OR WAIT null SECS
In the past decade, advances in both separations and mass spectrometry (MS) technologies have enabled new, streamlined, and data-rich approaches to monitor product quality attributes and their relationship with process parameters throughout the lifecycle of therapeutic proteins. As we enter a new decade of technology and method development, MS-based approaches utilized in the biopharmaceutical industry are evolving further. In this mini-review, we explore key developments that could inspire and improve the future of therapeutic protein development.
Protein-based therapeutics are inherently heterogenous because of the presence of a wide range of post-translational modifications, resulting from either the manufacturing process or degradation during storage. These liabilities must be well characterized, monitored, and controlled to ensure product safety and efficacy. High-resolution mass spectrometry (HRMS) has become an increasingly critical analytical tool for identifying, characterizing, and monitoring these attributes (1). Advances in HRMS technology have led to both the development of capabilities to aid characterization and quantitation and to the simplification of instrumentation to permit routine monitoring. Advancements of HRMS capabilities include increased resolution and faster scan rates compatible with liquid chromatography (LC) and alternative fragmentation techniques. Electron-based dissociation (ExD) and ultraviolet photodissociation (UVPD) approaches have become increasingly robust in commercial instrumentation, and these approaches can be utilized for glycosylation and disulfide characterization, confirmation of aspartic acid isomerization, and top- or middle-down sequencing. With these developments, the implementation of HRMS within the biopharmaceutical industry continues to expand and enhance the understanding of process and product quality while streamlining development.
Considerable HRMS hardware innovation has seen the development of compact and robust instrumentation, with HRMS platforms now small enough to be part of the LC stack. Furthermore, advances in acquisition and data processing software have led to ease of use and inclusion of cGMP-compliant capabilities to ensure data integrity (21 CFR part 11). This growth is due, in part, to the industry-wide interest in multi-attribute methods (MAMs), which utilizes enzymatic digestion of the therapeutic protein followed by LC–HRMS analysis. MAMs affords residue-specific identification and quantitative monitoring of multiple product quality attributes, together with the capability of new peak detection during process development and release and stability testing (2–4). Direct attribute quantitation achieved with MAMs has been demonstrated to provide a high level of agreement with the output of several established purity assays, including released glycan analysis by hydrophilic-interaction liquid chromatography (HILIC), charged variants measured by cation exchange chromatography (CEX), and fragments determined by the capillary electrophoresis–sodium dodecyl sulfate (CE-SDS) assay (4). In addition, MAMs can also be leveraged for protein identity and the monitoring of process related impurities, including protein A and host cell proteins. As a result, several purity assays that have traditionally been leveraged to track all attributes can be potentially replaced with a single MAM assay. Despite the additional complexities of the assay (including multi-step sample preparation, sophisticated instrumentation and data analysis software requirements), the advantages of MAMs have been widely recognized by both the biopharmaceutical industry and regulatory agencies, with the MAM Consortium (www.mamconsortium.org) serving as a platform to drive collaboration and growth.
HRMS instrumentation is most often utilized for MAMs, although low-resolution quadrupole instrumentation has been explored because of low costs, the small footprint, and enhanced instrument robustness (5). However, a report evaluating HRMS and low-resolution quadrupole instrumentation for MAMs found HRMS to be the preferred platform because of the enhanced selectivity, the lower limit of quantitation, the ease of method development, and the new peak detection capabilities (6). Recognizing the value of LC–HRMS data has led to extended utilization of MAMs, with examples of upstream in-process cell culture monitoring becoming available (7). Broader implementation of MAMs is likely to continue, particularly with the development of automated sample preparation techniques and artificial intelligence powered data analysis software to increase accuracy and consistency while reducing processing time (2).
