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The discovery of tyrosine kinase inhibitors (TKIs) brought a major breakthrough in the treatment of patients with chronic myeloid leukemia (CML). Pathogenetic CML events are closely linked with the Bcr-Abl protein with tyrosine kinase activity. TKIs block the ATP-binding site; therefore, the signal pathways leading to malignant transformation are no longer active. However, there is limited information about the impact of TKI treatment on the metabolome of CML patients. Using liquid chromatography mass spectrometric metabolite profiling and multivariate statistical methods, we analyzed plasma and leukocyte samples of patients newly diagnosed with CML, patients treated with hydroxyurea and TKIs (imatinib, dasatinib, nilotinib), and healthy controls. The global metabolic profiles clearly distinguished the newly diagnosed CML patients and the patients treated with hydroxyurea from those treated with TKIs and the healthy controls. The major changes were found in glycolysis, the citric acid cycle, and amino acid metabolism. We observed differences in the levels of amino acids and acylcarnitines between those patients responding to imatinib treatment and those who were resistant to it. According to our findings, the metabolic profiling may be potentially used as an additional tool for the assessment of response/resistance to imatinib.

A recently developed Thouless-expansion-based diagonalization-free approach for improving the efficiency of self-consistent field (SCF) methods (Noga and Šimunek, J. Chem. Theory Comput. 2010, 6, 2706) has been adapted to the four-component relativistic scheme and implemented within the program package ReSpect. In addition to the implementation, the method has been thoroughly analyzed, particularly with respect to cases for which it is difficult or computationally expensive to find a good initial guess. Based on this analysis, several modifications of the original algorithm, refining its stability and efficiency, are proposed. To demonstrate the robustness and efficiency of the improved algorithm, we present the results of four-component diagonalization-free SCF calculations on several heavy-metal complexes, the largest of which contains more than 80 atoms (about 6000 4-spinor basis functions). The diagonalization-free procedure is about twice as fast as the corresponding diagonalization.

IntroductionOne of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity.ObjectivesAnother way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.MethodsIn the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data.ResultsWe illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model.ConclusionFor urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.

Modern high resolution mass spectrometry offers unique identification capability in drug metabolism studies. In this work detailed imatinib metabolization in the plasma of patients with chronic myeloid leukemia is presented. The metabolites were separated by liquid chromatography on a C18 column with mass spectrometry detection via an Orbitrap Elite instrument (Thermo Scientific) based on exact mass measurement. A scan range of m/z 350–1200 resolution of 60,000 was applied (mass accuracy of 5 ppm). The data were evaluated using the advanced software for mass spectrometry Mass Frontier and MetWorks. In all plasma samples, studied 90 metabolites in the concentration range of 0.0001–1 μmol/L were identified by m/z values and confirmed by exact mass measurement of the MS2 and MS3 fragmentations. In order to achieve optimal clinical response and avoid toxicity, current therapeutic monitoring of parent drug is a useful tool for the individualization of treatment. Current high-resolution mass spectrometry possesses the potential to broaden this approach by monitoring number of potentially clinically relevant drug metabolites.