Pharmacokinetics and metabolism of icaritin in rats by UPLC‐MS/MS
Bibliographic record
Abstract
Abstract Icaritin (ICT) has distinct bioactivities, especially known for its beneficial effects on bone‐related degenerative disorders; however, its pharmacokinetic properties remain unknown. A novel developed UPLC‐MS/MS method for the determination of ICT and its main metabolite glucuronidated icaritin (GICT) was firstly applied to pharmacokinetic and metabolism studies of ICT in female rats, which were intraperitoneally given 40 mg/kg ICT. Following the protein precipitation of plasma samples with acetonitrile, ICT and GICT were separated on a C18 column using gradient elution mode and quantified in the multiple reaction monitoring mode. The linearities were acceptable for ICT (r = 0.9960) and GICT (r = 0.9968), and the lower limit of quantification values was 0.5 and 5 ng/ml, respectively. The accuracy fell in the range of 92.0%–103.1% and precisions were within 9.5%. Good linearity, accuracy, precision, and recovery were achieved for the UPLC‐MS/MS method. ICT was predominantly and rapidly biotransformed to GICT which was slowly eliminated in vivo with a terminal half‐life value of 4.51 hr. Pharmacokinetics of pure ICT eliminated biotransformation interference of Epimedium extract and disclosed genuine pharmacokinetic manner of ICT, as well as firstly elucidated low concentration and bioavailability of ICT in rat plasma.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".