Phenobarbital increases monkey<i>in vivo</i>nicotine disposition and induces liver and brain CYP2B6 protein
Bibliographic record
Abstract
1. CYP2B6 is a drug-metabolizing enzyme expressed in the liver and brain that can metabolize bupropion (Zyban), a smoking cessation drug), activate tobacco-smoke nitrosamines, and inactivate nicotine. Hepatic CYP2B6 is induced by phenobarbital and induction may affect in vivo nicotine disposition, while brain CYP2B6 induction may affect local levels of centrally acting substrates. We investigated the effect of chronic phenobarbital treatment on induction of in vivo nicotine disposition and CYP2B6 expression in the liver and brain of African Green (Vervet) monkeys. 2. Monkeys were split into two groups (n=6 each) and given oral saccharin daily for 22 days; one group was supplemented with 20 mg kg(-1) phenobarbital. Monkeys were given a 0.1 mg kg(-1) nicotine dose subcutaneously before and after treatment. 3. Phenobarbital treatment resulted in a significant, 56%, decrease (P=0.04) in the maximum nicotine plasma concentration and a 46% decrease (P=0.003) in the area under the concentration-time curve. Phenobarbital also increased hepatic CYP2B6 protein expression. In monkey brain, significant induction (P<0.05) of CYP2B6 protein levels was observed in all regions tested (caudate, putamen, hippocampus, cerebellum, brain stem and frontal cortex) ranging from 2-fold to 150-fold. CYP2B6 expression was induced in specific cells, such as frontal cortical pyramidal cells and thalamic neurons. 4. In conclusion, chronic phenobarbital treatment in monkeys resulted in increased in vivo nicotine disposition, and induced hepatic and brain CYP2B6 protein levels and cellular expression. This induction may alter the metabolism of CYP2B6 substrates including peripherally acting drugs such as cyclophosphamide and centrally acting drugs such as bupropion, ecstasy and phencyclidine.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".