2D Raman spectroscopy as an alternative technique for distinguishing oleanoic acid and ursolic acid

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Development of a liquid chromatography-mass spectrometry method for the determination of ursolic acid in rat plasma and tissue: application to the pharmacokinetic and tissue distribution study

A fast and sensitive liquid chromatography-mass spectrometry method was developed for the determination of ursolic acid (UA) in rat plasma and tissues. Glycyrrhetinic acid was used as the internal standard (IS). Chromatographic separation was performed on a 3.5 μm Zorbax SB-C18 column (30 mm × 2.1 mm) with a mobile phase consisting of methanol and aqueous 10 mM ammonium acetate using gradient elution. Quantification was performed by selected ion monitoring with (m/z)(-) 455 for UA and (m/z)(-) 469 for the IS. The method was validated in the concentration range of 2.5 - 1470 ng mL(-1) for plasma samples and 20 - 11760 ng g(-1) for tissue homogenates. The intra- and inter-day assay of precision in plasma and tissues ranged from 1.6% to 7.1% and 3.7% to 9.0%, respectively, and the intra- and inter-day assay accuracy was 84.2 - 106.9% and 82.1 - 108.1%, respectively. Recoveries in plasma and tissues ranged from 83.2% to 106.2%. The limits of detections were 0.5 ng mL(-1) or 4.0 ng g(-1). The recoveries for all samples were >90%, except for liver, which indicated that ursolic acid may metabolize in liver. The main pharmacokinetic parameters obtained were T(max) = 0.42 ± 0.11 h, C(max) = 1.10 ± 0.31 μg mL(-1), AUC = 1.45 ± 0.21 μg h mL(-1) and K(a) = 5.64 ± 1.89 h(-1). The concentrations of UA in rat lung, spleen, liver, heart, and cerebellum were studied for the first time. This method is validated and could be applicable to the investigation of the pharmacokinetics and tissue distribution of UA in rats.

Rapid and novel discrimination and quantification of oleanolic and ursolic acids in complex plant extracts using two-dimensional nuclear magnetic resonance spectroscopy-Comparison with HPLC methods

A novel strategy for NMR analysis of mixtures of oleanolic and ursolic acids that occur in natural products is described. These important phytochemicals have similar structure and their discrimination and quantification is rather difficult. We report herein the combined use of proton-carbon heteronuclear single-quantum coherence ((1)H-(13)C HSQC) and proton-carbon heteronuclear multiple-bond correlation ((1)H-(13)C HMBC) NMR spectroscopy, in the identification and quantitation of oleanolic acid (OA) and ursolic acid (UA)in plant extracts of the Lamiaceae and Oleaceae family. The combination of (1)H-(13)C HSQC and (1)H-(13)C HMBC techniques allows the connection of the proton and carbon-13 spins across the molecular backbone resulting in the identification and, thus, discrimination of oleanolic and ursolic acid without resorting to physicochemical separation of the components. The quantitative results provided by 2D (1)H-(13)C HSQC NMR data were obtained within a short period of time ( approximately 14min) and are in excellent agreement with those obtained by HPLC, which support the efficiency of the suggested methodology.