Analytical and Bioanalytical Chemistry

, Volume 401, Issue 3, pp 939–955 | Cite as

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

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

Abstract

Chemometric analysis of a set of one-dimensional (1D) 1H 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 1H 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 1H 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.

Figure

Contour plot from grid search of the optimal values of γ and C for the SVM model

Keywords

Heparin Proton nuclear magnetic resonance (1H NMR) Pattern recognition Classification and regression tree (CART) Artificial neural network (ANN) Support vector machine (SVM) 

References

  1. 1.
    Linhardt RJ (1991) Heparin: an important drug enters its seventh decade. Chem Ind 2:45–50Google Scholar
  2. 2.
    Lepor NE (2007) Anticoagulation for acute coronary syndromes: from heparin to direct thrombin inhibitors. Rev Cardiovasc Med 8(suppl 3):S9–S17Google Scholar
  3. 3.
    Casu B (1990) Heparin structure. Haemostasis 20:62–73Google Scholar
  4. 4.
    Ampofo SA, Wang HM, Linhardt RJ (1991) Disaccharide compositional analysis of heparin and heparan sulfate using capillary zone electrophoresis. Anal Biochem 199:249–255CrossRefGoogle Scholar
  5. 5.
    Rabenstein DL (2002) Heparin and heparan sulfate: structure and function. Nat Prod Rep 19:312–331CrossRefGoogle Scholar
  6. 6.
    Toida T, Maruyama T, Ogita Y, Suzuki A, Toyoda H, Imanari T, Linhardt RJ (1999) Preparation and anticoagulant activity of fully O-sulphonated glycosaminoglycans. Int J Biol Macromol 26:233–241CrossRefGoogle Scholar
  7. 7.
    Pervin A, Gallo C, Jandik KA, Han XJ, Linhardt RJ (1995) Preparation and structural characterization of large heparin-derived oligosaccharides. Glycobiology 5:83–95CrossRefGoogle Scholar
  8. 8.
    Griffin CC, Linhardt RJ, Van Gorp CL, Toida T, Hileman RE, Schubert RL, Brown SE (1995) Isolation and characterization of heparan sulfate from crude porcine intestinal mucosal peptidoglycan heparin. Carbohydr Res 276:183–197CrossRefGoogle Scholar
  9. 9.
    Beni S, Limtiaco JFK, Larive CK (2011) Analysis and characterization of heparin impurities. Anal Bioanal Chem 399:527–539CrossRefGoogle Scholar
  10. 10.
    Beyer T, Diehl B, Randel G, Humpfer E, Schäfer H, Spraul M, Schollmayer C, Holzgrabe U (2008) Quality assessment of unfractionated heparin using 1H nuclear magnetic resonance spectroscopy. J Pharm Biomed Anal 48:13–19CrossRefGoogle Scholar
  11. 11.
    McMahon AW, Pratt RG, Hammad TA, Kozlowski S, Zhou E, Lu S, Kulick CG, Mallick T, Dal Pan G (2010) Description of hypersensitivity adverse events following administration of heparin that was potentially contaminated with oversulfated chondroitin sulfate in early 2008. Pharmacoepidemiol Drug Saf 19:921–933CrossRefGoogle Scholar
  12. 12.
    Kishimoto TK, Viswanathan K, Ganguly T, Elankumaran S, Smith S, Pelzer K, Lansing JC, Sriranganathan N, Zhao G, Galcheva-Gargova Z, Al-Hakim A, Bailey GS, Fraser B, Roy S, Rogers-Cotrone T, Buhse L, Whary M, Fox J, Nasr M, Dal Pan GJ, Shriver Z, Langer RS, Venkataraman G, Austen KF, Woodcock J, Sasisekharan R (2008) Contaminated heparin associated with adverse clinical events and activation of the contact system. N Engl J Med 358:2457–2467CrossRefGoogle Scholar
  13. 13.
    Trehy ML, Reepmeyer JC, Kolinski RE, Westenberger BJ, Buhse LF (2009) Analysis of heparin sodium by SAX/HPLC for contaminants and impurities. J Pharm Biomed Anal 49:670–673CrossRefGoogle Scholar
  14. 14.
    Keire DA, Trehy ML, Reepmeyer JC, Kolinski RE, Dunn J, Ye W, Westenberger BJ, Buhse LF (2010) Analysis of crude heparin by 1H NMR, capillary electrophoresis, and strong-anion-exchange-HPLC for contamination by over sulfated chondroitin sulfate. J Pharm Biomed Anal 51:921–926CrossRefGoogle Scholar
  15. 15.
    Keire DA, Mans DJ, Ye H, Kolinski RE, Buhse LF (2010) Assay of possible economically motivated additives or native impurities levels in heparin by 1H NMR, SAX-HPLC, and anticoagulation time approaches. J Pharm Biomed Anal 52:656–664CrossRefGoogle Scholar
  16. 16.
