OCT 24, 2018 10:30 AM PDT

Chemometric Methods for Analysis of 2D 1H-13C Methyl NMR Spectra of Monoclonal Antibodies for Higher Order Structure Characterization

Speaker
  • Research Chemist, Macromolecular Structure and Function Group, National Institute of Standards and Technology (NIST)
    Biography
      Luke W. Arbogast is a Research Chemist in the Biomolecular Measurement Division at the National Institute of Standards and Technology (NIST). His current research focuses on the development and application of state-of-the-art two-dimensional NMR methods to characterize the structure of protein therapeutics. These methods are of particular interest to the biopharmaceutical industry where structural comparability studies are necessary for drug development, drug-quality testing and the benchmarking biosimilars against innovator reference products. In 2016, Luke received the Department of Commerce Silver Award for his work in this area. Prior to his current role at NIST, Luke was a National Research Council Postdoctoral Fellow at the Institute for Bioscience and Biotechnology Research in Rockville, Md. Luke received his B.S. in Chemistry from Virginia Commonwealth University and his Ph.D. in Chemistry from Johns Hopkins University.

    Abstract

    Two-dimensional (2D) 1H-13C methyl correlated NMR is increasingly being recognized as a powerful tool to characterize the higher order structure (HOS) of monoclonal antibody (mAb) therapeutics. 2D methyl NMR is well suited to the task as spectra can be readily be acquired on intact mAbs at natural isotopic abundance, and even small changes to chemical environment and structure manifest observable changes in the spectra, which can be interpreted at atomic resolution. This makes it possible to apply 2D NMR approaches directly to drug products in order to systematically characterize structural effects. Traditionally, such analysis has involved identifying specific changes to measured resonance peak parameters.  Recently, we have demonstrated a complementary approach using principal component analysis (PCA) to directly analyze the matrix of spectral data, correlating spectra according to similarities and differences in their overall shapes. This approach is particularly well-suited for spectra of mAbs, where many individual peaks may not be well resolved and peak parameters difficult to measure. Here we demonstrate the performance of the PCA method for discriminating structural variation among systematic sets of 2D NMR spectra using the NISTmAb reference material and illustrate how spectral variability identified by PCA may be correlated to structure.

    Learning Objectives: 

    1. 2D 1H-13C Methyl NMR spectra can be readily acquired on monoclonal antibody therapeutics at natural isotopic abundance.
    2. Such spectra are suitable to assess the major conformation(s) of mAb drugs in solution.
    3. Using multivariate statistics, such as principal component analysis mAb spectra can be classified based on minor structural perturbations with low levels of detection.


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