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Rapid LC-MS drug metabolite profiling using microsomal enzyme bioreactors in a parallel processing format.

ABSTRACT: Silica nanoparticle bioreactors featuring thin films of enzymes and polyions were utilized in a novel high-throughput 96-well plate format for drug metabolism profiling. The utility of the approach was illustrated by investigating the metabolism of the drugs diclofenac (DCF), troglitazone (TGZ), and raloxifene, for which we observed known metabolic oxidation and bioconjugation pathways and turnover rates. A broad range of enzymes was included by utilizing human liver (HLM), rat liver (RLM) and bicistronic human-cyt P450 3A4 (bicis.-3A4) microsomes as enzyme sources. This parallel approach significantly shortens sample preparation steps compared to an earlier manual processing with nanoparticle bioreactors, allowing a range of significant enzyme reactions to be processed simultaneously. Enzyme turnover rates using the microsomal bioreactors were 2-3 fold larger compared to using conventional microsomal dispersions, most likely because of better accessibility of the enzymes. Ketoconazole (KET) and quinidine (QIN), substrates specific to cyt P450 3A enzymes, were used to demonstrate applicability to establish potentially toxic drug-drug interactions involving enzyme inhibition and acceleration.

SUBMITTER: Bajrami B

PROVIDER: S-EPMC2795074 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Rapid LC-MS drug metabolite profiling using microsomal enzyme bioreactors in a parallel processing format.

Bajrami Besnik B Zhao Linlin L Schenkman John B JB Rusling James F JF

Analytical chemistry 20091201 24

Silica nanoparticle bioreactors featuring thin films of enzymes and polyions were utilized in a novel high-throughput 96-well plate format for drug metabolism profiling. The utility of the approach was illustrated by investigating the metabolism of the drugs diclofenac (DCF), troglitazone (TGZ), and raloxifene, for which we observed known metabolic oxidation and bioconjugation pathways and turnover rates. A broad range of enzymes was included by utilizing human liver (HLM), rat liver (RLM) and b ...[more]

PMID: 19904994

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Project description:BackgroundEsophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret's esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication.Methodology/principal findingsSerum metabolite profiles from patients with EAC (n?=?67), BE (n?=?3), HGD (n?=?9) and healthy volunteers (n?=?34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p<0.05) between EAC patients and healthy controls. A partial least-squares discriminant analysis (PLS-DA) model had good accuracy with the area under the receiver operative characteristic curve (AUROC) of 0.82. However, when the results of LC-MS were combined with 8 metabolites detected by nuclear magnetic resonance (NMR) in a previous study, the combination of NMR and MS detected metabolites provided a much superior performance, with AUROC?=?0.95. Further, mean values of 12 of these metabolites varied consistently from healthy controls to the high-risk individuals (BE and HGD patients) and EAC subjects. Altered metabolic pathways including a number of amino acid pathways and energy metabolism were identified based on altered levels of numerous metabolites.Conclusions/significanceMetabolic profiles derived from the combination of LC-MS and NMR methods readily distinguish EAC patients and potentially promise important routes to understanding the carcinogenesis and detecting the cancer. Differences in the metabolic profiles between high-risk individuals and the EAC indicate the possibility of identifying the patients at risk much earlier to the development of the cancer.

Dataset Information

Rapid LC-MS drug metabolite profiling using microsomal enzyme bioreactors in a parallel processing format.

Publications

Rapid LC-MS drug metabolite profiling using microsomal enzyme bioreactors in a parallel processing format.

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