Application of NMR and Chemometric Analyses to Better Understand the Quality Attributes in pH and Thermally Degraded Monoclonal Antibodies

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Abstract

Purpose

Nuclear magnetic resonance (NMR) spectroscopy provides the sensitivity and specificity to probe the higher order structure (HOS) of monoclonal antibodies (mAbs) for potential changes. This study demonstrates an application of chemometric tools to measure differences in the NMR spectra of mAbs after forced degradation relative to the respective unstressed starting materials.

Methods

Samples of adalimumab (Humira, ADL-REF) and trastuzumab (Herceptin, TRA-REF) were incubated in three buffer-pH conditions at 40°C for 4 weeks to compare to a control sample that was left unstressed. Replicate 1D ¹H and 2D ¹H- ¹³C HMQC NMR spectra were collected on all samples. Chemometric analyses such as Easy Comparability of HOS (ECHOS), PROtein FIngerprinting by Lineshape Enhancement (PROFILE), and Principal Component Analysis (PCA) were applied to capture and quantitate differences between the spectra.

Results

Visual and statistical inspection of the 2D ¹H- ¹³C HMQC spectra of adalimumab and trastuzumab after forced degradation conditions shows no changes in the spectra relative to the unstressed material. Chemometric analysis of the 1D ¹H NMR spectra shows only minor changes in the spectra of adalimumab after forced degradation, but significant differences in trastuzumab.

Conclusion

The chemometric analyses support the lack of statistical differences in the structure of pH-thermal stressed adalimumab, however, it reveals conformational changes or chemical modifications in trastuzumab after forced degradation. Application of chemometrics in comparative NMR studies enables HOS characterization and showcases the sensitivity and specificity in detecting differences in the spectra of mAbs after pH-thermal forced degradation with respect to local and global protein structure.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-023-03600-2.

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LIII.On lines and planes of closest fit to systems of points in space

Karl Pearson (1901)

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BioMagResBank

Eldon Ulrich, Hideo Akutsu, Jurgen F. Doreleijers … (2008)

The BioMagResBank (BMRB: www.bmrb.wisc.edu) is a repository for experimental and derived data gathered from nuclear magnetic resonance (NMR) spectroscopic studies of biological molecules. BMRB is a partner in the Worldwide Protein Data Bank (wwPDB). The BMRB archive consists of four main data depositories: (i) quantitative NMR spectral parameters for proteins, peptides, nucleic acids, carbohydrates and ligands or cofactors (assigned chemical shifts, coupling constants and peak lists) and derived data (relaxation parameters, residual dipolar couplings, hydrogen exchange rates, pKa values, etc.), (ii) databases for NMR restraints processed from original author depositions available from the Protein Data Bank, (iii) time-domain (raw) spectral data from NMR experiments used to assign spectral resonances and determine the structures of biological macromolecules and (iv) a database of one- and two-dimensional 1H and 13C one- and two-dimensional NMR spectra for over 250 metabolites. The BMRB website provides free access to all of these data. BMRB has tools for querying the archive and retrieving information and an ftp site (ftp.bmrb.wisc.edu) where data in the archive can be downloaded in bulk. Two BMRB mirror sites exist: one at the PDBj, Protein Research Institute, Osaka University, Osaka, Japan (bmrb.protein.osaka-u.ac.jp) and the other at CERM, University of Florence, Florence, Italy (bmrb.postgenomicnmr.net/). The site at Osaka also accepts and processes data depositions.

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Robert A van den Berg, Huub Hoefsloot, Johan A. Westerhuis … (2006)

Background Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. Results Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. Conclusion Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis). In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.

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Author and article information

Contributors

Victor A. Beaumont:

ORCID: http://orcid.org/0000-0001-9626-0111

victor.beaumont@pfizer.com

Hai-Young Kim: hai-young.kim@pfizer.com

Journal

Journal ID (nlm-ta): Pharm Res

Journal ID (iso-abbrev): Pharm Res

Title: Pharmaceutical Research

Publisher: Springer US (New York )

ISSN (Print): 0724-8741

ISSN (Electronic): 1573-904X

Publication date (Electronic): 5 October 2023

Publication date PMC-release: 5 October 2023

Publication date (Print): 2023

Volume: 40

Issue: 10

Pages: 2457-2467

Affiliations

[1 ]GRID grid.410513.2, ISNI 0000 0000 8800 7493, Pfizer, Inc. BioTherapeutics Pharmaceutical Sciences, Analytical Research and Development, ; 1 Burtt Road, Andover, MA 01810 USA

[2 ]Present Address: Pfizer, Inc. Pharmaceutical Sciences Small Molecules, Analytical Research and Development, ( https://ror.org/04x4v8p40) Discovery Park, Ramsgate Road, Sandwich, CT13 9FF UK

[3 ]GRID grid.410513.2, ISNI 0000 0000 8800 7493, Pfizer, Inc. Global Product Development, Oncology & Rare Disease Statistics, ; New York City, NY 10001 USA

Author information

Victor A. Beaumont http://orcid.org/0000-0001-9626-0111

Article

Publisher ID: 3600

DOI: 10.1007/s11095-023-03600-2

PMC ID: 10661726

PubMed ID: 37798537

SO-VID: 52aa4d5e-0bae-49c0-b274-477af0a2c7e1

License:

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