Chemometric Outlier Classification of 2D-NMR Spectra to Enable Higher Order Structure Characterization of Protein Therapeutics
| Autor: | David A. Sheen, John P. Marino, Robert G. Brinson, Vincent K. Shen, Luke W. Arbogast, Frank Delaglio |
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| Rok vydání: | 2021 |
| Předmět: |
0303 health sciences
Protein therapeutics Computer science business.industry Process Chemistry and Technology 010401 analytical chemistry Pattern recognition Nuclear magnetic resonance spectroscopy 01 natural sciences Article 0104 chemical sciences Computer Science Applications Analytical Chemistry Characterization (materials science) Chemometrics 03 medical and health sciences Outlier Anomaly detection Artificial intelligence business Two-dimensional nuclear magnetic resonance spectroscopy Higher Order Structure Spectroscopy Software 030304 developmental biology |
| Zdroj: | Chemometr Intell Lab Syst |
| ISSN: | 0169-7439 |
| Popis: | 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 (1)H,(13)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. |
| Databáze: | OpenAIRE |
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