Genetic and clinical determinants of CYP3A4 activity in patients using 4β‐hydroxycholesterol as an in vivo probe
Bibliographic record
Abstract
There is marked interindividual variability in cytochrome P450 3A (CYP3A) activity. Plasma concentration of 4β‐hydroxycholesterol (4βOH‐C), an endogenous metabolite formed by CYP3A‐mediated hydroxylation of cholesterol, has been proposed as in vivo probe of CYP3A activity. Thus, we characterized the clinical and genetic variables associated with 4βOH‐C concentration in patients (n = 532). Subjects were consented to provide a brief medical history, plasma and DNA for genotyping. 4βOH‐C was measured by liquid‐chromatography tandem mass spectrometry. Predictors of molar ratio of 4βOH‐C to cholesterol (4βOH‐C/C) were assessed by multiple linear regression analysis. The mean 4βOH‐C concentration was 29.3 ng/mL (SD 20.6). 4βOH‐C/C did not appear to be significantly affected by concomitant administration of drugs with CYP3A inducing or inhibitory properties. Of the demographic variables assessed, 4βOH‐C/C was significantly related to body mass index (p < 0.001). Importantly, 4βOH‐C/C was associated with CYP3A4*22 (p < 0.01), CYP3A5*3 (p < 0.005), and POR*28 (cytochrome P450 oxidoreductase, p < 0.05) genotypes. Finally, 4βOH‐C/C concentrations were associated with concentrations of the CYP3A substrates atorvastatin and tamoxifen (p < 0.01). Our data demonstrate 4βOH‐C levels can be effectively used to quantify the in vivo relevance of CYP3A genetic variations and clinical variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".