OCT 24, 2019 1:30 PM PDT

Characterization of Biotherapeutics by Chemometric and Machine Learning Analysis of NMR Spectra

C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Principal Investigator, National Institute of Standards and Technology Institute Fellow, Institute for Bioscience and Biotechnology Research, University of Maryland
    BIOGRAPHY

Abstract

With annual sales exceeding $145 billion, growth of biologic therapeutics is outpacing that of small-molecule drugs, and the development, manufacture, and delivery of biologics presents very different challenges. Among biologics, monoclonal antibodies (mAbs) have become particularly attractive for drug development, since it is possible to select and duplicate antibodies that bind with high affinity and specificity to most any target, and since biomanufacturing platforms for mAbs can be reused for more than one therapeutic.

Characterization of biologics and their formulations requires monitoring high order structure (HOS), since misfolding or aggregation can lead to loss of efficacy or harmful immune responses. There are many circumstances during the development and manufacturing of a biologic where high-resolution characterization of HOS is valuable, including in evaluation of similarity or stability.  Heteronuclear Nuclear Magnetic Resonance (NMR) is a practical multi-attribute method that can be applied directly to intact biologics and their formulations to meet this important measurement need.

We have previously demonstrated practical approaches to generate two-dimensional 1H/13C NMR spectra at natural isotopic abundance for molecules as large as intact monoclonal antibodies, and the robustness and precision of these methods has recently been confirmed by an international multi-laboratory study with participation from 25 academic, pharmaceutical, governmental, and regulatory organizations.

Using measurements on the IgG1k NIST reference mAb (NISTmAb), we demonstrate that small variations of structure and interaction can be revealed and classified by direct computational analysis of the shapes of such spectra, as an alternative to interactive analysis and assignment of spectral features, paving the way for NMR HOS characterization via chemometrics and machine learning that is both objective and automated.


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OCT 24, 2019 1:30 PM PDT

Characterization of Biotherapeutics by Chemometric and Machine Learning Analysis of NMR Spectra

C.E. Credits: P.A.C.E. CE Florida CE

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