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At least 253 records · Page 14Linked to original sources
PubMed2003

A comparison of the quantitative methods for the analysis of the platinum-containing anticancer drug [cis-[amminedichloro(2-methylpyridine)]platinum(II)] (ZD0473) by HPLC coupled to either a triple quadrupole mass spectrometer or an inductively coupled plasma mass spectrometer.

The use of high performance liquid chromatography coupled with inductively coupled plasma mass spectrometry (HPLC-ICPMS) as means for the quantitative determination of ZD0473, a platinum anticancer drug, and its related biologically active "aqua" compounds in biofluid samples is described. The performance of the resulting HPLC-ICPMS method was compared with that of a conventional HPLC-triple quadrupole mass spectrometer-based (HPLC-MS/MS) system for properties such as limit of detection, linearity, and reproducibility using spiked samples. The methods were then applied to the determination of plasma ultrafitrate concentrations of ZD0473 in dog plasma samples obtained following intravenous and oral administration at 0.5 and 6 mg/kg, respectively. These experiments showed that both methods were capable of providing accurate and precise results but that the HPLC-ICPMS method had advantages of extended linear range and superior sensitivity, providing a limit of quantification of 0.1 ng/mL for ZD0473, as compared to 5 ng/mL using the current HPLC-MS/MS method. In addition, by using a single combined HPLC-ICPMS/MS/MS system, it was possible to determine the relative MS/MS response of the aqua compounds for the first time.

Animals↗
PubMed2003

Advancing NMR sensitivity for LC-NMR-MS using a cryoflow probe: application to the analysis of acetaminophen metabolites in urine.

Cryogenic cooling of the NMR radio frequency coils and electronics to give greatly enhanced sensitivity is arguably the most significant recent advance in NMR spectroscopy. Here we report the first cryogenic probe built in flow configuration and demonstrate the application to LC-NMR-MS studies. This probe provides superior sensitivity over conventional noncryogenic flow NMR probes, allowing the use of 100 microL of untreated urine (40% less material than previous studies that required preconcentration) and yet revealing drug metabolites hitherto undetected by LC-NMR-MS at 500 MHz. Besides the known sulfate and glucuronide metabolites, previously undetected metabolites of acetaminophen were directly observable in a 15-min on-flow experiment. Simultaneous MS data also provided knowledge on the NMR-silent functional moieties. Further, stop-flow LC-NMR-MS experiments were conducted for greater signal-to-noise ratios on minor metabolites. The cryoflow probe enables the NMR analysis of lower concentrations of metabolites than was previously possible for untreated biofluids. This strategy is generally applicable for samples containing mass-limited analytes, such as those from drug metabolism studies, biomarker and toxicity profiling, impurity analysis, and natural product analysis.

Acetaminophen↗
PubMed2003

Screening of biomarkers in rat urine using LC/electrospray ionization-MS and two-way data analysis.

Biofluids, like urine, form very complex matrixes containing a large number of potential biomarkers, that is, changes of endogenous metabolites in response to xenobiotic exposure. This paper describes a fast and sensitive method of screening biomarkers in rat urine. Biomarkers for phospholipidosis, induced by an antidepressant drug, were studied. Urine samples from rats exposed to citalopram were analyzed using solid-phase extraction (SPE) and liquid chromatography mass spectrometry (LC/MS) analysis detecting negative ions. A fast iterative method, called Gentle, was used for the automatic curve resolution, and metabolic fingerprints were obtained. After peak alignment principal component analysis (PCA) was performed for pattern recognition, PCA loadings were studied as a means of discovering potential biomarkers. In this study a number of potential biomarkers of phospholipidosis in rats are discussed. They are reported by their retention time and base peak, as their identification is not within the scope of the study. In addition to the fact that it was possible to differentiate control samples from dosed samples, the data were very easy to interpret, and signals from xenobiotic-related substances were easily removed without affecting the endogenous compounds. The proposed method is a complement or an alternative to NMR for metabolomic applications.

Animals↗
PubMed2005

Desorption/ionization on silicon nanowires.

