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Chemometric outlier classification of 2D-NMR spectra to enable higher order structure characterization of protein therapeutics by Sheen, David A.; Shen, Vincent K.; Brinson, Robert G.; Arbogast, Luke W.; Marino, John P.; Delaglio, Frank is a Chemistry article available to read on EtoBox.
What is Chemometric outlier classification of 2D-NMR spectra to enable higher order structure characterization of protein therapeutics about?
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 H,C 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.
Who reads Chemometric outlier classification of 2D-NMR spectra to enable higher order structure characterization of protein therapeutics?
It is typically read by researchers, students, and practitioners in Chemistry.
- Author
- Sheen, David A.; Shen, Vincent K.; Brinson, Robert G.; Arbogast, Luke W.; Marino, John P.; Delaglio, Frank
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0169-7439)
- Published
- 2020
- Language
- EN
- Field
- Chemistry (Physical Sciences)