The different metabolism of morusin in various species and its potent inhibition against UDP-glucuronosyltransferase (UGT) and cytochrome p450 (CYP450) enzymes
Bibliographic record
Abstract
1. The aim of this study was to investigate the inhibitory effect of morusin on Glucuronosyltransferase (UGT) isoforms and cytochrome P450 enzymes (CYP450s). We also investigated the metabolism of morusin in human, rat, dog, monkey, and minipig liver microsomes. 2. 100 μM of morusin exhibited strong inhibition on all UGTs and CYP450s. The half inhibition concentration (IC50) values for CYP3A4, CYP1A2, CYP2C9, CYP2E1, UGT1A6, UGT1A7, and UGT1A8 were 2.13, 1.27, 3.18, 9.28, 4.23, 0.98, and 3.00 μM, and the inhibition kinetic parameters (Ki) were 1.34, 1.16, 2.98, 6.23, 4.09, 0.62, and 2.11 μM, respectively. 3. Metabolism of morusin exhibited significant species differences. The quantities of M1 from minipig, monkey, dog, and rat were 7.8, 11.9, 2.0, and 6.3-fold of human levels. The Km values in HLMs, RLMs, MLMs, DLMs, and PLMs were 7.84, 22.77, 14.32, 9.13, and 22.83 μM, and Vmax for these species were 0.09, 1.23, 1.43, 0.15, and 0.75 nmol/min/mg, respectively. CLint (intrinsic clearance) values (Vmax/Km) for morusin obeyed the following order: monkey > rat > minipig > dog > human. CLH (hepatic clearance) values for humans, dogs, and rats were calculated to be 8.28, 17.38, and 35.12 mL/min/kg body weight, respectively. 4. This study provided vital information to understand the inhibitory potential and metabolic behavior of morusin among various species.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".