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.
Resumen
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...
Por qué esto importa para la hirudoterapia
Este estudio analítico empleó el análisis de reconocimiento de patrones quimiométrico combinado con espectroscopia de resonancia magnética nuclear (RMN) de protón para identificar impurezas y contaminantes, como el sulfato de condroitina sobresulfatado y el sulfato de dermatán, en muestras de heparina sódica. La investigación demostró con éxito que estos métodos estadísticos pueden ofrecer un enfoque no subjetivo para el control de calidad de la heparina. Este estudio se centra estrictamente en la evaluación de laboratorio y el aseguramiento de la calidad de productos farmacéuticos de heparina. En consecuencia, carece de relevancia para la American Society of Hirudotherapy, ya que el resumen no investiga sanguijuelas, hirudoterapia ni el secretoma de las sanguijuelas. La investigación no aporta datos sobre anticoagulantes derivados de sanguijuela ni análisis comparativos con hirudina.
Citación
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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Añadido a la biblioteca ASH: May 27, 2026 · Última actualización del sitio: 18 de junio de 2026