Discrimination of acceptable and contaminated heparin by chemometric analysis of proton nuclear magnetic resonance spectral data

Heparin is a highly effective anticoagulant that can contain varying amounts of undesirable galactosamine impurities (mostly dermatan sulfate or DS), the level of which indicates the purity of the drug substance. Currently, the United States Pharmacopeia (USP) monograph for heparin purity dictates that the weight percent of galactosamine in total hexosamine (%Gal) may not exceed 1%. In 2007 and 2008, heparin contaminated with oversulfated chondroitin sulfate (OSCS) was associated with adverse clinical effects, i.e., a rapid and acute onset of a potentially fatal anaphylactoid-type reaction. In order to develop efficient and reliable screening methods for detecting and identifying contaminants in existing and future lots of heparin to ensure the integrity of the global supply, chemometric techniques for heparin proton nuclear magnetic resonance (1H NMR) spectral data were applied to establish adequate multivariate statistical models for discrimination between pure heparin samples and those deemed unacceptable based on their levels of DS and/or OSCS.;The whole research work consisted of two parts: (1) the development of quantitative regression models to predict the %Gal in various heparin samples from NMR spectral data. Multivariate analyses including multiple linear regression (MLR), Ridge regression (RR), partial least squares regression (PLSR), and support vector regression (SVR) were employed in this investigation. To obtain stable and robust models with high predictive ability, variables were selected by genetic algorithms (GA) and stepwise methods; (2) differentiation of heparin samples from impurities and contaminants by the different pattern recognition and classification approaches, such as principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), k-nearest- neighbor (kNN), classification and regression tree (CART), artificial neural networks (ANN) and support vector machine (SVM), as well as the class modeling techniques soft-independent modeling of class analogy (SIMCA) and unequal dispersed classes (UNEQ).;Overall, the results from this study demonstrate that NMR spectroscopy coupled with multivariate chemometric techniques shows promise as a valuable tool for evaluating the quality of heparin sodium active pharmaceutical ingredients (APIs). These developed models may be useful in monitoring purity of other complex pharmaceutical products from high information content data.