Dense arrays of single-crystal silicon nanowires (SiNWs) have been used as a platform for laser desorption/ionization mass spectrometry of small molecules, peptides, protein digests, and endogenous and xenobiotic metabolites in biofluids. Sensitivity down to the attomole level has been achieved on the nanowire surfaces by optimizing laser energy, surface chemistry, nanowire diameter, length, and growth orientation. An interesting feature of the nanowire surface is that it requires lower laser energy as compared to porous silicon and MALDI to desorb/ionize small molecules, therefore reducing background ion interference. Taking advantage of their high surface area and fluid wicking capabilities, SiNWs were used to perform chromatographic separation followed by mass analysis of the separated molecules providing a unique platform that can integrate separation and mass spectrometric detection on a single surface.

Chromatography↗
PubMed2005

Use of selective TOCSY NMR experiments for quantifying minor components in complex mixtures: application to the metabonomics of amino acids in honey.

The application of the traditional methods of multivariate statistics, such as the calculation of principle components, to the analysis of NMR spectra taken on sets of biofluid samples is one of the central approaches in the field of metabonomics. While this approach has proven to be a powerful and widely applicable technique, it has an inherent weakness, in that it tends to be dominated by those chemical species present at relatively higher concentrations. Using a set of commercial honey samples, a comparison of this classical metabonomics approach to one based on the use of the selective TOCSY experiment is presented. While the NMR spectrum of honey and its classical metabonomic analysis is completely dominated by a very few chemical species, specifically alpha-glucose and fructose, the statistical signal carried by minor honey components, such as amino acids, may be accessed using a selective TOCSY-based approach. This approach has the intrinsic virtue that it focuses the statistical analysis on a set of predefined chemical species, which might be chosen for their metabolic significance, and could be composed of either major or minor mixture constituents. Furthermore, the selective TOCSY method allows for more certain chemical identification, acquisition times of approximately 1 min, and accurate quantification of the species contributing to the statistical discriminatory signal.

Amino Acids↗
PubMed2005

Microfluidic motion for a direct investigation of the structural dynamics of glass-forming liquids.

Glass-forming liquids, polymer solutions, and biofluids have additional inertial and elastic macroscopic degrees of freedom that are related to the elasticity of the molecular coils and affect the determination of the structural dynamical parameters. In this work, we propose a new approach for the direct evaluation of the fundamental material parameters (viscosity, fragility, glass transition temperature) of a viscoelastic liquid in a capillary flow inside a microfluidic device. The proposed technique substantially reduces the complexity of the theoretical analysis and provides an evaluation of the most relevant functional parameters of the fluid dynamics. Moreover, the approach allows the investigation of localization phenomena in geometrical confined systems, such as those required in miniaturized devices.

Microfluidic Analytical Techniques↗
PubMed2004

High sensitivity and analyte capture with desorption/ionization mass spectrometry on silylated porous silicon.

Silylation chemistry on porous silicon provides for ultrahigh sensitivity and analyte specificity with desorption/ionization on silicon mass spectrometry (DIOS-MS) analysis. Here, we report that the silylation of oxidized porous silicon offers a DIOS platform that is resistant to air oxidation and acid/base hydrolysis. Furthermore, surface modification with appropriate hydrophobic silanes allows analytes to absorb to the surface via hydrophobic interactions for direct analyte extraction from complex matrixes containing salts and other nonvolatile interferences present in the sample matrix. This enables rapid cleanup by simply spotting the sample onto the modified DIOS target and removing the liquid phase containing the interferences. This approach is demonstrated in the analysis of protein digests and metabolites in biofluids, as well as for the characterizing of inhibitors from their enzyme complex. An unprecedented detection limit of 480 molecules (800 ymol) for des-Arg(9)-bradykinin is reported on a pentafluorophenyl-functionalized DIOS chip.

Amino Acids↗
PubMed2004

Quantification of organic and amino acids in beer by 1H NMR spectroscopy.

