Best Practices in Utilization of 2D-NMR Spectral Data as the Input for Chemometric Analysis in Biopharmaceutical...

Quality attributes (QAs) are measureable parameters of a biologic that impact product safety and efficacy and are essential characteristics that are linked to positive patient health outcomes. One QA, higher order structure (HOS), is directly coupled to the function of protein biologics, and deviations in this QA may cause adverse effects. To address the critical need for HOS assessment, methods for analyzing structural fingerprints from 2D nuclear magnetic resonance spectroscopy (2D-NMR) spectra have been established for drug substances as large as monoclonal antibody therapeutics. Here, chemometric analyses have been applied to 2D ¹H,¹³C-methyl NMR correlation spectra of the IgG1κ NIST monoclonal antibody (NISTmAb), recorded at natural isotopic abundance, to benchmark the performance and robustness of the methods. In particular, a variety of possible spectral input schemes (e.g., chemical shift, peak intensity, and total spectral matrix) into chemometric algorithms are examined using two case studies: (1) a large global 2D-NMR interlaboratory study and (2) a blended series of enzymatically glycan-remodeled NISTmAb isoforms. These case studies demonstrate that the performance of chemometric algorithms using either peak positions or total spectral matrix as the input will depend on the study design and likely be product-specific. In general, peak positions are found to be a more robust spectral parameter for input into chemometric algorithms, whereas the total spectral matrix approach lends itself to easier automation and requires less user intervention. Analysis with different input data also shows differences in sensitivity to certain changes in HOS, highlighting that product knowledge will further guide appropriate method selection based on the fit-for-purpose application in the context of biopharmaceutical development, production, and quality control.