Solid-State NMR Characterization of Lyophilized Formulations of Monoclonal Antibody Therapeutics.

Monoclonal antibodies (mAbs) are an important and growing class of biotherapeutic drugs. Method development for the characterization of critical quality attributes, including higher-order structure (HOS), of mAbs remains an area of active inquiry. Recently, solution-state nuclear magnetic resonance (NMR) spectroscopy has received increased attention and is a means for reliable, high-resolution HOS characterization of aqueous-based preparations of mAbs. While mAbs are predominantly formulated in solution, up to 20% are prepared as solid amorphous powders and techniques for the robust characterization of HOS in the solid state remain limited. We propose here the use of solid-state NMR (ssNMR) fingerprinting to inform directly on the HOS of solid preparations of mAbs. Using lyophilized samples of the NISTmAb reference material prepared with different formulation conditions, we demonstrate that 1H-13C cross-polarization (hC-CP) buildup spectral series at natural isotopic abundance mAb samples are sensitive to differences in formulation. We also demonstrate that principal component analysis (PCA) can be used to differentiate the samples from one another in a user-independent manner while also highlighting areas where expert analysis can provide structural details about important molecular interactions in solid-phase protein formulations. Results from this study contribute to establishing the foundation for the use of ssNMR for HOS characterization of solid-phase biotherapeutics.

Principal component analysis for automated classification of 2D spectra and interferograms of protein therapeutics: influence of noise, reconstruction details, and data preparation.

Protein therapeutics have numerous critical quality attributes (CQA) that must be evaluated to ensure safety and efficacy, including the requirement to adopt and retain the correct three-dimensional fold without forming unintended aggregates. Therefore, the ability to monitor protein higher order structure (HOS) can be valuable throughout the lifecycle of a protein therapeutic, from development to manufacture. 2D NMR has been introduced as a robust and precise tool to assess the HOS of a protein biotherapeutic. A common use case is to decide whether two groups of spectra are substantially different, as an indicator of difference in HOS. We demonstrate a quantitative use of principal component analysis (PCA) scores to perform this decision-making, and demonstrate the effect of acquisition and processing details on class separation using samples of NISTmAb monoclonal antibody Reference Material subjected to two different oxidative stress protocols. The work introduces an approach to computing similarity from PCA scores based upon the technique of histogram intersection, a method originally developed for retrieval of images from large databases. Results show that class separation can be robust with respect to random noise, reconstruction method, and analysis region selection. By contrast, details such as baseline distortion can have a pronounced effect, and so must be controlled carefully. Since the classification approach can be performed without the need to identify peaks, results suggest that it is possible to use even more efficient measurement strategies that do not produce spectra that can be analyzed visually, but nevertheless allow useful decision-making that is objective and automated.

Comparative Analysis of One-Dimensional Protein Fingerprint by Line Shape Enhancement and Two-Dimensional 1H,13C Methyl NMR Methods for Characterization of the Higher Order Structure of IgG1 Monoclonal Antibodies.

The use of NMR spectroscopy has emerged as a premier tool to characterize the higher order structure of protein therapeutics and in particular IgG1 monoclonal antibodies (mAbs). Due to their large size, traditional 1H-15N correlation experiments have proven exceedingly difficult to implement on mAbs, and a number of alternative techniques have been proposed, including the one-dimensional (1D) 1H protein fingerprint by line shape enhancement (PROFILE) method and the two-dimensional (2D) 1H-13C methyl correlation-based approach. Both 1D and 2D approaches have relative strengths and weaknesses, related to the inherent sensitivity and resolution of the respective methods. To further increase the utility of NMR to the biopharmaceutical community, harmonized criteria for decision making in employing 1D and 2D approaches for mAb characterization are warranted. To this end, we have conducted an interlaboratory comparative study of the 1D PROFILE and 2D methyl methods on several mAbs samples to determine the degree to which each method is suited to detect spectral difference between the samples and the degree to which results from each correlate with one another. Results from the study demonstrate both methods provide statistical data highly comparable to one another and that each method is capable of complementing the limitations commonly associated with the other, thus providing a better overall picture of higher order structure.

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

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.

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

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.

Selective suppression of excipient signals in 2D 1H-13C methyl spectra of biopharmaceutical products.

While the use of 1H-13C methyl correlated NMR spectroscopy at natural isotopic abundance has been demonstrated as feasible on protein therapeutics as large as monoclonal antibodies, spectral interference from aliphatic excipients remains a significant obstacle to its widespread application. These signals can cause large baseline artifacts, obscure protein resonances, and cause dynamic range suppression of weak peaks in non-uniform sampling applications, thus hampering both traditional peak-based spectral analyses as well as emerging chemometric methods of analysis. Here we detail modifications to the 2D 1H-13C gradient-selected HSQC experiment that make use of selective pulsing techniques for targeted removal of interfering excipient signals in spectra of the NISTmAb prepared in several different formulations. This approach is demonstrated to selectively reduce interfering excipient signals while still yielding 2D spectra with only modest losses in protein signal. Furthermore, it is shown that spectral modeling based on the SMILE algorithm can be used to simulate and subtract any residual excipient signals and their attendant artifacts from the resulting 2D NMR spectra.

