Interindividual Variability in Drug Metabolizing Enzymes

Exploring the Interactome of Cytochrome P450 2E1 in Human Liver Microsomes with Chemical Crosslinking Mass Spectrometry

Aiming to elucidate the system-wide effects of the alcohol-induced increase in the content of cytochrome P450 2E1 (CYP2E1) on drug metabolism, we explored the array of its protein-protein interactions (interactome) in human liver microsomes (HLM) with chemical crosslinking mass spectrometry (CXMS). Our strategy employs membrane incorporation of purified CYP2E1 modified with photoreactive crosslinkers benzophenone-4-maleimide and 4-(N-succinimidylcarboxy)benzophenone. Exposure of bait-incorporated HLM samples to light was followed by isolating the His-tagged bait protein and its crosslinked aggregates on Ni-NTA agarose. Analyzing the individual bands of SDS-PAGE slabs of thereby isolated protein with the toolset of untargeted proteomics, we detected the crosslinked dimeric and trimeric complexes of CYP2E1 with other drug-metabolizing enzymes. Among the most extensively crosslinked partners of CYP2E1 are the cytochromes P450 2A6, 2C8, 3A4, 4A11, and 4F2, UDP-glucuronosyltransferases (UGTs) 1A and 2B, fatty aldehyde dehydrogenase (ALDH3A2), epoxide hydrolase 1 (EPHX1), disulfide oxidase 1α (ERO1L), and ribophorin II (RPN2). These results demonstrate the exploratory power of the proposed CXMS strategy and corroborate the concept of tight functional integration in the human drug-metabolizing ensemble through protein-protein interactions of the constituting enzymes.

Assembling the P450 puzzle: on the sources of nonadditivity in drug metabolism

There is an increasing number of indications of an oversimplification in the premise that the cumulative properties of the human drug-metabolizing ensemble represent a simple aggregate of the properties of the constituting enzymes. Recent studies of the functional effects of hetero-association of multiple cytochrome P450 species and their interactions with metabolically related enzymes revealed a tight integration in the drug-metabolizing ensemble. In our opinion, the sources of interindividual variability in drug metabolism can be elucidated only when considering this ensemble as a multienzyme system, the functional parameters of which are determined by interactions between its constituents. In this article, we present a conceptual model providing a mechanistic explanation for the functional effects of the interactions between multiple P450 species and propose a clue to understanding the nonadditive behavior of the drug-metabolizing ensemble.

Nonadditivity in human microsomal drug metabolism revealed in a study with coumarin 152, a polyspecific cytochrome P450 substrate

We closely characterized 7-Dimethylamino-4-trifluromethylcoumarin (Coumarin 152, C152), a substrate metabolized by multiple P450 species, to establish a new fluorogenic probe for the studies of functional integration in the cytochrome P450 ensemble. Scanning fluorescence spectroscopy and LC/MS-MS were used to characterize the products of N-demethylation of C152 and optimize their fluorometric detection. The metabolism of C152 by the individual P450 species was characterized using the microsomes containing cDNA-expressed enzymes. C152 metabolism in human liver microsomes (HLM) was studied in a preparation with quantified content of eleven P450 species. C152 is metabolized by CYP2B6, CYP3A4, CYP3A5, CYP2C19, CYP1A2, CYP2C9, and CYP2C8 listed in the order of decreasing turnover. The affinities exhibited by CYP3A5, CYP2C9, and CYP2C8 were lower than those characteristic to the other enzymes. The presumption of additivity suggests the participation of CYP3A4, CYP2B6, and CYP2C19 to be 84, 8, and 0.2%, respectively. Contrary to this prediction, inhibitory analysis identified CYP2C19 as the principal C152-metabolizing enzyme. We thoroughly characterize C152 for the studies of drug metabolism in HLM and demonstrate the limitations of the proportional projection approach by providing an example, where the involvement of individual P450 species cannot be predicted from their content.