AAPS PharmSciTech

, Volume 19, Issue 3, pp 1011–1019 | Cite as

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


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.


Mahalanobis distance distance ratio PCA Tucker3 graph invariant 



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

Funding Information

Support for this work from the US FDA CDER Critical Path Award is gratefully acknowledged.

Compliance with Ethical Standards


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)


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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

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