Determination of CYP450 Expression Levels in the Human Small Intestine by Mass Spectrometry-Based Targeted Proteomics
Bibliographic record
Abstract
The human small intestine can be involved in the first-pass metabolism of drugs. Under this condition, members of the CYP450 superfamily are expected to contribute to drug presystemic biotransformation. The aim of this study was to quantify protein expression levels of 16 major CYP450 isoforms in tissue obtained from nine human organ donors in seven subsections of the small intestine, i.e., duodenum (one section, N = 7 tissue samples), jejunum (three subsections (proximal, mid and distal), N = 9 tissue samples) and ileum (three subsections, (proximal, mid and distal), N = 9 tissue samples), using liquid chromatography tandem mass spectrometry (LC-MS/MS) based targeted proteomics. CYP450 absolute protein expression levels were compared to mRNA levels and enzyme activities by using established probe drugs. Proteins corresponding to seven of sixteen potential CYP450 isoforms were detected and quantified in various sections of the small intestine: CYP2C9, CYP2C19, CYP2D6, CYP2J2, CYP3A4, CYP3A5 and CYP4F2. Wide inter-subject variability was observed, especially for CYP2D6. CYP2C9 (p = 0.004) and CYP2C19 (p = 0.005) expression levels decreased along the small intestine. From the duodenum to the ileum, CYP2J2 (p = 0.001) increased, and a trend was observed for CYP3A5 (p = 0.13). CYP3A4 expression was higher in the jejunum than in the ileum (p = 0.03), while CYP4F2 expression was lower in the duodenum compared to the jejunum and the ileum (p = 0.005). CYP450 protein levels were better correlated with specific isoform activities than with mRNA levels. This study provides new data on absolute CYP450 quantification in human small intestine that could improve physiologically based pharmacokinetic models. These data could better inform drug absorption profiles while considering the regional expression of CYP450 isoforms.
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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) | 0.000 | 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 | 0.000 |
| 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 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".