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

<b>Background/Objectives</b>: 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. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.