Peptide mapping with mass spectrometry (MS) is an important tool for protein characterization in the biopharmaceutical industry. Historically, peptide mapping monitors post-translational modifications (PTMs) of protein products and process intermediates during development. Multi-attribute monitoring (MAM) methods have been used previously in commercial release and stability testing panels to ensure control of selected critical quality attributes (CQAs). Our goal is to use MAM methods as part of an overall analytical testing strategy specifically focused on CQAs, while removing or replacing historical separation methods that do not effectively distinguish CQAs from non-CQAs due to co-elution. For example, in this study, we developed a strategy to replace a profile-based ion-exchange chromatography (IEC) method using a MAM method in combination with traditional purity methods to ensure control of charge variant CQAs for a commercial antibody (mAb) drug product (DP). To support this change in commercial testing strategy, the charge variant CQAs were identified and characterized during development by high-resolution LC-MS and LC-MS/MS. The charge variant CQAs included PTMs, high molecular weight species, and low molecular weight species. Thus, removal of the IEC method from the DP specification was achieved using a validated LC-MS MAM method on a QDa system to directly measure the charge variant PTM CQAs in combination with size exclusion chromatography (SE-HPLC) and capillary electrophoresis (CE-SDS) to measure the non-PTM charge variant CQAs. Bridging data between the MAM, IEC, and SE-HPLC methods were included in the commercial marketing application to justify removing IEC from the DP specification. We have also used this MAM method as a test for identity to reduce the number of QC assays. This strategy has received approvals from several health authorities.

The multi-attribute method (MAM), a liquid chromatography-mass spectrometry (LC-MS)-based peptide mapping method, has gained increased interest and applications in the biopharmaceutical industry. MAM can, in one method, provide targeted quantitation of multiple site-specific product quality attributes, as well as new peak detection. In this review, we focus on the scientific and regulatory considerations of using MAM in product quality attribute monitoring and quality control (QC) of therapeutic proteins. We highlight MAM implementation challenges and solutions with several case studies, and provide our perspective on the opportunities to use MS in QC for applications other than standard peptide mapping-based MAM.

Succinimide (Asu) is the intermediate for asparagine deamidation in therapeutic proteins, and it can be readily hydrolyzed to form aspartate and iso-aspartate residues. Moreover, Asu plays an important role in the protein degradation pathways, asparagine deamidation, and aspartic acid isomerization. Here, Asu modification with a high abundance in the framework region (FR) of golimumab was first reported, the effect of denaturing buffer pH on the Asu modification homeostasis was studied, and the results revealed that it was relatively stable over a pH range of 6.0-7.0 whereas a rapid decrease at pH 8.0. Then, the peptide-based multi-attribute method (MAM) analyses showed that the Asu formation was at Asn 43 in the FR of the heavy chain. Meanwhile, the efficacy [affinity, binding and bioactivity, complement-dependent cytotoxicity (CDC) activity, and antibody-dependent cell-mediated cytotoxicity (ADCC) activity] and stability of the Asu modification of golimumab were evaluated, and the current results demonstrated comparable efficacy and stability between the Asu low- and high-abundance groups. Our findings provide valuable insights into Asu modification and its effect on efficacy and stability, and this study also demonstrates that there is a need to develop a broad-spectrum, rapid, and accurate platform to identify and characterize new peaks in the development of therapeutic proteins, particularly for antibody drugs.

The Multi-Attribute Method (MAM) Consortium was initially formed as a venue to harmonize best practices, share experiences, and generate innovative methodologies to facilitate widespread integration of the MAM platform, which is an emerging ultra-high-performance liquid chromatography-mass spectrometry application. Successful implementation of MAM as a purity-indicating assay requires new peak detection (NPD) of potential process- and/or product-related impurities. The NPD interlaboratory study described herein was carried out by the MAM Consortium to report on the industry-wide performance of NPD using predigested samples of the NISTmAb Reference Material 8671. Results from 28 participating laboratories show that the NPD parameters being utilized across the industry are representative of high-resolution MS performance capabilities. Certain elements of NPD, including common sources of variability in the number of new peaks detected, that are critical to the performance of the purity function of MAM were identified in this study and are reported here as a means to further refine the methodology and accelerate adoption into manufacturer-specific protein therapeutic product life cycles.

The multi-attribute method (MAM) based on liquid chromatography (LC)-tandem mass spectrometry is emerging as a powerful tool to directly monitor multiple product quality attributes simultaneously. To better implement MAM, either for product characterization or for quality control (QC), there is a need for a robust, universal, and high-throughput workflow that can be broadly adopted in different laboratories with minimal barriers to implementation. Manual preparation of samples for MAM, however, is labor intensive and produces nontrivial variations across analysts and laboratories. We describe the development of a fully automated peptide mapping procedure with a high-throughput robotic liquid handling system to improve sample handling capacity and outcome reproducibility while saving analyst hands-on time. Our procedure features the automation of a \"microdialysis\" step, an efficient desalting approach prior to proteolytic digestion that optimizes digestion completeness and consistency each time. The workflow is completely hands-free and requires the analyst only to pre-normalize the sample concentrations and to load buffers and reagents at their designated positions on the robotic deck. The robotic liquid handler performs all the subsequent preparation steps and stores the digested samples on a chiller unit to await retrieval for further analysis. We also demonstrate that the manual and automated procedures are comparable with regard to protein sequence coverage, digestion completeness and consistency, and quantification of posttranslational modifications. Notably, in contrast to a previously reported automated sample preparation protocol that relied on customized accessories, all components in our automation procedure are commercial products that are readily available. In addition, we also present the high-throughput data analysis workflow by using Protein Metrics. The automation procedure can be applied cross-functionally in the biopharmaceutical industry and, given its practicality and reproducibility, can pave the way for MAM implementation in QC laboratories.

