Chemometric Methods to Quantify 1D and 2D NMR Spectral Differences Among Similar Protein Therapeutics

  • Kang Chen
  • Junyong Park
  • Feng Li
  • Sharadrao M. Patil
  • David A. Keire
Research Article

Abstract

NMR spectroscopy is an emerging analytical tool for measuring complex drug product qualities, e.g., protein higher order structure (HOS) or heparin chemical composition. Most drug NMR spectra have been visually analyzed; however, NMR spectra are inherently quantitative and multivariate and thus suitable for chemometric analysis. Therefore, quantitative measurements derived from chemometric comparisons between spectra could be a key step in establishing acceptance criteria for a new generic drug or a new batch after manufacture change. To measure the capability of chemometric methods to differentiate comparator NMR spectra, we calculated inter-spectra difference metrics on 1D/2D spectra of two insulin drugs, Humulin R® and Novolin R®, from different manufacturers. Both insulin drugs have an identical drug substance but differ in formulation. Chemometric methods (i.e., principal component analysis (PCA), 3-way Tucker3 or graph invariant (GI)) were performed to calculate Mahalanobis distance (D M) between the two brands (inter-brand) and distance ratio (D R) among the different lots (intra-brand). The PCA on 1D inter-brand spectral comparison yielded a D M value of 213. In comparing 2D spectra, the Tucker3 analysis yielded the highest differentiability value (D M = 305) in the comparisons made followed by PCA (D M = 255) then the GI method (D M = 40). In conclusion, drug quality comparisons among different lots might benefit from PCA on 1D spectra for rapidly comparing many samples, while higher resolution but more time-consuming 2D-NMR-data-based comparisons using Tucker3 analysis or PCA provide a greater level of assurance for drug structural similarity evaluation between drug brands.

KEY WORDS

Mahalanobis distance distance ratio PCA Tucker3 graph invariant 

Notes

Acknowledgements

We thank the reviewer for pointing us to the Tucker3 method. We thank Prof. P.M. Kroonenberg for helpful discussion on Tucker3 application.

Compliance with Ethical Standards

Disclaimer

This article reflects the views of the author and should not be construed to represent U.S. FDA’s views or policies.

Supplementary material

12249_2017_911_MOESM1_ESM.docx (524 kb)
ESM 1 NMR data processing scripts, MATLAB scripts, R scripts and principle component scores can be found in supplementary materials. (DOCX 523 kb)