The quantification of organic and amino acids in beer using 1H NMR spectroscopy is demonstrated. Quantification was made both by integration of signals in the spectra together with use of calibration references and by use of partial least-squares (PLS) regression. Results from the NMR quantifications were compared with those obtained from determinations by amino acid analysis on HPLC and organic acid analysis by capillary electrophoresis. The described NMR-based methods could satisfactorily be used for quantification of several of the investigated metabolites in beer down to approximately 10 mg/L and for most with a good to high accuracy compared to results obtained by HPLC and capillary electrophoresis (R2 0.90-0.99). This was achieved with a simple sample preparation and one-dimensional 1H NMR spectra obtained in a few minutes. The use of PLS clearly improves the accuracy of the quantifications, based on comparison to results obtained by HPLC and capillary electrophoresis, and furthermore permits the determination of components with partially overlapped signals in the spectrum. NMR spectroscopy in combination with PLS will be a useful tool for the quantification of metabolites, not only in beer but also in other beverages and biofluids.

Amino Acids↗
PubMed2004

Studying rat brain neurochemistry using nanoprobe NMR spectroscopy: a metabonomics approach.

In the present experiments, in vivo microdialysis techniques together with nanoprobe NMR spectroscopy were used to evaluate the neurochemical environment of the rat frontal cortex. Metabonomics techniques of data reduction and pattern recognition were used to examine whether collected neurochemicals were sensitive to tetrodotoxin (TTX), a neurotoxin that when infused into discrete brain regions can help distinguish between the neuronal versus glial origin of neurochemicals in cerebrospinal fluid microdialysate. (1)H NMR spectra recorded on samples collected from the rat frontal cortex before and after an intracortical TTX infusion (10 microM for 60 min) were subjected to multivariate statistical analysis. Glutamate, isoleucine, valine, alanine, and alpha- and beta-hydroxybutyrate were found to have decreased concentrations after the addition of TTX, suggesting that their release is likely from cortical neurons. In contrast, lactate, formate, acetate, glucose, creatinine, pyruvate, and other neurochemicals remained unchanged following local application of TTX. The present findings extend our previous work combining the analytical technology of small-volume nanoprobe NMR spectroscopy with in vivo microdialysis in freely moving animals and show that it is possible to apply metabonomics methodology to this important class of biofluid to monitor changes in neurochemical composition of the rat brain.

Anesthetics, Local↗
PubMed2004

Sample classification based on Bayesian spectral decomposition of metabonomic NMR data sets.

1H NMR spectra of biofluids provides a wealth of biochemical information on the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, the data have been shown to provide information on disease diagnosis and the beneficial and adverse effects of potential therapeutics. Here, a novel approach is described for identifying subsets of spectral patterns in databases of NMR spectra, and it is shown that the intensities of these spectral patterns can be related to the onset and recovery from a toxic lesion in both a time-related and dose-related fashion. These patterns form a new type of combination biomarker for the biological effect under study. The approach is illustrated with a study of liver toxicity in rats using NMR spectra of urine following administration of a model hepatotoxin hydrazine.

Animals↗
PubMed2005

Curve-fitting method for direct quantitation of compounds in complex biological mixtures using 1H NMR: application in metabonomic toxicology studies.

A new software tool has been developed that provides automated measurement of signal intensities in NMR spectra of complex mixtures without using data reduction procedures. The algorithm finds best-fit transformations between signals in reference compound spectra and the corresponding signals in analyte spectra. Unlike other algorithms, it is insensitive to variation in chemical shift and can even be used for relative quantitation of compounds whose identities have not yet been established. Additionally, the parameters of the transformation provide information and error metrics that may assist in the streamlining of quality control. The approach presented is general in scope but has been tested by application to peak quantitation in NMR spectra of biofluids. Replicate NMR measurements of solutions of biologically important compounds at various concentrations were made. Further NMR data were collected on urine samples from human, rat, and mouse, which were "spiked" with reference compound solutions at known concentrations. Finally, existing data from an independent toxicology project involving several hundred samples were analyzed, and the consistency of the measurements for metabolites that give multiple NMR signals was assessed. The results of all these tests give confidence that the technique can be used in automated quantitation of compounds in large NMR data sets with minimal operator intervention.

