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


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Article Info

DATE

2018-04

AUTHORS

Kang Chen, Junyong Park, Feng Li, Sharadrao M. Patil, David A. Keire

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. More... »

PAGES

1011-1019

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  • Identifiers

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    http://scigraph.springernature.com/pub.10.1208/s12249-017-0911-1

    DOI

    http://dx.doi.org/10.1208/s12249-017-0911-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092563013

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29110294


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