Genetic epidemiology of induced CYP3A4 activity
Bibliographic record
Abstract
AIM: The cytochrome P450 3A4 (CYP3A4) enzyme is implicated in the metabolism of more than 50% of all prescribed medications and its activity - including induced or inhibited activity - is deemed to be a crucial determinant of interindividual variability in drug disposition, poor therapeutic efficacy, and adverse response to medication. METHODS: We used the classical twin model in conjunction with an induction experiment to uncover the relative contribution of genetic and environmental factors to interindividual variation in induced CYP3A4 activity. A total of 367 healthy twins participated in the study. Each volunteer was administered a potent inducer of CYP3A4 (St John's Wort) for 14 days and the activity of CYP3A4 was quantified through the metabolism of the exogenously administered probe drug quinine sulfate. RESULTS: Baseline and induced CYP3A4 activity were highly variable with a seven-fold and 11-fold difference among our population, respectively. Alcohol consumption, BMI, and smoking were significantly associated with induced CYP3A4 activity, collectively explaining 20% of the variation (P<1×10(-4)). The narrow-sense heritability of induced CYP3A4 activity was estimated at 66%, whereas the remainder of the variation was attributed to unique environmental factors. CONCLUSION: To our knowledge, this is the first genetic epidemiological study of induced CYP3A4 activity. Our results motivate further research to identify common and rarer genetic variants that underpin the heritable component of variation in induced CYP3A4 activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".