Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data
Research article published in Analytical and bioanalytical chemistry (2011)
Abstract
Chemometric analysis of a set of one-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and/or unacceptable levels of dermatan sulfate (DS) impurity. Three chemometric pattern recognition approaches were implemented: classification and regression tree (CART), artificial neural network (ANN), and support vector machine (SVM). Heparin sodium samples from various manufacturers were analyzed in 2008 and 2009 by 1D (1)H NMR, strong anion-exchange high-performance liquid chromatography, and percent galactosamine in total hexosamine tests. Based on these data, the samples were divided into three groups: Heparin, DS ≤ 1.0% and OSCS = 0%; DS, DS > 1.0% and OSCS = 0%; and OSCS, OSCS > 0% with any content of DS. Three data sets corresponding to different chemical shift regions (1.95-2.20, 3.10-5.70, and 1.95-5.70 ppm) were evaluated. While all three chemometric approaches were able to effectively model the data in the 1.95-2.20 ppm region, SVM was found to substantially outperform CART and ANN for data in the 3.10-5.70 ppm region in terms of classification success rate. A 100% prediction rate was frequently achieved for discrimination between heparin and OSCS samples. The majority of classification errors between heparin and DS involved cases where the DS content was close to the 1.0% DS borderline between the two classes. When these borderline samples were removed, nearly perfect classification results were attained. Satisfactory results were achieved when the resulting models were challenged by test samples containing blends of heparin APIs spiked with non-, partially, or fully oversulfated chondroitin sulfate A, heparan sulfate, or DS at the 1.0%, 5.0%, and 10.0% (w/w) levels. This study demonstrated that the combination of 1D (1)H NMR spectroscopy with multivariate chemometric methods is a nonsubjective, statistics-based approach for heparin quality control and purity assessment that, once standardized, minimizes the need for expert analysts.
Abstract sourced from PubMed (NCBI) for the cited record. See the original publication for the authoritative version.
Summary
Chemometric analysis of a set of one-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectral data for heparin sodium active pharmaceutical ingredient (API) samples was employed to distinguish USP-grade heparin samples from those containing oversulfated chondroitin sulfate (OSCS) contaminant and/or unacceptab...
Why This Matters for Hirudotherapy
This analytical study utilized chemometric pattern recognition analysis combined with proton nuclear magnetic resonance (NMR) spectroscopy to identify impurities and contaminants, such as oversulfated chondroitin sulfate and dermatan sulfate, in heparin sodium samples. The research successfully demonstrated that these statistical methods can provide a non-subjective approach for heparin quality control. This study is strictly focused on the laboratory assessment and quality assurance of pharmaceutical heparin products. Consequently, it has no relevance to the American Society of Hirudotherapy, as the abstract does not investigate leeches, hirudotherapy, or the leech secretome. The research provides no data on leech-derived anticoagulants or any comparative analysis with hirudin.
Citation
Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data
Zang Q et al. · Analytical and bioanalytical chemistry, 2011
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