Metabolic Activity and mRNA Levels of Human Cardiac CYP450s Involved in Drug Metabolism
Bibliographic record
Abstract
BACKGROUND: Tissue-specific expression of CYP450s can regulate the intracellular concentration of drugs and explain inter-subject variability in drug action. The overall objective of our study was to determine in a large cohort of samples, mRNA levels and CYP450 activity expressed in the human heart. METHODOLOGY: CYP450 mRNA levels were determined by RTPCR in left ventricular samples (n = 68) of explanted hearts from patients with end-stage heart failure. Samples were obtained from ischemic and non-ischemic hearts. In some instances (n = 7), samples were available from both the left and right ventricles. A technique for the preparation of microsomes from human heart tissue was developed and CYP450-dependent activity was determined using verapamil enantiomers as probe-drug substrates. PRINCIPAL FINDINGS: Our results show that CYP2J2 mRNA was the most abundant isoform in all human heart left ventricular samples tested. Other CYP450 mRNAs of importance were CYP4A11, CYP2E1, CYP1A1 and CYP2C8 mRNAs while CYP2B6 and CYP2C9 mRNAs were present at low levels in only some of the hearts analyzed. CYP450 mRNAs did not differ between ischemic and non-ischemic hearts and appeared to be present at similar levels in the left and right ventricles. Incubation of verapamil with heart microsomes led to the formation of nine CYP450-dependent metabolites: a major finding was the observation that stereoselectivity was reversed compared to human liver microsomes, in which the R-enantiomer is metabolized to a greater extent. CONCLUSIONS: This study determined cardiac mRNA levels of various CYP450 isozymes involved in drug metabolism and demonstrated the prevalent expression of CYP2J2 mRNA. It revealed that cardiomyocytes can efficiently metabolize drugs and that cardiac CYP450s are highly relevant with regard to clearance of drugs in the heart. Our results support the claim that drug metabolism in the vicinity of a drug effector site can modulate drug effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".