Animals↗
PubMed2005

Deposition method for preparing SERS-active gold nanoparticle substrates.

Surface-enhanced Raman scattering or SERS, discovered some 20 years ago, has recently become a promising tool for routine biofluid assays in a clinical setting. Many attempts have been made to produce cheap and reproducible SERS-active substrates. In this study, we report on the fabrication of SERS-active substrates through the convective assembly of gold (Au) particles on electrostatically charged glass slides. We show that, by a proper control of the initial particle concentration in an evaporating Au suspension droplet, it is possible to obtain a closely packed colloidal film capable of generating SERS activity. Finally, AFM and SERS measurements of the resulting films reveal comparability in performance with previous silane-immobilized Au colloidal films. The minimum electromagnetic enhancement factor of our films is estimated to be about 2 x 10(4).

Colloids↗
PubMed2006

Second-order peak detection for multicomponent high-resolution LC/MS data.

The first step when analyzing multicomponent LC/MS data from complex samples such as biofluid metabolic profiles is to separate the data into information and noise via, for example, peak detection. Due to the complex nature of this type of data, with problems such as alternating backgrounds and differing peak shapes, this can be a very complex task. This paper presents and evaluates a two-dimensional peak detection algorithm based on raw vector-represented LC/MS data. The algorithm exploits the fact that in high-resolution centroid data chromatographic peaks emerge flanked with data voids in the corresponding mass axis. According to the proposed method, only 4 per thousand of the total amount of data from a urine sample is defined as chromatographic peaks; however, 94% of the raw data variance is captured within these peaks. Compared to bucketed data, results show that essentially the same features that an experienced analyst would define as peaks can automatically be extracted with a minimum of noise and background. The method is simple and requires a priori knowledge of only the minimum chromatographic peak width-a system-dependent parameter that is easily assessed. Additional meta parameters are estimated from the data themselves. The result is well-defined chromatographic peaks that are consistently arranged in a matrix at their corresponding m/z values. In the context of automated analysis, the method thus provides an alternative to the traditional approach of bucketing the data followed by denoising and/or one-dimensional peak detection. The software implementation of the proposed algorithm is available at http://www.anchem.su.se/peakd as compiled code for Matlab.

Algorithms↗
PubMed2006

Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics.

For the analysis of the spectra of complex biofluids, preprocessing methods play a crucial role in rendering the subsequent data analyses more robust and accurate. Normalization is a preprocessing method, which accounts for different dilutions of samples by scaling the spectra to the same virtual overall concentration. In the field of 1H NMR metabonomics integral normalization, which scales spectra to the same total integral, is the de facto standard. In this work, it is shown that integral normalization is a suboptimal method for normalizing spectra from metabonomic studies. Especially strong metabonomic changes, evident as massive amounts of single metabolites in samples, significantly hamper the integral normalization resulting in incorrectly scaled spectra. The probabilistic quotient normalization is introduced in this work. This method is based on the calculation of a most probable dilution factor by looking at the distribution of the quotients of the amplitudes of a test spectrum by those of a reference spectrum. Simulated spectra, spectra of urine samples from a metabonomic study with cyclosporin-A as the active compound, and spectra of more than 4000 samples of control animals demonstrate that the probabilistic quotient normalization is by far more robust and more accurate than the widespread integral normalization and vector length normalization.

Nuclear Magnetic Resonance, Biomolecular↗
PubMed2006

Metabonomics and biomarker discovery: LC-MS metabolic profiling and constant neutral loss scanning combined with multivariate data analysis for mercapturic acid analysis.