References

  1. 1.
    Zuperl S, Pristovsek P, Menart V, Gaberc-Porekar V, Novic M. Chemometric approach in quantification of structural identity/similarity of proteins in biopharmaceuticals. J Chem Inf Model. 2007;47(3):737–43.CrossRefPubMedGoogle Scholar
  2. 2.
    Poppe L, Jordan JB, Rogers G, Schnier PD. On the analytical superiority of 1D NMR for fingerprinting the higher order structure of protein therapeutics compared to multidimensional NMR methods. Anal Chem. 2015;87:5539–45.CrossRefPubMedGoogle Scholar
  3. 3.
    Poppe L, Jordan JB, Lawson K, Jerums M, Apostol I, Schnier PD. Profiling formulated monoclonal antibodies by H-1 NMR spectroscopy. Anal Chem. 2013;85(20):9623–9.CrossRefPubMedGoogle Scholar
  4. 4.
    Guerrini M, Rudd TR, Mauri L, Macchi E, Fareed J, Yates EA, et al. Differentiation of generic Enoxaparins marketed in the United States by employing NMR and multivariate analysis. Anal Chem. 2015;87(16):8275–83.CrossRefPubMedGoogle Scholar
  5. 5.
    Kozlowski S, Woodcock J, Midthun K, Sherman RB. Developing the nation's biosimilars program. New Engl J Med. 2011;365(5):385–8.CrossRefPubMedGoogle Scholar
  6. 6.
    Ghasriani H, Hodgson DJ, Brinson RG, McEwen I, Buhse LF, Kozlowski S, et al. Precision and robustness of 2D-NMR for structure assessment of filgrastim biosimilars. Nat Biotechnol. 2016;34(2):139–41.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Keire DA, Buhse LF, Al-Hakim A. Characterization of currently marketed heparin products: composition analysis by 2D-NMR. Anal Methods-Uk. 2013;5(12):2984–94.CrossRefGoogle Scholar
  8. 8.
    Ye HP, Toby TK, Sommers CD, Ghasriani H, Trehy ML, Ye W, et al. Characterization of currently marketed heparin products: key tests for LMWH quality assurance. J Pharmaceut Biomed. 2013;85:99–107.CrossRefGoogle Scholar
  9. 9.
    Rogstad S, Pang E, Sommers C, Hu M, Jiang XH, Keire DA, et al. Modern analytics for synthetically derived complex drug substances: NMR, AFFF-MALS, and MS tests for glatiramer acetate. Anal Bioanal Chem. 2015;407(29):8647–59.CrossRefPubMedGoogle Scholar
  10. 10.
    Levy MJ, Boyneii MT, Rogstad S, Skanchy DJ, Jiang XH, Geerlof-Vidavsky I. Marketplace analysis of conjugated estrogens: determining the consistently present steroidal content with LC-MS. AAPS J. 2015;17(6):1438–45.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Arzhantsev S, Vilker V, Kauffman J. Deep-ultraviolet (UV) resonance Raman spectroscopy as a tool for quality control of formulated therapeutic proteins. Appl Spectrosc. 2012;66(11):1262–8.CrossRefPubMedGoogle Scholar
  12. 12.
    Hmiel LK, Brorson KA, Boyne MT. Post-translational structural modifications of immunoglobulin G and their effect on biological activity. Anal Bioanal Chem. 2015;407(1):79–94.CrossRefPubMedGoogle Scholar
  13. 13.
    Korang-Yeboah M, Rahman Z, Shah D, Mohammad A, Wu SY, Siddiqui A, et al. Impact of formulation and process variables on solid-state stability of theophylline in controlled release formulations. Int J Pharm. 2016;499(1–2):20–8.CrossRefPubMedGoogle Scholar
  14. 14.
    Panjwani N, Hodgson DJ, Sauve S, Aubin Y. Assessment of the effects of pH, formulation and deformulation on the conformation of interferon alpha-2 by NMR. J Pharm Sci-Us. 2010;99(8):3334–42.CrossRefGoogle Scholar
  15. 15.
    Jin X, Kang S, Kwon H, Park S, Heteronuclear NMR. As a 4-in-1 analytical platform for detecting modification-specific signatures of therapeutic insulin formulations. Anal Chem. 2014;86(4):2050–6.CrossRefPubMedGoogle Scholar
  16. 16.
    Aubin Y, Jones C, Freedberg DI. Using NMR spectroscopy to obtain the higher order structure of biopharmaceutical products. Biopharm Int. 2010;Supplement:28–34.Google Scholar
  17. 17.
    Aubin Y, Hodgson DJ, Thach WB, Gingras G, Sauve S. Monitoring effects of excipients, formulation parameters and mutations on the high order structure of filgrastim by NMR. Pharm Res. 2015;32:3365–75.CrossRefPubMedGoogle Scholar
  18. 18.
    Aubin Y, Gingras G, Sauve S. Assessment of the three-dimensional structure of recombinant protein therapeutics by NMR fingerprinting: demonstration on recombinant human granulocyte macrophage-colony stimulation factor. Anal Chem. 2008;80(7):2623–7.CrossRefPubMedGoogle Scholar
  19. 19.
    Arbogast LW, Brinson RG, Marino JP. Mapping monoclonal antibody structure by 2D (13)C NMR at natural abundance. Anal Chem. 2015;87(7):3556–61.CrossRefPubMedGoogle Scholar
