The effects of UDP-sugars, UDP and Mg<sup>2+</sup>on uridine diphosphate glucuronosyltransferase activity in human liver microsomes
Bibliographic record
Abstract
1. The UDP-glucuronosyltransferase (UGT) enzymes are important in the metabolism, elimination and detoxification of many xenobiotics and endogenous compounds. As extrapolation of in vitro kinetics of drug metabolizing enzymes to predict in vivo clearance rates becomes more sophisticated, it is important to ensure proper optimization of enzyme assays. The luminal location of the enzyme active site (i.e. latency), and the complexity of UGT kinetics, results in consistent under-prediction of clearance of drugs metabolized by glucuronidation.2. We examined inhibition of UGT activity in alamethicin-disrupted human liver microsomes (HLM) by uridine diphosphate (UDP), a UGT reaction product, and its reversal by Mg2+ ions. We also determined whether UDP-sugars other than the co-substrate UDP-glucuronic acid (UDP-GlcA) affected glucuronidation.3. We show that other UDP-sugars do not significantly influence glucuronidation. We also demonstrate that UDP inhibits HLM UGT activity and that this is reversed by including Mg2+ in the assay. The Mg2+ effect can be offset using EDTA, and is dependent on the concentration of UDP-GlcA in the assay.4. We propose that formation of a Mg2+–UDP complex prevents UDP from affecting the enzyme. Our results suggest that 5 mM UDP-GlcA and 10 mM Mg2+ be used for UGT assays in fully disrupted HLM.
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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) | 0.000 | 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 | 0.001 |
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".