Characterization of Differential Tissue Abundance of Major Non-CYP Enzymes in Human

Abstract

The availability of assays that predict the contribution of cytochrome P450 (CYP) metabolism allows for the design of new chemical entities (NCEs) with minimal oxidative metabolism. These NCEs are often substrates of non-CYP drugmetabolizing enzymes (DMEs), such as UDP-glucuronosyltransferases (UGTs), sulfotransferases (SULTs), carboxylesterases (CESs), and aldehyde oxidase (AO). Nearly 30% of clinically approved drugs are metabolized by non-CYP enzymes. However, knowledge about the differential hepatic versus extrahepatic abundance of non-CYP DMEs is limited. In this study, we detected and quantified the protein abundance of eighteen non-CYP DMEs (AO, CES1 and 2, ten UGTs, and five SULTs) across five different human tissues. AO was most abundantly expressed in the liver and to a lesser extent in the kidney; however, it was not detected in the intestine, heart, or lung. CESs were ubiquitously expressed with CES1 being predominant in the liver, while CES2 was enriched in the small intestine. Consistent with the literature, UGT1A4, UGT2B4, and UGT2B15 demonstrated liver-specific expression, whereas UGT1A10 expression was specific to the intestine. UGT1A1 and UGT1A3 were expressed in both the liver and intestine; UGT1A9 was expressed in the liver and kidney; and UGT2B17 levels were significantly higher in the intestine than in the liver. All five SULTs were detected in the liver and intestine, and SULT1A1 and 1A3 were detected in the lung. Kidney abundance was the most variable among the studied tissues, and overall, high interindividual variability (>15-fold) was observed for UGT2B17, CES2 (intestine), SULT1A1 (liver), UGT1A9, UGT2B7, and CES1 (kidney). These differential tissue abundance data can be integrated into physiologically based pharmacokinetic (PBPK) models for the prediction of non-CYP drug metabolism and toxicity in hepatic and extrahepatic tissues.

Contribution of Extrahepatic Aldehyde Oxidase Activity to Human Clearance

“…Thus, using the AOX modulated activity model, estimates of active AOX enzyme abundance in each tissue's S9 fraction were obtained. It was found that the active AOX abundance in the liver (21 pmol/mg S9 protein) is in general agreement with measured liver AOX abundance (11.96+/-4.46 pmol/mg S9 protein) described by Basit and colleagues (Basit et al, 2020). In addition, the published extra-hepatic AOX abundance levels for the heart and intestine were below the limit of quantification, which agrees with much lower estimated active AOX levels that we obtained using AOX activity (Table 2).…”

“…This model was able to accurately capture (adj. R 2 = 0.99) 4-oxo-carbazeran formation time-course data in all tissue S9 incubations (Figure 3), with estimated parameter values captured in The tissue fractions tested were chosen due to the identified expression levels of AOX in the tissue (Basit et al, 2020), the volume of tissue of the organ (Davies and Morris, 1993), and/or the lack of blood flow restriction pertaining to the tissue. While it would have been desirable to study additional human tissues beyond the scope of this work, it is unlikely, based on these criteria that any would contribute significantly to whole-body AOX clearance.…”

Quantitative Investigation of Irinotecan Metabolism, Transport, and Gut Microbiome Activation

“…To our knowledge, this is the first study that investigated the quantitative effect of the interplay of host enzymes, transporters, and the gut microbiome on irinotecan tissuespecific disposition. Irinotecan hydrolysis to SN-38 was significantly higher in the intestine as compared to the liver (Figure 1), which corroborates with the higher intestinal abundance of CES2, the primary hydrolytic enzyme involved in SN-38 formation (Basit et al, 2020).…”

Tissue-Specific Proteomics Analysis of Anti-COVID-19 Nucleoside and Nucleotide Prodrug-Activating Enzymes Provides Insights into the Optimization of Prodrug Design and Pharmacotherapy Strategy

“…enzyme was similar to that of its native counterpart in vivo. 24 Summation of the individual enzyme contributions in a specific tissue was then used to estimate the total metabolic activity in that tissue ( eq 4 ). The calculation process was exemplified by sofosbuvir hydrolysis rate prediction as shown in Supplementary Table 3 .…”

“… 21 − 23 A recent proteomics study quantified various noncytochrome P450 (CYP) drug-metabolizing enzymes (DMEs) in assorted human tissues. 24 The expression patterns of many non-CYP DMEs differed markedly between organs, indicating tissue-dependent metabolism of non-CYP substrate drugs. However, prodrug-activating enzymes, such as serine hydrolases, have not been fully characterized in the human tissues most relevant to SARS-CoV-2 infection.…”