  20. 20.
    Amezcua CA, Szabo CM. Assessment of higher order structure comparability in therapeutic proteins using nuclear magnetic resonance spectroscopy. J Pharm Sci-Us. 2013;102(6):1724–33.CrossRefGoogle Scholar
  21. 21.
    Chen K, Long DS, Lute SC, Levy MJ, Brorson KA, Keire DA. Simple NMR methods for evaluating higher order structures of monoclonal antibody therapeutics with quinary structure. J Pharmaceut Biomed. 2016;128:398–407.CrossRefGoogle Scholar
  22. 22.
    Zang QD, Keire DA, Buhse LF, Wood RD, Mital DP, Haque S, et al. Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data. Anal Bioanal Chem. 2011;401(3):939–55.CrossRefPubMedGoogle Scholar
  23. 23.
    Kiers HAL. Towards a standardized notation and terminology in multiway analysis. J Chemom. 2000;14(3):105–22.CrossRefGoogle Scholar
  24. 24.
    Kroonenberg PM, Basford KE, Gemperline PJ. Grouping three-mode data with mixture methods: the case of the diseased blue crabs. J Chemom. 2004;18(11):508–18.CrossRefGoogle Scholar
  25. 25.
    Tucker LR. Some mathematical notes on 3-mode factor analysis. Psychometrika. 1966;31(3):279.CrossRefPubMedGoogle Scholar
  26. 26.
    Randic M, Novic M, Vracko M. Novel characterization of proteomics maps by sequential neighborhoods of protein spots. J Chem Inf Model. 2005;45(5):1205–13.CrossRefPubMedGoogle Scholar
  27. 27.
    Bro R. Review on multiway analysis in chemistry—2000-2005. Crit Rev Anal Chem. 2006;36(3–4):279–93.CrossRefGoogle Scholar
  28. 28.
    Rencher AC. Methods of multivariate analysis. 2nd ed. Hoboken: Wiley-Interscience; 2003.Google Scholar
  29. 29.
    Brereton RG. The Mahalanobis distance and its relationship to principal component scores. J Chemom. 2015;29(3):143–5.CrossRefGoogle Scholar
  30. 30.
    Wishart DS, Bigam CG, Yao J, Abildgaard F, Dyson HJ, Oldfield E, et al. H-1, C-13 and N-15 chemical-shift referencing in biomolecular NMR. J Biomol NMR. 1995;6(2):135–40.CrossRefPubMedGoogle Scholar
  31. 31.
    Chen K, Freedberg DI, Keire DA. NMR profiling of biomolecules at natural abundance using 2D H-1-N-15 and H-1-C-13 multiplicity-separated (MS) HSQC spectra. J Magn Reson. 2015;251:65–70.CrossRefPubMedGoogle Scholar
  32. 32.
    Delaglio F, Grzesiek S, Vuister GW, Zhu G, Pfeifer J, Bax A. Nmrpipe—a multidimensional spectral processing system based on Unix pipes. J Biomol NMR. 1995;6(3):277–93.CrossRefPubMedGoogle Scholar
  33. 33.
    Kiers HAL, Kroonenberg PM, Tenberge JMF. An efficient algorithm for Tuckals3 on data with large numbers of observation units. Psychometrika. 1992;57(3):415–22.CrossRefGoogle Scholar
  34. 34.
    Patil, S.M., Keire, D.A. & Chen, K. AAPS J. 2017.  https://doi.org/10.1208/s12248-017-0127-z.
  35. 35.
    Keller D, Clausen R, Josefsen K, Led JJ. Flexibility and bioactivity of insulin: an NMR investigation of the solution structure and folding of an unusually flexible human insulin mutant with increased biological activity. Biochemistry. 2001;40(35):10732–40.CrossRefPubMedGoogle Scholar
  36. 36.
    Chang XQ, Jorgensen AMM, Bardrum P, Led JJ. Solution structures of the R-6 human insulin hexamer. Biochemistry. 1997;36(31):9409–22.CrossRefPubMedGoogle Scholar
  37. 37.
    Dyrby M, Baunsgaard D, Bro R, Engelsen SB. Multiway chemometric analysis of the metabolic response to toxins monitored by NMR. Chemometr Intell Lab. 2005;76(1):79–89.CrossRefGoogle Scholar
  38. 38.
    Pedersen HT, Bro R, Engelsen SB. Towards rapid and unique curve resolution of low-field NMR relaxation data: trilinear SLICING versus two-dimensional curve fitting. J Magn Reson. 2002;157(1):141–55.CrossRefPubMedGoogle Scholar
  39. 39.
    Randic M. A graph theoretical characterization of proteomics maps. Int J Quantum Chem. 2002;90(2):848–58.CrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2017

Authors and Affiliations

  • Kang Chen
    • 1
  • Junyong Park
    • 2
  • Feng Li
    • 2
  • Sharadrao M. Patil
    • 1
  • David A. Keire
    • 3
  1. 1.Division of Pharmaceutical Analysis, Office of Testing and Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringUSA
  2. 2.Department of Mathematics and StatisticsUniversity of Maryland Baltimore CountyBaltimoreUSA
  3. 3.Division of Pharmaceutical Analysis, Office of Testing and Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and ResearchUS Food and Drug AdministrationSt. LouisUSA

Personalised recommendations