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Rapid and quantitative measurement of metabolic stability without chromatography or mass spectrometry.

ABSTRACT: Metabolic stability measurements are a critical component of preclinical drug development. Available measurement strategies rely on chromatography and mass spectrometry, which are expensive and labor intensive. We have developed a general method to determine the metabolic stability of virtually any compound by quantifying cofactors in the mechanism of cytochrome P450 enzymes using fluorescence intensity measurements. While many previous studies have shown that simple measurements of cofactor depletion do not correlate with substrate conversion (i.e., metabolic stability) in P450 systems, the present work employs a reaction engineering approach to simplify the overall rate equation, thus allowing the accurate and quantitative determination of substrate depletion from simultaneous measurements of NADPH and oxygen depletion. This method combines the accuracy and generality of chromatography with the ease, throughput, and real-time capabilities of fluorescence.

SUBMITTER: Traylor MJ

PROVIDER: S-EPMC3179854 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Rapid and quantitative measurement of metabolic stability without chromatography or mass spectrometry.

Traylor Matthew J MJ Ryan Jessica D JD Arnon Eric S ES Dordick Jonathan S JS Clark Douglas S DS

Journal of the American Chemical Society 20110727 37

Metabolic stability measurements are a critical component of preclinical drug development. Available measurement strategies rely on chromatography and mass spectrometry, which are expensive and labor intensive. We have developed a general method to determine the metabolic stability of virtually any compound by quantifying cofactors in the mechanism of cytochrome P450 enzymes using fluorescence intensity measurements. While many previous studies have shown that simple measurements of cofactor dep ...[more]

PMID: 21766815

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Project description:Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry combined with isotope labeling methods are effective for protein and peptide quantification, but limited in their multiplexing capacity, cost-effectiveness and dynamic range. This study investigates MALDI-MS-based quantification of peptide phosphorylation without labeling, and aims to overcome the shot-to-shot variability of MALDI using a mathematical transformation and extended data acquisition times.A linear relationship between the reciprocal of phosphopeptide mole fraction and the reciprocal of phosphorylated-to-unphosphorylated signal ratio is derived, and evaluated experimentally using three separate phosphopeptide systems containing phosphorylated serine, threonine and tyrosine residues: mixtures of phosphopeptide and its des-phospho-analog with known stoichiometry measured by vacuum MALDI-linear ion trap mass spectrometry and fit to the linear model. The model is validated for quantifying in vitro phosphorylation assays with inhibition studies on Cdk2/cyclinA.Dynamic range of picomoles to femtomoles, good accuracy (deviations of 1.5-3.0% from expected values) and reproducibility (relative standard deviation (RSD)?= 4.3-6.3%) are achieved. Inhibition of cyclin-dependent kinase phosphorylation by the classical inhibitors olomoucine and r-roscovitine was evaluated and IC50 values found to be in agreement with reported literature values. These results, achieved with single-point calibration, without isotope or chromatography, compare favorably to those arrived at using isotope dilution (p > 0.5 for accuracy).The mathematical relationship derived here can be applied to a method that we term Double Reciprocal Isotope-free Phosphopeptide Quantification (DRIP-Q), as a strategy for quantification of in vitro phosphorylation assays, the first MALDI-based, isotope- and calibration curve-free method of its type. These results also pave the way for further systematic studies investigating the effect of peptide composition and experimental conditions on quantitative, label-free MALDI.

Project description:BackgroundParkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use.MethodsWe presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts.ResultsA list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD.ConclusionsThis study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.

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Rapid and quantitative measurement of metabolic stability without chromatography or mass spectrometry.

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Rapid and quantitative measurement of metabolic stability without chromatography or mass spectrometry.

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