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PUBLICATIONS

Chemometric Outlier Classification of 2D-NMR Spectra to Enable Higher Order Structure Characterization of Protein Therapeutics

Published

Author(s)

David Sheen, Vincent K. Shen, Robert G. Brinson, Luke Arbogast, John Marino, Frank Delaglio

Abstract

Protein therapeutics are vitally important clinically and commercially, with monoclonal antibody (mAb) therapeutic sales alone accounting for $115 billion in revenue for 2018.[1] In order for these therapeutics to be safe and efficacious, their protein components must maintain their high order structure (HOS), which includes retaining their three-dimensional fold and not forming aggregates. As demonstrated in the recent NISTmAb Interlaboratory nuclear magnetic resonance (NMR) Study[2], NMR spectroscopy is a robust and precise approach to address this HOS measurement need. Using the NISTmAb study data, we benchmark a procedure for automated outlier detection used to identify spectra that are not of sufficient quality for further automated analysis. When applied to a diverse collection of all 252 1H,13C gHSQC spectra from the study, a recursive version of the automated procedure performed comparably to visual analysis, and identified three outlier cases that were missed by the human analyst. In total, this method represents a distinct advance in chemometric detection of outliers due to variation in both measurement and sample.
Citation
Chemometrics and Intelligent Laboratory Systems
Volume
199
Pub Type
Journals

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DOI Link

Keywords

Nuclear Magnetic Resonance (NMR), biopharmaceuticals, chemometrics, monoclonal antibody (mAb), NISTmAb, spectral similarity metric, higher order structure
Biopharmaceuticals, Numerical methods and software, Biomaterials, Biomanufacturing and Analytical chemistry

Citation

Sheen, D. , Shen, V. , Brinson, R. , Arbogast, L. , Marino, J. and Delaglio, F. (2020), Chemometric Outlier Classification of 2D-NMR Spectra to Enable Higher Order Structure Characterization of Protein Therapeutics, Chemometrics and Intelligent Laboratory Systems, [online], https://doi.org/10.1016/j.chemolab.2020.103973 (Accessed December 3, 2024)

Issues

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Created April 14, 2020, Updated October 12, 2021