Multivariate Analysis of Two-Dimensional 1H, 13C Methyl NMR Spectra of Monoclonal Antibody Therapeutics To Facilitate Assessment of Higher Order Structure.

Two-dimensional (2D) 1H-13C methyl NMR provides a powerful tool to probe the higher order structure (HOS) of monoclonal antibodies (mAbs), since spectra can readily be acquired on intact mAbs at natural isotopic abundance, and small changes in chemical environment and structure give rise to observable changes in corresponding spectra, which can be interpreted at atomic resolution. This makes it possible to apply 2D NMR spectral fingerprinting approaches directly to drug products in order to systematically characterize structure and excipient effects. Systematic collections of NMR spectra are often analyzed in terms of the changes in specifically identified peak positions, as well as changes in peak height and line widths. A complementary approach is to apply principal component analysis (PCA) directly to the matrix of spectral data, correlating spectra according to similarities and differences in their overall shapes, rather than according to parameters of individually identified peaks. This is particularly well-suited for spectra of mAbs, where some of the individual peaks might not be well resolved. Here we demonstrate the performance of the PCA method for discriminating structural variation among systematic sets of 2D NMR fingerprint spectra using the NISTmAb and illustrate how spectral variability identified by PCA may be correlated to structure.

2D (1)H(N), (15)N Correlated NMR Methods at Natural Abundance for Obtaining Structural Maps and Statistical Comparability of Monoclonal Antibodies.

PURPOSE: High-resolution nuclear magnetic resonance spectroscopy (NMR) provides a robust approach for producing unique spectral signatures of protein higher order structure at atomic resolution. Such signatures can be used as a tool to establish consistency of protein folding for the assessment of monoclonal antibody (mAb) drug quality and comparability. METHODS: Using the NIST monoclonal antibody (NISTmAb) and a commercial-sourced polyclonal antibody, both IgG1κ isotype, we apply 2D NMR methods at natural abundance for the acquisition and unbiased statistical analysis of (1)H(N) -(15)N correlated spectra of intact antibody (Ab) and protease-cleaved Fab and Fc fragments. RESULTS: The study demonstrates the feasibility of applying 2D NMR techniques to Abs and the precision with which these methods can be used to map structure and establish comparability between samples at atomic resolution. CONCLUSIONS: The statistical analyses suggests that, within the limit of detection, no significant structural differences are observed between the Fab and Fc domains of each respective intact Ab and its corresponding fragments. Discrimination between dissimilar species, such as between the Fab domains of both Abs or between the glycosylated and deglycosylated Fc domains, was further demonstrated. As such, these methods should find general utility for the assessment of mAb higher order structure.

Mapping monoclonal antibody structure by 2D 13C NMR at natural abundance.

Monoclonal antibodies (mAbs) represent an important and rapidly growing class of biotherapeutics. Correct folding of a mAb is critical for drug efficacy, while misfolding can impact safety by eliciting unwanted immune or other off-target responses. Robust methods are therefore needed for the precise measurement of mAb structure for drug quality assessment and comparability. To date, the perception in the field has been that NMR could not be applied practically to mAbs due to the size (∼150 kDa) and complexity of these molecules, as well as the insensitivity of the method. The feasibility of applying NMR methods to stable isotope-labeled, protease-cleaved, mAb domains (Fab and Fc) has been demonstrated from both E. coli and Chinese hamster ovaries (CHO) cell expression platforms; however, isotopic labeling is not typically available when analyzing drug products. Here, we address the issue of feasibility of NMR-based mapping of mAb structure by demonstrating for the first time the application of a 2D (13)C NMR methyl fingerprint method for structural mapping of an intact mAb at natural isotopic abundance. Further, we show that 2D (13)C NMR spectra of protease-cleaved Fc and Fab fragments can provide accurate reporters on the domain structures that can be mapped directly to the intact mAb. Through combined use of rapid acquisition and nonuniform sampling techniques, we show that these Fab and Fc fingerprint spectra can be rapidly acquired in as short as approximately 30 min.

Principal Component Analysis of 1D 1H Diffusion Edited NMR Spectra of Protein Therapeutics.

The one-dimensional (1D) diffusion edited proton NMR method, Protein Fingerprint by Lineshape Enhancement (PROFILE) has been demonstrated to be suitable for higher order structure (HOS) characterization of protein therapeutics including monoclonal antibodies. Recent reports in the literature have demonstrated its advantages for HOS characterization over traditional methods such as circular dichroism and Fourier-transform infrared spectroscopy. Previously, we have demonstrated that the PROFILE method is complementary with high resolution 2D methyl correlated NMR methods and how both may be deployed as a multi-modal platform to further the utility of NMR for HOS characterization. A major limitation of the PROFILE method remains its need for high signal to noise data due to its reliance on convolution difference processing and linear correlation metrics to assess spectral similarity. Here we present an alternative method for analyzing 1D diffusion edited spectra, which overcomes this limitation by using nonlinear iterative partial least squares (NIPALS) principal component analysis, and which we dub PROtein Fingerprint Observed Using NIPALS Decomposition (PROFOUND). We demonstrate that results from the PROFOUND method are robust with respect to instrument, operator and in the presence of high experimental noise and how it may be employed to provide quantitative assessment of spectral similarity.