    Keire DA, Ye H, Trehy ML, Ye W, Kolinski RE, Westenberger BJ, Buhse LF, Nasr M, Al-Hakim A (2011) Characterization of currently marketed heparin products: key tests for quality assurance. Anal Bioanal Chem 399:581–591CrossRefGoogle Scholar
  17. 17.
    Brustkern AM, Buhse LF, Nasr M, Al-Hakim A, Keire DA (2010) Characterization of currently marketed heparin products: reversed-phase ion-pairing liquid chromatography mass spectrometry of heparin digests. Anal Chem 82:9865–9870CrossRefGoogle Scholar
  18. 18.
    Guerrini M, Beccati D, Shriver Z, Naggi A, Viswanathan K, Bisio A, Capila I, Lansing JC, Guglieri S, Fraser B, Al-Hakim A, Gunay NS, Zhang Z, Robinson L, Buhse L, Nasr M, Woodcock J, Langer R, Venkataraman G, Linhardt RJ, Casu B, Torri G, Sasisekharan R (2008) Oversulfated chondroitin sulfate is a contaminant in heparin associated with adverse clinical events. Nat Biotechnol 26:669–675CrossRefGoogle Scholar
  19. 19.
    Guerrini M, Zhang Z, Shriver Z, Naggi A, Masuko S, Langer R, Casu B, Linhardt RJ, Torri G, Sasisekharan R (2009) Orthogonal analytical approaches to detect potential contaminants in heparin. PNAS 106:16956–16961CrossRefGoogle Scholar
  20. 20.
    Zhang Z, Li B, Suwan J, Zhang F, Wang Z, Liu H, Mulloy B, Linhardt RJ (2009) Analysis of pharmaceutical heparins and potential contaminants using 1H-NMR and PAGE. J Pharm Sci 98:4017–4026CrossRefGoogle Scholar
  21. 21.
    Bigler P, Brenneisen R (2009) Improved impurity fingerprinting of heparin by high resolution 1H NMR spectroscopy. J Pharm Biomed Anal 49:1060–1064CrossRefGoogle Scholar
  22. 22.
    Ruiz-Calero V, Saurina J, Galceran MT, Hernández-Cassou S, Puignou L (2002) Estimation of the composition of heparin mixtures from various origins using proton nuclear magnetic resonance and multivariate calibration methods. Anal Bioanal Chem 373:259–265CrossRefGoogle Scholar
  23. 23.
    Rudd TR, Gaudesi D, Lima MA, Skidmore MA, Mulloy B, Torri G, Nader HB, Guerrini M, Yates EA (2011) High-sensitivity visualisation of contaminants in heparin samples by spectral filtering of 1H NMR spectra. Analyst 136:1390–1398CrossRefGoogle Scholar
  24. 24.
    Rudd TR, Gaudesi D, Skidmore MA, Ferro M, Guerrini M, Mulloy B, Torri G, Yates EA (2011) Construction and use of a library of bona fide heparins employing 1H NMR and multivariate analysis. Analyst 136:1380–1389CrossRefGoogle Scholar
  25. 25.
    Alban S, Lühn S, Schiemann S, Beyer T, Norwig J, Schilling C, Rädler O, Wolf B, Matz M, Baumann K, Holzgrabe U (2011) Comparison of established and novel purity tests for the quality control of heparin by means of a set of 177 heparin samples. Anal Bioanal Chem 399:605–620CrossRefGoogle Scholar
  26. 26.
    Zang Q, Keire DA, Wood RD, Buhse LF, Moore CMV, Nasr M, Al-Hakim A, Trehy ML, Welsh WJ (2011) Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their 1H NMR spectra. Anal Bioanal Chem 399:635–649CrossRefGoogle Scholar
  27. 27.
    Zang Q, Keire DA, Wood RD, Buhse LF, Moore CMV, Nasr M, Al-Hakim A, Trehy ML, Welsh WJ (2011) Combining 1H NMR spectroscopy and chemometrics to identify heparin samples that may possess dermatan sulfate (DS) impurities or oversulfated chondroitin sulfate (OSCS) contaminants. J Pharm Biomed Anal 54:1020–1029CrossRefGoogle Scholar
  28. 28.
    Zang Q, Keire DA, Wood RD, Buhse LF, Moore CMV, Nasr M, Al-Hakim A, Trehy ML, Welsh WJ (2011) Class modeling analysis of heparin 1H NMR spectral data using the soft independent modeling of class analogy and unequal class modeling techniques. Anal Chem 83:1030–1039CrossRefGoogle Scholar
  29. 29.
    Berrueta LA, Alonso-Salces RM, Héberger K (2007) Supervised pattern recognition in food analysis. J Chromatogr A 1158:196–214CrossRefGoogle Scholar
  30. 30.
    Welsh WJ, Lin W, Tersigni SH, Collantes E, Duta R, Carey MS, Zielinski WL, Brower J, Spencer JA, Layloff TP (1996) Pharmaceutical fingerprinting: evaluation of neural networks and chemometric techniques for distinguishing among same-product manufacturers. Anal Chem 68:3473–3482CrossRefGoogle Scholar
  31. 31.
    Zielinski WL, Brower JF, Welsh WJ, Collantes E, Layloff TP (1998) A strategy for developing consistent HPLC data for assessing sameness and difference in consistency of pharmaceutical products. American Pharm Rev 1:44–54Google Scholar
