Enantiospecific Analysis of 8‐Prenylnaringenin in Biological Fluids by Liquid‐Chromatography‐Electrospray Ionization Mass Spectrometry: Application to Preclinical Pharmacokinetic Investigations
Bibliographic record
Abstract
8-Prenylnaringenin (8PN) is a naturally occurring bioactive chiral prenylflavonoid found most commonly in the female flowers of hops (Humulus lupulus L.). A stereospecific method of analysis for 8PN in biological fluids is necessary to study the pharmacokinetic disposition of each enantiomer. A novel and simple liquid chromatographic-electrospray ionization-mass spectrometry (LC-ESI-MS) method was developed for the simultaneous determination of R- and S-8PN in rat serum and urine. Carbamazepine was used as the internal standard (IS). Enantiomeric resolution of 8PN was achieved on a Chiralpak(®) AD-RH column with an isocratic mobile phase consisting of 2-propanol and 10 mM ammonium formate (pH 8.5) (40:60, v/v) and a flow rate of 0.7 mL/min. Detection was achieved using negative selective ion monitoring (SIM) of 8PN at m/z 339.15 for both enantiomers and positive SIM m/z at 237.15 for the IS. The calibration curves for urine were linear over a range of 0.01-75 µg/mL and 0.05-75 µg/mL for serum with a limit of quantification of 0.05 µg/mL in serum and 0.01 µg/mL in urine. The method was successfully validated showing that it was sensitive, reproducible, and accurate for enantiospecific quantification of 8PN in biological matrices. The assay was successfully applied to a preliminary study of 8PN enantiomers in rat.
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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 | 0.000 | 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) | 0.001 | 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".