HRMS-compatible purity assays for simultaneous attribute quantitation and identification is realistic with the latest technologies. The direct hyphenation of native size-exclusion chromatography (SEC) (8), CEX (9), and hydrophobic interaction chromatography (HIC) (10) with HRMS (in further discussion referred to as simply MS) has been achieved with volatile mobile phases, such as ammonium acetate. Additionally, capillary zone electrophoresis (CZE)–MS (11,12) and, more recently, imaged capillary isoelectric focusing (iCIEF)–MS (13) have matured to the point of commercial availability. These new approaches offer the rapid determination of attributes associated with each discrete chromatographic or electrophoretic peak. As with the established purity assays, the “single” attributes are typically quantitated using the peak area of each region (for example, the basic or acidic region in CEX). The deconvoluted spectra are evaluated for potential attribute identifications. These spectra are typically highly complex, especially in cases where multiple attributes make up each peak. For many glycoproteins, major glycoforms are often the most abundant species, and are easily identified by the characteristic series of +162 Da hexoses additions. The mass shifts relative to the main chromatographic or electrophoretic peak can assist with the identification of the separated variants. Highly encouraging proof-of-concept work has focused on the characterization of attributes with large mass shifts such as size variants, glycoforms, incomplete processing of C-terminal lysine, and drug-antibody ratios (12,14–16). The assignment of attributes with small or nonexistent mass shifts, such as deamidation (Δ0.98 Da) and aspartic acid isomerization (Δ0 Da), remains challenging, but can be inferred based on a shift in retention or migration time.
Despite the improvements in both separation technologies and MS capabilities, in-depth characterization and monitoring of key product quality attributes at the intact level remains challenging, because of the inherent complexity and the size of the molecules (~150 kDa for an IgG). Further, the ability of intact analysis to localize the attribute to the amino acid residue is limited, and, hence, it is difficult to determine criticality. To improve MS-based identification and quantitation of intact proteins, it is essential to reduce this complexity by either increasing the peak capacity of the separation to resolve each variant, or reducing the complexity of the sample itself. Multidimensional analysis can be very effective to reduce complexity, and is increasingly being explored; a recent comprehensive review has discussed advances in this field (17). Nevertheless, the integration of these rapid separation techniques for at-line analysis during protein production for real-time process feedback has the potential to improve the future of therapeutic protein development.
Reducing the size and complexity of protein-based therapeutics into smaller fragments improves the ability to confidently identify variant species without the need for multidimensional separation instrumentation. A simple reduction of the disulfide bonds of an IgG yields two heavy chain and two light chain species of ~50 kDa and ~25 kDa, respectively. The heavy chain and light chain species are routinely separated chromatographically or electrophoretically, and the simplification of the spectra assists with attribute identification and the localization of the attribute to the subunit. The enhanced resolution of modern MS instrumentation enables the acquisition of isotopically resolved spectra of both the light chain and heavy chain for improved confidence in primary sequence confirmation and attribute assignment. Two options for the relative quantitation of attributes at the subunit level have been described; the first more conventional approach relies on the chromatographic or electrophoretic separation of the modified species from the unmodified species. The second approach involves deconvolution of the raw spectra followed by the relative quantitation of the attribute based on the intensities of the deconvoluted spectral peaks separated by mass. The ability to quantitate using deconvoluted spectra reduces analysis time significantly because there is less dependency on the upfront separation. This approach was explored for the at- line bioreactor monitoring of glycosylation to support process improvements (18), and has recently been validated for the cGMP monitoring of mannose-5 in routine manufacturing (19).
Further reduction in the molecule size and complexity can be achieved with limited proteolysis and a suite of enzymes that facilitate a highly site-specific cleavage are gaining popularity. The most commonly employed is IdeS (immunoglobulin-degrading enzyme of S. pyogenes), which cleaves the IgG heavy chain below the hinge region, producing the F(ab’)2 and Fc fragments with molecular weights of ~100 kDa and ~25 kDa, respectively (20). A successive chemical reduction of the disulfide bonds produces three ~25 kDa fragments—the light chain, the Fc, and the Fd. The simplified data permits a high confidence mass determination and the ability to localize key attributes to a fragment. Furthermore, the ~25 kDa fragments are highly amenable to middle down sequencing by ExD and UVPD fragmentation, which has the potential to localize the attribute at the amino acid residue. As above, attributes can be characterized and quantitated from the separation, or from the deconvoluted mass spectra. Chromatographically separated identifications of clips, select charge variants, and glycoforms have been successfully demonstrated (20–23). An interesting body of work, poised to be impactful, has further reduced the spectral complexity for attribute quantitation of glycoproteins using deconvoluted mass spectra. The endoglycosidases, EndoS or EndoS2, are employed to selectively cleave between the two N-acetyl glucosamine (GlcNAc) residues of the core glycan, leaving a single residue with or without fucose, thus reducing the complexity related to glycan heterogeneity.
