A High-Performance-Liquid-Chromatography-Based Method for the Determination of Hydroxylated Testosterone Metabolites Formed In Vitro in Liver Microsomes from Gray Seal (Halichoerus grypus)
Bibliographic record
Abstract
A reproducible and sensitive high-performance-liquid-chromatography (HPLC)-based method with UV-vis detection is developed and optimized for the determination of hydroxytestosterone compounds formed via the cytochrome P450 enzyme-mediated metabolism of testosterone. The method is used to characterize and quantitate hydroxytestosterone metabolites formed in vitro via testosterone incubation with hepatic microsomes from the liver of gray seals (Halichoerus grypus). The HPLC method employs a Zorbax Eclipse XDB-C18 column (5 microm, 250- x 4.6-mm i.d.) and a combination of step gradient and solvent systems of mixtures of acetonitrile, methanol, and water. Metabolites are detected at 254 nm. The eluted peaks of 10 testosterone metabolite standards are well-resolved and a flat baseline is maintained over the elution period of the entire chromatogram. The instrumental detection limits (signal-to-noise ratio = 3) of 6beta-, 16beta-, 16alpha-, and 7alpha-hydroxytestostone and androstenedione are 14, 3, 3, 14, and 3 pmol (20 microL injection), respectively. Eleven hydroxytestosterone metabolites are detected after in vitro testosterone incubation with hepatic microsomes of gray seals. Six are identified as 6beta-, 7alpha-, 16alpha-, 16beta-, and 2beta-hydroxytestosterone and androstenedione. In order of abundance, the formation rates are 2100, 39.6, 12.8, 26.2, and 132 pmol/mg protein/min for 6beta-, 7alpha-, 16alpha-, and 16beta-hydroxytestosterone and androstenedione, respectively. The within-day precision (relative standard deviation) is less than 3% for testosterone metabolites. Five relatively substantial peaks are detected but not identified.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".