Our Technology

Computational Tools for Assessing Product Quality and Safety
Snowdon employs its expertise in statistics, chemometrics, and pattern recognition to assist partner organizations to solve problems in product quality and safety. This is accomplished by transforming complex analytical data obtained from chromatographic and spectroscopic experiments on food, pharmaceutical, and other consumer products into simple computational models for data visualization, classification, and prediction. These models have become indispensable tools for issues in product quality assurance. Examples of recent projects are cited below.

Heparin Contamination   Snowdon recently completed a study for the FDA to develop chemometric tools to distinguish authentic from impure and contaminated heparin samples. The impetus behind the study arose in March 2008, when oversulfated chondroitin sulfate (OSCS) was identified as a contaminant in heparin that caused nearly 100 deaths and widespread illness in the US. In response to this public health crisis, the FDA developed NMR methods so that manufacturers could screen their active pharmaceutical ingredients (APIs) for the presence of OSCS contaminant. This task was complicated in that heparin is a heterogeneous mixture of straight-chain anionic polysaccharides called glycosaminoglycans which can mask potential contaminants; therefore, the FDA contracted Snowdon to develop chemometric tools that consider heparin’s inherent variability. Snowdon based its strategy on the concept of “pharmaceutical fingerprinting” that proved successful in earlier studies with the FDA. Processing of NMR and other analytical data provided by the FDA enabled Snowdon to generate a suite of chemometric models to identify and quantify the content of natural impurities (e.g., dermatan sulfate, or DS) and contaminants (e.g., oversulfated chondroitin sulfate, or OSCS) in heparin obtained from various commercial manufacturers. The predictive performance of the resulting models approached 100% success in distinguishing acceptable heparin from unacceptable impure and contaminated heparin.

Implementing Quality by Design (QbD) Principles   Snowdon is currently engaged in a project to implement principles of Quality by Design (QbD) and Process Analytical Technology (PAT) espoused by the FDA to design, monitor, and control pharmaceutical manufacturing processes. This is achieved by developing adaptive in-line feedback control systems for real-time regulation of the pharmaceutical manufacturing process. The central goal of QbD is to produce pharmaceuticals that are free of contamination and that reproducibly deliver the intended therapeutic benefit. Using near infrared spectroscopy (NIR-S) and near infrared chemical imaging (NIR-CI) data provided by our partner organization to explore the surface of drug tablets, Snowdon is using multivariate chemometric methods to explore both qualitative and quantitative relationships between the NIR spectral output and the pre-defined Critical Quality Attributes (CQAs) of the drug tablets. The ultimate goal is to deploy these methods and models for rapid and early detection of impurities and contaminants in these pharmaceutical products.

In general, chemometrics has become an essential component in modern chemical and biomedical industries. Chemometrics software has been widely used by product development scientists, process engineers, PAT specialists, and QA/QC scientists to build reliable models, ensure product quality, classify raw materials, and to monitor process end points in real-time. Snowdon provides an assortment of customized tools to satisfy the needs for our customer, whether large or small. A sampling of our suite of chemometric tools for data exploration and analysis is provided below.

• Principal Component Analysis (PCA)
• Hierarchical Cluster Analysis
• Data Preprocessing (mean centering, autoscale, variance scale)
• Regression (PLS, PCR, MLR, 3-way PLS) and Prediction
• SIMCA and PLS-DA Classification
• Design of Experiments
• ANOVA and Response Surface Methodology
• Clustering (K-Means)
• Genetic Algorithms (GAs)

Relevant Publications

Welsh W.J., Lin W., Tersigni S.H., Collantes E., Duta R., Carey M.S., Zielinski W.L., Brower J., Spencer J.A. and Layloff T.P. (1996). Pharmaceutical fingerprinting: evaluation of neural networks and chemometric techniques for distinguishing among same-product manufacturers. Anal Chem 68 (19): 3473-82. DOI: 10.1021/ac951164e

Collantes E.R., Duta R., Welsh W.J., Zielinski W.L. and Brower J. (1997). Preprocessing of HPLC trace impurity patterns by wavelet packets for pharmaceutical fingerprinting using artificial neural networks. Anal Chem 69 (7): 1392-7. DOI: 10.1021/ac9608836

Tetko I.V., Villa A.E., Aksenova T.I., Zielinski W.L., Brower J., Collantes E.R. and Welsh W.J. (1998). Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting. J Chem Inf Comput Sci 38 (4): 660-8. DOI: 10.1021/ci970439j

Zielinski W.L., Brower J.F., Welsh W.J., Collantes E. and Layloff T.P. (1998). A strategy for developing consistent HPLC data for assessing sameness and difference in consistency of pharmaceutical products. American Pharmaceutical Reviews 1: 44-54.

Aksenova T.I., Tetko I.V., Ivakhnenko A.G., Villa A.E., Welsh W.J. and Zielinski W.L. (1999). Pharmaceutical fingerprinting in phase space. 1. Construction of phase fingerprints. Anal Chem 71 (13): 2423-30. DOI: 10.1021/ac981345r

Tetko I.V., Aksenova T.I., Patiokha A.A., Villa A.E., Welsh W.J., Zielinski W.L. and Livingstone D.J. (1999). Pharmaceutical fingerprinting in phase space. 2. Pattern recognition. Anal Chem 71 (13): 2431-9. DOI: 10.1021/ac981346j

Yiyu C., Minjun C. and Welsh W.J. (2003). Fractal fingerprinting of chromatographic profiles based on wavelet analysis and its application to characterize the quality grade of medicinal herbs. J Chem Inf Comput Sci 43 (6): 1959-65. DOI: 10.1021/ci034090d

USFDA (2004). Guidance for Industry: PAT - A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance. Rockville, MD. U.S. Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Veterinary Medicine (CVM), Office of Regulatory Affairs (ORA) [read the report]

Zang Q., Keire D.A., Wood R.D., Buhse L.F., Moore C.M., Nasr M., Al-Hakim A., Trehy M.L. and Welsh W.J. (2010). Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their (1)H NMR spectra. Anal Bioanal Chem. DOI: 10.1007/s00216-010-4268-5

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