This approach has been demonstrated for the quantitative monitoring of aglycosylation, afucosylation, glycation, and high mannose, and the correlation with the relevant bioassay output confirms the potential applicability of this assay (18,24). The fast sample preparation and rapid analysis enables the same simple method to be extended for the quantitative monitoring of Fc methionine oxidation (MetOx) (25). The success of this work has led to the transfer, co-validation, implementation, and regulatory approval of the subunit Fc MetOx method in commercial QC laboratories for product release and stability testing, a key milestone for HRMS (26). Automated, compliant-ready software tools are becoming available to streamline the quantitation of deconvoluted subunit mass spectra and will likely increase the adoption of this workflow to support an wider number of attributes.
The past decade has seen enormous advancements in MS instrumentation and software capabilities for improved characterization and quantitation of the attributes of protein-based therapeutics. MS-compatible separation approaches and new enzymes continue to be introduced to the market, breaking down the complexity of the protein-based therapeutics prior to introduction to the MS. As technology matures and software evolves to simplify data analysis, it will be exciting to see the increasing adoption of ExD and UVPD for primary sequence confirmation and attribute localization with top- and middle-down approaches. We expect to see increasing applications of HRMS, particularly MAMs, in the cGMP environment, and the global regulatory acceptance of HRMS-based methods to replace established assays. The sustained implementation of HRMS-based technologies will continue to advance process and product improvements.
(1) S. Rogstad, H. Yan, X. Wang, D. Powers, K. Brorson, B. Damdinsuren, and S. Lee, Anal. Chem. 91, 14170–14177 (2019). doi:10.1021/acs.analchem.9b03808
(2) D. Ren, Trends Biotechnol. 38, 1051–1053 (2020). doi:10.1016/j.tibtech.2020.06.007
(3) R.S. Rogers, M. Abernathy, D.D. Richardson, J.C. Rouse, J.B. Sperry, P. Swann, J. Wypych, C. Yu, L. Zang, and R. Deshpande, AAPS J. 20, 7 (2017). doi:10.1208/s12248-017-0168-3
(4) R.S. Rogers, N.S. Nightlinger, B. Livingston, P. Campbell, R. Bailey, and A. Balland, MAbs 7, 881–890 (2015). doi:10.1080/19420 862.2015.1069454
(5) W. Xu, R.B. Jimenez, R. Mowery, H. Luo, M. Cao, N. Agarwal, I. Ramos, X. Wang, and J. Wang, MAbs 9, 1186–1196 (2017). doi:10.1080/19420862.2017.1364326
(6) Z. Zhang, P.K. Chan, J. Richardson, and B. Shah, MAbs 12, 1783062 (2020). doi:10.1080/19420862.2020.1783062
(7) C. Jakes, S. Millan-Martin, S. Carillo, K. Scheffler, I. Zaborowska, and J. Bones, J. Am. Soc. Mass Spectrom. 32, 1998–2012 (2021). doi:10.1021/jasms.0c00432
(8) M. Haberger, M. Leiss, A. K. Heidenreich, O. Pester, G. Hafenmair, M. Hook, L. Bonnington, H. Wegele, M. Haindl, D. Reusch, and P. Bulau, MAbs 8, 331–339 (2016). doi:10.1080/19420862.2015.1122150
(9) F. Fussl, K. Cook, K. Scheffler, A. Farrell, S. Mittermayr, and J. Bones, Anal. Chem. 90, 4669–4676 (2018). doi:10.1021/acs.analchem.7b05241