More than 370 biotherapeutics drug products have been approved by regulatory agencies on the US and EU markets and this industry continues to expand. Process change and optimization is necessary to develop new effective biologics in a cost effective and productive way. Consequently, improvement of analytical techniques is required for better product characterization according to Quality by Design (QbD) approach recommended by regulatory agencies. Recently, multi-attribute method (MAM) has emerged to meet such demands using mass spectrometry coupled to liquid chromatography (LC-MS). However, traditional sample preparation or data processing would not be suitable to guide process development, because one of the common challenges during development of analytical platforms is instrument or method variability which can cause deviation in results. Here, we show a new automated analytical platform for MAM implemented on 3 different sites: the components of MAM platform include automated sample preparation, LC-MS based MAM, and data treatment automation. To our knowledge, this is the first study to show global harmonization on automated MAM platforms and the inter-sites comparability including the automated sample preparation and LC-MS instrument. Also, we demonstrate the applicability of MAM to support cell line development, cell culture process development and downstream process development. We expect that this MAM platform will effectively guide process development across multiple projects.

The multi-attribute method (MAM) has garnered attention as a new quality control method of therapeutic monoclonal antibodies (mAbs). MAM analysis allows multiple relative quantifications of several structural attributes of therapeutic mAbs; however, some issues remain to be addressed in its procedures especially for sample preparation. The goal of this study was to optimize the sample preparation method for MAM analysis of mAbs. Using a model mAb, we compared five sample preparation methods based on sequence coverage, peptide redundancy, missed cleavage and chemical deamidation. It was found that low pH buffer and short digestion time reduced artificial deamidation. The desalting process after carboxymethylation was essential to obtaining high sequence coverage by a short digestion time. The generation of missed cleavage peptides was also improved by using a trypsin/lysyl endopeptidase (Lys-C) mixture. Next, we evaluated the usefulness of our method as a part of MAM analysis. Finally, 17 glycopeptides, 2 deamidated peptides and N- and C-terminal peptides of the heavy chain were successfully monitored with acceptable mass accuracy and coefficient of variation (CV, %) of the relative peak area. On the other hand, 4 oxidated peptides indicated the unavoidable slightly higher inter-assay CV (%) of the peak area ratio due to the instability in the MS sample solution. Collectively, we demonstrated that our method was applicable as an easy and reliable sample preparation method for MAM analysis, and the variation in the relative peak area could be influenced by the modification type rather than by the amount of each peptide.

Multi-attribute methods (MAM), based on proteolytic digestion followed by liquid chromatography-mass spectrometry analysis of proteolytic peptides, have gained substantial attention in the biopharmaceutical industry for quantifying a variety of quality attributes for therapeutic proteins. Most MAM developed so far have been based on high-resolution mass spectrometers, due to their superb resolving power to distinguish analyte signals from interferences. Lower-resolution instruments, if demonstrated suitable, may further promote the adoption of the technology due to their low cost, small footprint, and ease of use. In this work, we compared the performance of a high-resolution instrument with a few low-resolution quadrupole-type instruments in quantifying a diverse set of quality attributes in a monoclonal antibody product. Different modes of operation for the quadrupole instruments, including scan mode, selected-ion monitoring and multiple-reaction monitoring, were evaluated. The high-resolution instrument has superb performance, with a quantitation limit of 0.002%. Single-quadrupole instruments in scan mode, on the other hand, provide a quantitation limit of about 1%, which may be fit-for-purpose for many routine MAM applications.

Combination therapy is a fast-growing strategy to maximize therapeutic benefits to patients. Co-formulation of two or more therapeutic proteins has advantages over the administration of multiple medications, including reduced medication errors and convenience for patients. Characterization of co-formulated biologics can be challenging due to the high degree of similarity in the physicochemical properties of co-formulated proteins, especially at different concentrations of individual components. We present the results of a deamidation study of one monoclonal antibody component (mAb-B) in co-formulated combination antibodies (referred to as COMBO) that contain various ratios of mAb-A and mAb-B. A single deamidation site in the complementarity-determining region of mAb-B was identified as a critical quality attribute (CQA) due to its impact on biological activity. A conventional charge-based method of monitoring mAb-B deamidation presented specificity and robustness challenges, especially when mAb-B was a minor component in the COMBO, making it unsuitable for lot release and stability testing. We developed and qualified a new, quality-control-friendly, single quadrupole Dalton mass detector (QDa)-based method to monitor site-specific deamidation. Our approach can be also used as a multi-attribute method for monitoring other quality attributes in COMBO. This analytical paradigm is applicable to the identification of CQAs in combination therapeutic molecules, and to the subsequent development of a highly specific, highly sensitive, and sufficiently robust method for routine monitoring CQAs for lot release test and during stability studies.

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