In the field of metabonomics, 1H NMR and full scan mass spectrometry methods have usually been combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to detect patterns in biofluids that correspond to specific effects, usually a toxic site effect of a compound. Confounders together with great interindividual variation complicate such analysis in humans, and therefore, metabonomic data are almost restricted to animals. In our study, a constant neutral loss (CNL) scan on a linear ion trap demonstrated increased sensitivity and specificity compared to a full scan approach and was performed to detect mercapturic acids (MA), a class of effect markers. The method was applied to human volunteers administered 50 and 500 mg of acetaminophen (AAP), a model compound known to form MAs. Using a new algorithm to prepare the CNL data for chemometrics, discrimination of control and postdose samples could be performed using PCA and PLS-DA. The loadings plots clearly revealed AAP-MA as a marker, even at low-dose levels. Orthogonal signal correction (OSC) was carried out to investigate background information that is not due to exposure. Surprisingly, the OSC data provided a classification of male and female subjects showing the performance of the new approach.

Acetylcysteine↗
PubMed2006

Scaling and normalization effects in NMR spectroscopic metabonomic data sets.

Considerable confusion appears to exist in the metabonomics literature as to the real need for, and the role of, preprocessing the acquired spectroscopic data. A number of studies have presented various data manipulation approaches, some suggesting an optimum method. In metabonomics, data are usually presented as a table where each row relates to a given sample or analytical experiment and each column corresponds to a single measurement in that experiment, typically individual spectral peak intensities or metabolite concentrations. Here we suggest definitions for and discuss the operations usually termed normalization (a table row operation) and scaling (a table column operation) and demonstrate their need in 1H NMR spectroscopic data sets derived from urine. The problems associated with "binned" data (i.e., values integrated over discrete spectral regions) are also discussed, and the particular biological context problems of analytical data on urine are highlighted. It is shown that care must be exercised in calculation of correlation coefficients for data sets where normalization to a constant sum is used. Analogous considerations will be needed for other biofluids, other analytical approaches (e.g., HPLC-MS), and indeed for other "omics" techniques (i.e., transcriptomics or proteomics) and for integrated studies with "fused" data sets. It is concluded that data preprocessing is context dependent and there can be no single method for general use.

Algorithms↗
PubMed2006

Statistical search space reduction and two-dimensional data display approaches for UPLC-MS in biomarker discovery and pathway analysis.

A new analytical strategy for biomarker recovery from directly coupled ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC Tof MS) data on biofluids is presented and exemplified using a study on hydrazine-induced liver toxicity. A key step in the strategy involves a novel procedure for reducing the spectroscopic search space by differential analysis of cohorts of normal and pathological samples using an orthogonal projection to latent structures discriminant analysis (O-PLS-DA). This efficiently sorts principal discriminators of toxicity from the background of thousands of metabolic features commonly observed in data sets generated by UPLC-MS analysis of biological fluids and is thus a powerful tool for biomarker discovery.

Animals↗
PubMed2006

Targeted profiling: quantitative analysis of 1H NMR metabolomics data.

Extracting meaningful information from complex spectroscopic data of metabolite mixtures is an area of active research in the emerging field of "metabolomics", which combines metabolism, spectroscopy, and multivariate statistical analysis (pattern recognition) methods. Chemometric analysis and comparison of 1H NMR1 spectra is commonly hampered by intersample peak position and line width variation due to matrix effects (pH, ionic strength, etc.). Here a novel method for mixture analysis is presented, defined as "targeted profiling". Individual NMR resonances of interest are mathematically modeled from pure compound spectra. This database is then interrogated to identify and quantify metabolites in complex spectra of mixtures, such as biofluids. The technique is validated against a traditional "spectral binning" analysis on the basis of sensitivity to water suppression (presaturation, NOESY-presaturation, WET, and CPMG), relaxation effects, and NMR spectral acquisition times (3, 4, 5, and 6 s/scan) using PCA pattern recognition analysis. In addition, a quantitative validation is performed against various metabolites at physiological concentrations (9 microM-8 mM). "Targeted profiling" is highly stable in PCA-based pattern recognition, insensitive to water suppression, relaxation times (within the ranges examined), and scaling factors; hence, direct comparison of data acquired under varying conditions is made possible. In particular, analysis of metabolites at low concentration and overlapping regions are well suited to this analysis. We discuss how targeted profiling can be applied for mixture analysis and examine the effect of various acquisition parameters on the accuracy of quantification.

Metabolism↗