(10) B. Wei, G. Han, J. Tang, W. Sandoval, and Y.T. Zhang, Anal. Chem. 91, 15360–15364 (2019). doi:10.1021/acs.analchem.9b04467
(11) R. Haselberg, G.J. de Jong, and G.W. Somsen, Anal. Chem. 85, 2289–2296 (2013). doi:10.1021/ac303158f
(12) E.A. Redman, N.G. Batz, J.S. Mellors, and J.M. Ramsey, Anal. Chem. 87, 2264–2272 (2015). doi:10.1021/ac503964j
(13) S. Mack, D. Arnold, G. Bogdan, L. Bousse, L. Danan, V. Dolnik, M. Ducusin, E. Gwerder, C. Herring, M. Jensen, J. Ji, S. Lacy, C. Richter, I. Walton, and E. Gentalen, Electrophoresis 40, 3084–3091 (2019). doi:10.1002/elps.201900325
(14) F. Fussl, A. Trappe, S. Carillo, C. Jakes, and J. Bones, Anal. Chem. 92, 5431–5438 (2020). doi:10.1021/acs.analchem.0c00185
(15) C. Gstottner, S. Nicolardi, M. Haberger, D. Reusch, M. Wuhrer, and E. Dominguez-Vega, Anal. Chim. Acta. 1134, 18–27 (2020). doi:10.1016/j.aca.2020.07.069
(16) E.A. Redman, J.S. Mellors, J.A. Starkey, and J.M. Ramsey, Anal. Chem. 88, 2220–2226 (2016). doi:10.1021/acs.analchem.5b03866
(17) J. Camperi, A. Goyon, D. Guillarme, K. Zhang, and C. Stella, Analyst 146, 747–769 (2021). doi:10.1039/d0an01963a
(18) J. Dong, N. Migliore, S.J. Mehrman, J. Cunningham, M.J. Lewis and P. Hu, Anal. Chem. 88, 8673–8679 (2016). doi:10.1021/acs.analchem.6b01956
(19) M. Schilling, P. Feng, Z. Sosic, and S.L. Traviglia, Bioengineered 11, 1301–1312 (2020). doi:10.1080/21655979.2020.1842651
(20) J. Sjogren, F. Olsson, and A. Beck, Analyst 141, 3114–3125 (2016). doi:10.1039/c6an00071a
(21) Y. An, Y. Zhang, H.M. Mueller, M. Shameem, and X. Chen, MAbs 6, 879–893 (2014). doi:10.4161/mabs.28762
(22) V. D’Atri, S. Fekete, A. Beck, M. Lauber, and D. Guillarme, Anal. Chem. 89, 2086–2092 (2017). doi:10.1021/acs.analchem.6b04726
(23) T. Formolo, M. Ly, M. Levy, L. Kilpatrick, S. Lute, K. Phinney, L. Marzilli, K. Brorson, M. Boyne, D. Davis, and J. Schiel, in State-of-the-Art and Emerging Technologies for Therapeutic Monoclonal Antibody Characterization Volume 2. Biopharmaceutical Characterization: The NISTmAb Case Study. (American Chemical Society, Washington, D.C., 2015), pp. 1–62.
(24) R. Upton, L. Bell, C. Guy, P. Caldwell, S. Estdale, P.E. Barran, and D. Firth, Anal. Chem. 88, 10259–10265 (2016). doi:10.1021/acs.analchem.6b02994
(25) I. Sokolowska, J. Mo, J. Dong, M.J. Lewis, and P. Hu, MAbs 9, 498–505 (2017). doi:10.1 080/19420862.2017.1279773
(26) I. Sokolowska, J. Mo, F. Rahimi Pirkolachahi, C. McVean, L.A.T. Meijer, L. Switzar, C. Balog, M.J. Lewis, and P. Hu, Anal. Chem. 92, 2369–2373 (2020). doi:10.1021/acs. analchem.9b05036
Esme Candish is a Process Development Scientist at Amgen, in Cambridge, Massachusetts. Andrew Dykstra is a Process Development Principal Scientist at Amgen, in Thousand Oaks, California. Alla Polozova is a Director of Process Development at Amgen, in Cambridge, Massachusetts. Da Ren is a Process Development Scientific Director at Amgen, in Thousand Oaks, California. Hao Zhang is a Process Development Senior Scientist at Amgen, in Cambridge, Massachusetts. Direct correspondence to: email@example.com