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.

Monoclonal antibodies (mAbs) represent the largest class of therapeutic protein drug products. mAb glycosylation produces a heterogeneous, analytically challenging distribution of glycoforms that typically should be adequately characterized because glycosylation-based product quality attributes (PQAs) can impact product quality, immunogenicity, and efficacy. In this study, two products were compared using a panel of analytical methods. Two high-resolution mass spectrometry (HRMS) workflows were used to analyze N-glycans, while nuclear magnetic resonance (NMR) was used to generate monosaccharide fingerprints. These state-of-the-art techniques were compared to conventional analysis using hydrophilic interaction chromatography (HILIC) coupled with fluorescence detection (FLD). The advantages and disadvantages of each method are discussed along with a comparison of the identified glycan distributions. The results demonstrated agreement across all methods for major glycoforms, demonstrating how confidence in glycan characterization is increased by combining orthogonal analytical methodologies. The full panel of methods used represents a diverse toolbox that can be selected from based on the needs for a specific product or analysis.

This review provides an overview of the latest advancements and applications in multi-dimensional liquid chromatography coupled with mass spectrometry (mD-LC-MS), covering aspects such as inter-laboratory studies, digestion strategy, trapping column, and multi-level analysis. The shift from an offline to an online workflow reduces sample processing artifacts, analytical variability, analysis time, and the labor required for data acquisition. Over the past few years, this technique has demonstrated sufficient maturity for application across a diverse range of complex products. Moreover, there is potential for this strategy to evolve into an integrated process analytical technology tool for the real-time monitoring of monoclonal antibody quality. This review also identifies emerging trends, including its application to new modalities, the possibility of evaluating biological activity within the mD-LC set-up, and the consideration of multi-dimensional capillary electrophoresis as an alternative to mD-LC. As mD-LC-MS continues to evolve and integrate emerging trends, it holds the potential to shape the next generation of analytical tools, offering exciting possibilities for enhanced characterization and monitoring of complex biopharmaceutical products.

Multi-attribute methods employing mass spectrometry are applied throughout the biopharmaceutical industry for product and process characterization purposes but are not yet widely accepted as a method for batch release and stability testing under the good manufacturing practice (GMP) regime, due to limited experience and level of comfort with the technical, compliance and regulatory aspects of its implementation at quality control (QC) laboratories. This article is the second part of a two-tiered publication aiming at providing guidance for implementation of the multi-attribute method by peptide mapping liquid chromatography mass spectrometry (MAM) in a QC laboratory. The first part [1] focuses on technical considerations, while this second part provides considerations related to GMP compliance and regulatory aspects. This publication has been prepared by a group of industry experts representing 14 globally acting major biotechnology companies under the umbrella of the European Federation of Pharmaceutical Industries and Associations (EFPIA) Manufacturing & Quality Expert Group (MQEG).

Biopharmaceutical products have seen rapid growth over the past few decades and continue to dominate the global pharmaceutical market. Aligning with the quality by design (QbD) framework and realization, recent advances in liquid chromatography-mass spectrometry (LC-MS) instrumentation and related techniques have enhanced biopharmaceutical characterization capabilities and have supported an increased development of biopharmaceutical products. Beyond its routine qualitative characterization, the quantitative feature of LC-MS has unique applications in biopharmaceutical process development and manufacturing. This review describes the recent applications and implications of the advancement of quantitative MS methods in biopharmaceutical process development, and characterization of biopharmaceutical product, product-related variants, and process-related impurities. We also provide insights on the emerging applications of quantitative MS in the lifecycle of biopharmaceutical product development including quality control in the Good Manufacturing Practice (GMP) environment and process analytical technology (PAT) practices during process development and manufacturing. Through collaboration with instrument and software vendors and regulatory agencies, we envision broader adoption of phase-appropriate quantitative MS-based methods for the analysis of biopharmaceutical products, which in turn has the potential to enable manufacture of higher quality products for patients.

