Analytical and Bioanalytical Chemistry

, Volume 399, Issue 2, pp 635–649 | Cite as

Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their 1H NMR spectra

  • Qingda Zang
  • David A. Keire
  • Richard D. Wood
  • Lucinda F. Buhse
  • Christine M. V. Moore
  • Moheb Nasr
  • Ali Al-Hakim
  • Michael L. Trehy
  • William J. Welsh
Original Paper

Abstract

Heparin, a widely used anticoagulant primarily extracted from animal sources, contains varying amounts of galactosamine impurities. Currently, the United States Pharmacopeia (USP) monograph for heparin purity specifies that the weight percent of galactosamine (%Gal) may not exceed 1%. In the present study, multivariate regression (MVR) analysis of 1H NMR spectral data obtained from heparin samples was employed to build quantitative models for the prediction of %Gal. MVR analysis was conducted using four separate methods: multiple linear regression, ridge regression, partial least squares regression, and support vector regression (SVR). Genetic algorithms and stepwise selection methods were applied for variable selection. In each case, two separate prediction models were constructed: a global model based on dataset A which contained the full range (0–10%) of galactosamine in the samples and a local model based on the subset dataset B for which the galactosamine level (0–2%) spanned the 1% USP limit. All four regression methods performed equally well for dataset A with low prediction errors under optimal conditions, whereas SVR was clearly superior among the four methods for dataset B. The results from this study show that 1H NMR spectroscopy, already a USP requirement for the screening of contaminants in heparin, may offer utility as a rapid method for quantitative determination of %Gal in heparin samples when used in conjunction with MVR approaches.

Keywords

Heparin Galactosamine impurities Proton nuclear magnetic resonance (1H NMR) Multivariate regression (MVR) Variable selection 

Notes

FDA disclaimer

The findings and conclusions in this article have not been formally disseminated by the Food and Drug Administration and should not be construed to represent any agency determination or policy.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Qingda Zang
    • 1
    • 2
    • 3
  • David A. Keire
    • 4
  • Richard D. Wood
    • 2
  • Lucinda F. Buhse
    • 4
  • Christine M. V. Moore
    • 5
  • Moheb Nasr
    • 5
  • Ali Al-Hakim
    • 5
  • Michael L. Trehy
    • 4
  • William J. Welsh
    • 1
  1. 1.Department of Pharmacology, Robert Wood Johnson Medical SchoolUniversity of Medicine & Dentistry of New JerseyPiscatawayUSA
  2. 2.Snowdon, Inc.Monmouth JunctionUSA
  3. 3.Department of Health Informatics, School of Health Related ProfessionsUniversity of Medicine & Dentistry of New JerseyNewarkUSA
  4. 4.Division of Pharmaceutical Analysis, Food and Drug Administration, CDERSt LouisUSA
  5. 5.Office of New Drug Quality Assessment, Food and Drug Administration, CDERSilver SpringUSA

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