  32. 32.
    Rudd TR, Skidmore MA, Guimond SE, Cosentino C, Torri G, Fernig DG, Lauder RM, Guerrini M, Yates EA (2009) Glycosaminoglycan origin and structure revealed by multivariate analysis of NMR and CD spectra. Glycobiology 19:52–67CrossRefGoogle Scholar
  33. 33.
    Lima MA, Rudd TR, de Farias EHC, Ebner LF, Gesteira TF, de Souza LM, Mendes A, Córdula CR, Martins JRM, Hoppensteadt D, Fareed J, Sassaki GL, Yates EA, Tersariol ILS, Nader HB (2011) A new approach for heparin standardization: combination of scanning UV spectroscopy, nuclear magnetic resonance and principal component analysis. PLoS One 6:e15970CrossRefGoogle Scholar
  34. 34.
    Varmuza K, Filzmoser P (2009) Introduction to multivariate statistical analysis in chemometrics. CRC Press, Boca RatonCrossRefGoogle Scholar
  35. 35.
    U.S. Pharmacopeia (2009) Pharmacopeial forum, March–April. p 257Google Scholar
  36. 36.
    R Development Core Team (2005) R: software, a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from: www.r-project.org
  37. 37.
    Wehrens R (2011) Chemometrics with R: multivariate data analysis in the natural sciences and life sciences. Springer, BerlinGoogle Scholar
  38. 38.
    Caetano S, Aires-de-Sousa J, Daszykowski M, Vander Heyden Y (2005) Prediction of enantioselectivity using chirality codes and classification and regression trees. Anal Chim Acta 544:315–326CrossRefGoogle Scholar
  39. 39.
    Questier F, Put R, Coomans D, Walczak B, Vander Heyden Y (2005) The use of CART and multivariate regression trees for supervised and unsupervised feature selection. Chemom Intell Lab Syst 76:45–54CrossRefGoogle Scholar
  40. 40.
    Deconinck E, Hancock T, Coomans D, Massart DL, Vander Heyden Y (2005) Classification of drugs in absorption classes using the classification and regression trees (CART) methodology. J Pharm Biomed Anal 39:91–103CrossRefGoogle Scholar
  41. 41.
    Marini F (2009) Artificial neural networks in foodstuff analyses: trends and perspectives, a review. Anal Chim Acta 635:121–131CrossRefGoogle Scholar
  42. 42.
    Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22:717–727CrossRefGoogle Scholar
  43. 43.
    Tetko IV, Villa AEP, Aksenova TI, Zielinski WL, Brower J, Collantes ER, Welsh WJ (1998) Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting. J Chem Inf Comput Sci 38:660–668Google Scholar
  44. 44.
    Collantes ER, Duta R, Welsh WJ, Zielinski WL, Brower J (1997) Preprocessing of HPLC trace impurity patterns by wavelet packets for pharmaceutical fingerprinting using artificial neural networks. Anal Chem 69:1392–1397CrossRefGoogle Scholar
  45. 45.
    Belousov AI, Verzakov SA, von Frese J (2002) Applicational aspects of support vector machines. J Chemom 16:482–489CrossRefGoogle Scholar
  46. 46.
    Xu Y, Zomer S, Brereton RG (2006) Support vector machines: a recent method for classification in chemometrics. Crit Rev Anal Chem 36:177–188CrossRefGoogle Scholar
  47. 47.
    Li H, Liang Y, Xu Q (2009) Support vector machines and its applications in chemistry. Chemom Intell Lab Syst 95:188–198CrossRefGoogle Scholar
  48. 48.
    Devos O, Ruckebusch C, Durand A, Duponchel L, Huvenne JP (2009) Support vector machines (SVM) in near infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation. Chemom Intell Lab Syst 96:27–33CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Qingda Zang
    • 1
    • 2
    • 3
  • David A. Keire
    • 4
  • Lucinda F. Buhse
    • 4
  • Richard D. Wood
    • 2
  • Dinesh P. Mital
    • 3
  • Syed Haque
    • 3
  • Shankar Srinivasan
    • 3
  • 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 and Dentistry of New JerseyPiscatawayUSA
  2. 2.Snowdon, Inc.Monmouth JunctionUSA
  3. 3.Department of Health Informatics, School of Health Related ProfessionsUniversity of Medicine and Dentistry of New JerseyNewarkUSA
  4. 4.Division of Pharmaceutical AnalysisFood and Drug Administration, CDERSt. LouisUSA
  5. 5.Office of New Drug Quality AssessmentFood and Drug Administration, CDERSilver SpringUSA

Personalised recommendations