Propranolol is a mechanism‐based inhibitor of CYP2D and CYP2D6 in humanized CYP2D6‐transgenic mice: Effects on activity and drug responses
Bibliographic record
Abstract
Background and Purpose Genetics and drug interactions contribute to large interindividual variation in human CYP2D6 activity. Here, we have characterized propranolol inhibition of human and mouse CYP2D using transgenic (TG) mice, which express both mouse CYP2D and human CYP2D6, and wild‐type (WT) mice. Our purpose was to develop a method for in vivo manipulation of CYP2D6 enzyme activity which could be used to investigate the role of CYP2D6 in drug‐induced behaviours. Experimental Approach Dextromethorphan metabolism to dextrorphan was used to measure CYP2D activity and to characterize propranolol inhibition in vitro and in vivo. Effects of propranolol pretreatment (24 hr) on serum levels of the CYP2D6 substrate haloperidol and haloperidol‐induced catalepsy were also studied. Key Results Dextrorphan formation velocity in vitro was threefold higher in liver microsomes of TG compared to WT mice. Propranolol acted as a mechanism‐based inhibitor (MBI), inactivating CYP2D in liver microsomes from TG and WT mice, and humans. Pretreatment (24 hr) of TG and WT mice with 20 mg·kg−1 intraperitoneal propranolol reduced dextrorphan formation in vivo and by liver microsomes in vitro. Serum haloperidol levels and catalepsy were increased. Conclusions and Implications Propranolol was a potent MBI of dextrorphan formation in liver microsomes from TG and WT mice, and humans. The inhibition parameters in TG overlapped with those in WT mice and in humans. Inhibition of CYP2D with propranolol in vivo in TG and WT mice altered drug responses, allowing further investigation of variations in CYP2D6 on drug interactions and drug responses.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".