The multi-attribute method (MAM) is a liquid chromatography-mass spectrometry (LC-MS) peptide mapping technique that has been proposed as a replacement for several conventional quality control (QC) methods for therapeutic proteins. In addition to quantification of multiple product quality attributes (PQAs), MAM can also monitor impurities using a new peak detection (NPD) feature. Here, results are provided from method validation and NPD studies of an MAM approach applied to rituximab as a model monoclonal antibody (mAb). Twenty-one rituximab PQAs were monitored, including oxidation, pyroglutamination, deamidation, lysine clipping, and glycosylation. The PQA monitoring aspect of the method was validated according to ICH Guidance. Accuracy, precision, specificity, detection and quantitation limits, linearity, range, and robustness were demonstrated for this MAM approach with minimal issues. All PQAs were successfully validated except for several oxidation sites, which did not pass intermediate precision criteria. The variability found in oxidation measurements was attributed to artificial oxidation during sample preparation and could likely be alleviated through several approaches. The NPD aspect of the method was also evaluated. A spike-in approach was used to assess the limits of detection and quantitation (LOD/LOQ) of the NPD feature of MAM. For NPD, the peak intensity threshold was found to be the most critical parameter for accurate detection of impurities since a low threshold can result in false positives while a high threshold can obscure the detection of true peaks. Overall, the MAM approach presented and validated here has been demonstrated to be suitable for both targeted monitoring of rituximab PQAs and non-targeted detection of new peaks that represent impurities.

LC-MS based multi-attribute methods (MAM) have drawn substantial attention due to their capability of simultaneously monitoring a large number of quality attributes of a biopharmaceutical product. For successful implementation of MAM, it is usually considered a requirement that the method is capable of detecting any new or missing peaks in the sample when compared to a control. Comparing a sample to a control for rare differences is also commonly practiced in many fields for investigational purpose. Because MS signal variability differs greatly between signals of different intensities, this type of comparison is often challenging, especially when the comparison is made without enough replicates. In this report we describe a statistical method for detecting rare differences between two very similar samples without replicate analyses. The method assumes that an overwhelming majority of components have equivalent abundance between the two samples, and signals with similar intensities have similar relative variability. By analyzing several monoclonal antibody peptide mapping datasets, we demonstrated that the method is suitable for new-peak detection for MAM as well as for other applications when rare differences between two samples need to be detected. The method greatly reduced false positive rate without a significant increase of false negative rate.

Due to the rise of adeno-associated viruses (AAVs) as gene therapy delivery vectors, boundary sedimentation velocity analytical ultracentrifugation (boundary SV-AUC) has been developed into a widely used quality control assay even for release analytics. It can be considered as the \"gold standard\" for the determination of the loading status of empty, partially filled, and full capsids especially when conducted in multiwavelength (MWL) mode. It can be considered to provide the most accurate determination of the loading status, and it also provides information on the capsid titer, aggregates, and potential contaminants such as free DNA. MWL boundary SV-AUC can be regarded as a multi-attribute (MAM) method for the characterization of AAVs. One major drawback of the method is the high sample consumption both in terms of concentration and volume. Here, we compare two alternative AUC techniques, band SV-AUC and analytical CsCl density gradient sedimentation equilibrium AUC (CsCl SE-AUC) with the boundary SV-AUC and the MWL-SV-AUC experiment. Our data show a high consistency of the determined full/empty ratios between these techniques if the appropriate wavelengths and extinction coefficients are used.

Multi-attribute methods employing mass spectrometry are applied throughout the biopharmaceutical industry for product and process characterization purposes but are not yet widely accepted as a method for batch release and stability testing under good manufacturing practice (GMP) due to limited experience and level of comfort with the technical, compliance and regulatory aspects of its implementation at quality control (QC) laboratories. Here, current literature related to the development and application of the multi-attribute method by peptide mapping liquid chromatography mass spectrometry (MAM) is compiled with the aim of providing guidance for the implementation of MAM in a QC laboratory. This article, focusing on technical considerations, is the first part of a two-tiered publication, whereby the second part will focus on GMP compliance and regulatory aspects. This publication has been prepared by a group of industry experts representing 14 globally acting major biotechnology companies under the umbrella of the European Federation of Pharmaceutical Industries and Associations (EFPIA) Manufacturing & Quality Expert Group (MQEG).

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.