Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data.

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.

Advances in biomedical informatics for the management of cancer.

Increased access to health care, and advances in education and technology have resulted in a larger proportion of the population having longer life expectancy. The strong correlation between age and cancer has resulted in a major healthcare problem for this century, and until recently cancer has defied any long-lasting cure. However, progress, especially in the field of biomedical informatics, promises a successful prediction and possibly a permanent cure for cancer within the next two decades. Biomedical informatics-with its roots in computer science, biomedical engineering, biostatistics, and mathematics-helps to bring the patient closer to the physician, facilitates access to specialist information and knowledge bases across the world, and makes it possible to identify genetic expression profiles for malignant or cancerous cells. This paper reviews the new research findings in biomedical informatics, working toward the ultimate goal of successfully predicting cancer, solving complex problems in prevention and treatment of cancer, and perhaps completely curing the scourge of cancer.

Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development.

INTRODUCTION: Approaches aiming to model the time course of tumor growth and tumor growth inhibition following a therapeutic intervention have recently been proposed for supporting decision making in oncology drug development. When considered in a comprehensive model-based approach, tumor growth can be included in the cascade of quantitative and causally related markers that lead to the prediction of survival, the final clinical response. AREAS COVERED: The authors examine articles dealing with the modeling of tumor growth and tumor growth inhibition in both preclinical and clinical settings. In addition, the authors review models describing how pharmacological markers can be used to predict tumor growth and models describing how tumor growth can be linked to survival endpoints. EXPERT OPINION: Approaches and success stories of application of model-based drug development centered on tumor growth modeling are growing. It is also apparent that these approaches can answer practical questions on drug development more effectively than that in the past. For modeling purposes, some improvements are still needed related to study design and data quality. Further efforts are needed to encourage the mind shift from a simple description of data to the prediction of untested conditions that modeling approaches allow.

An online decision support system for diagnosing hematopoietic malignancies by flow cytometry immunophenotyping.

An online decision support system for hematopoietic neoplasm based on virtual flow has been developed. Rules were implemented via eXtensible Markup Language (XML). 153 cases representing 28 different hematopoietic neoplasms were correctly classified. Further testing for unknown cases is undergoing.