Extended Physicochemical Characterization of the Synthetic Anticoagulant Pentasaccharide Fondaparinux Sodium by Quantitative NMR and Single Crystal X-ray Analysis.

Fondaparinux sodium is a synthetic pentasaccharide representing the high affinity antithrombin III binding site in heparin. It is the active pharmaceutical ingredient of the anticoagulant drug Arixtra®. The single crystal X-ray structure of Fondaparinux sodium is reported, unequivocally confirming both structure and absolute configuration. The iduronic acid adopts a somewhat distorted chair conformation. Due to the presence of many sulfur atoms in the highly sulfated pentasaccharide, anomalous dispersion could be applied to determine the absolute configuration. A comparison with the conformation of Fondaparinux in solution, as well as complexed with proteins is presented. The content of the solution reference standard was determined by quantitative NMR using an internal standard both in 1999 and in 2016. A comparison of the results allows the conclusion that this method shows remarkable precision over time, instrumentation and analysts.

Visualization and recovery of the (bio)chemical interesting variables in data analysis with support vector machine classification.

Support vector machines (SVMs) have become a popular technique in the chemometrics and bioinformatics field, and other fields, for the classification of complex data sets. Especially because SVMs are able to model nonlinear relationships, the usage of this technique has increased substantially. This modeling is obtained by mapping the data in a higher-dimensional feature space. The disadvantage of such a transformation is, however, that information about the contribution of the original variables in the classification is lost. In this paper we introduce an innovative method which can retrieve the information about the variables of complex data sets. We apply the proposed method to several benchmark data sets and a metabolomics data set to illustrate that we can determine the contribution of the original variables in SVM classifications. The corresponding visualization of the contribution of the variables can assist in a better understanding of the underlying chemical or biological process.

Multivariate calibration with least-squares support vector machines.

This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and chemometrics. The advantages of SVM-based methods over many other methods are that these lead to global models that are often unique, and nonlinear regression can be performed easily as an extension to linear regression. An additional advantage of LS-SVM (compared to SVM) is that model calculation and optimization can be performed relatively fast. As a test case to study the use of LS-SVM, the well-known and important chemical problem is considered in which spectra are affected by nonlinear interferences. As one specific example, a commonly used case is studied in which near-infrared spectra are affected by temperature-induced spectral variation. Using this test case, model optimization, pruning, and model interpretation of the LS-SVM have been demonstrated. Furthermore, excellent performance of the LS-SVM, compared to other approaches, has been presented on the specific example. Therefore, it can be concluded that LS-SVMs can be seen as very promising techniques to solve ill-posed problems. Furthermore, these have been shown to lead to robust models in cases of spectral variations due to nonlinear interferences.