Protein Levels of 16 Cytochrome P450s and 2 Carboxyl Esterases Using Absolute Quantitative Proteomics: CYP2C9 and CYP3A4 Are the Most Abundant Isoforms in Human Liver and Intestine, Respectively
Bibliographic record
Abstract
Background/Objectives: Metabolic enzymes are crucial for the detoxification of exogenously administered drugs, especially enzymes expressed in the intestine and the liver. Recent advancements in analytical methodologies enable sensitive and specific quantitative measurements of proteins, facilitating a more accurate evaluation of their expression and relative contribution to drug metabolism. Methods: The aim of the study was to characterize the protein expression levels of 16 Cytochrome P450s (CYP450s) and 2 carboxylesterases (CESs) in human liver and intestinal tissues using absolute quantification by HPLC-MS/MS. Human hepatocytes (HHEP) and human liver microsomes (HLM) were utilized, along with a novel intestinal preparation from cryopreserved human intestinal mucosa (CHIM), to perform proteomic analyses. Results: A comprehensive evaluation of 16 CYP450s and 2 CES enzyme expression in human liver and intestinal tissues is provided to reflect their relative abundance. Among the various in vitro systems evaluated, 14 of 16, 15/16, and 7/16 CYP450 of the isoforms analyzed were detected in HHEP, HLM, and CHIM, respectively. In hepatic systems, CYP2C9 exhibited the highest expression among CYP450 isoforms, a trend consistently observed in both HHEP and HLM. CYP3A4 was the most abundantly expressed isoform in CHIM preparations. Across all systems tested, CES1 and CES2 showed the highest overall protein expression levels, surpassing those of the CYP450s. Conclusions: Our findings demonstrate that the absolute quantification method employed is reliable, producing consistent results across two different in vitro hepatic systems (HHEP and HLM). This study supports the utility of absolute quantification approaches for accurately profiling drug-metabolizing enzymes and provides new, valuable insights to improve in vitro/in vivo extrapolation and more informed predictive pharmacokinetic modeling strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".