimum baseline separation using binary mobile phase and gradient elution. Herein, we report the development and validation of an LC-MS/MS assay, and its application to quantify the formation of EET enantiomers mediated by human liver microsomes. Assay linearity extends over 10–600 ng/mL with r 2 > 0.99 for all EETs enantiomers. The inter-run accuracy was within ± 15 %, and precision was ≤ 15 %, and < 20 % for the LLOQ. The matrix effect for the current assay was within ≤ ±20 %, and the mean recovery for quantitative methods was 70–125 %. The assay proved to be reliable and practical for chiral analysis.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".