Automated mass spectrometry multi-attribute method analyses for process development and characterization of mAbs

Implementation of a LC-MS based multi-attribute method (MAM) and intact multi-attribute method (iMAM) workflow for the characterisation of a GLP-Fc fusion protein

Over the past few years, the implementation of mass spectrometry (MS) in QC laboratories has become a more common occurrence. The multi-attribute method (MAM), and emerging intact multi-attribute method (iMAM), are powerful analytical tools utilising liquid chromatography-mass spectrometry (LC-MS) methods that enable the monitoring of critical quality attributes (CQAs) in biotherapeutic proteins in compliant settings. Both MAM and iMAM are intended to replace or supplement several conventional assays with a single LC-MS method utilising MS data in combination with robust, semi-automated data processing workflows. MAM and iMAM workflows can also be implemented into current Good Manufacturing Practices environments due to the availability of CFR 11 compliant chromatography data system software. In this study, MAM and iMAM are employed for the analysis of 4 batches of a glucagon-like peptide-Fc fusion protein. MAM approach involved a first the discovery phase for the identification of CQAs and second, the target monitoring phase of the selected CQAs in other samples. New peak detection was performed on the data set to determine the appearance, absence or change of any peak. For native iMAM workflow both size exclusion and strong cation exchange chromatography were optimized for the identification and monitoring of CQAs at the intact level.

Mitigating In-Column Artificial Modifications in High-Temperature LC-MS for Bottom-Up Proteomics and Quality Control of Protein Biopharmaceuticals

Elevating the column temperature is an effective strategy for improving the chromatographic separation of peptides. However, high temperatures induce artificial modifications that compromise the quality of the peptide analysis. Here, we present a novel high-temperature LC-MS method that retains the benefits of a high column temperature while significantly reducing peptide modification and degradation during reversed-phase liquid chromatography. Our approach leverages a short inline trap column maintained at a near-ambient temperature installed upstream of a separation column. The retentivity and dimensions of the trap column were optimized to shorten the residence time of peptides in the heated separation column without compromising the separation performance. This easy-to-implement approach increased peak capacity by 1.4-fold within a 110 min peptide mapping of trastuzumab and provided 10% more peptide identifications in exploratory LC-MS proteomic analyses compared with analyses conducted at 30 °C while maintaining the extent of modifications close to the background level. In the peptide mapping of biopharmaceuticals, where in-column modifications can falsely elevate the levels of some critical quality attributes, the method reduced temperature-related artifacts by 66% for N-terminal pyroGlu and 63% for oxidized Met compared to direct injection at 60 °C, thus improving reliability in quality control of protein drugs. Our findings represent a promising advancement in LC-MS methodology, providing researchers and industry professionals with a valuable tool for improving the chromatographic separation of peptides while significantly reducing the unwanted modifications.

The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning

Therapeutic antibodies such as monoclonal antibodies (mAbs), bispecific and multispecific antibodies are pivotal in therapeutic protein development and have transformed disease treatments across various therapeutic areas. The integrity of therapeutic antibodies, however, is compromised by sequence liabilities, notably deamidation, where asparagine (N) and glutamine (Q) residues undergo chemical degradations. Deamidation negatively impacts the efficacy, stability, and safety of diverse classes of antibodies, thus necessitating the critical need for the early and accurate identification of vulnerable sites. In this article, a comprehensive antibody deamidation-specific dataset (n = 2285) of varied modalities was created by using high-throughput automated peptide mapping followed by supervised machine learning to predict the deamidation propensities, as well as the extents, throughout the entire antibody sequences. We propose a novel chimeric deep learning model, integrating protein language model (pLM)-derived embeddings with local sequence information for enhanced deamidation predictions. Remarkably, this model requires only sequence inputs, eliminating the need for laborious feature engineering. Our approach demonstrates state-of-the-art performance, offering a streamlined workflow for high-throughput automated peptide mapping and deamidation prediction, with the potential of broader applicability to other antibody sequence liabilities.

Multi-dimensional technology - Recent advances and applications for biotherapeutic characterization

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.

Targeted CQA analytical control strategy for commercial antibody products: Replacing ion-exchange chromatography methods for charge heterogeneity with multi-attribute monitoring

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.

Multi-Attribute Method (MAM) Analytical Workflow for Biotherapeutic Protein Characterization from Process Development to QC

The multi-attribute method (MAM) has emerged significantly in recent years to support biotherapeutic protein characterization from process development to the QC environment. MAM is a liquid chromatography mass spectrometry (LC-MS) based peptide mapping approach, which combines the benefits from liquid chromatography coupled to high resolution accurate mass mass spectrometry (LC-HRAM MS), enabling direct assessment of protein sequence and product quality attributes with site specificity. These product quality attributes may impact efficacy, safety, stability, and process robustness. MAM is intended to replace conventional analytical approaches as it offers a more streamlined strategy for parallel monitoring of multiple attributes in a single analysis with high sensitivity and confidence, and ultimately supports more robust Quality by Design (QbD) approaches and faster decision cycles for biotherapeutic development. MAM consists of three main stages. The first stage is sample digestion, which typically entails proteolytic digestion of the protein. The second stage is reversed-phase chromatographic separation of the generated peptides and detection by HRAM MS in two phases. During MAM Phase I (discovery phase), data-dependent acquisition (DDA) MS/MS is performed to enable confident identification of peaks and development of a peptide workbook. During MAM Phase II (monitoring phase), full MS acquisition is only carried out for the monitoring of predefined product quality attributes (PQAs). The third stage is data processing, which entails analysis and reporting for each of the two phases including evaluation of sequence coverage, assessment of PQAs and peptide workbook creation during phase I, and targeted monitoring of predefined product attributes and new peak detection (NPD) during phase II. The latter is a comparative analysis that uses a base peak alignment algorithm to determine any non-monitored differences between the LC-MS chromatograms of a test sample and a reference standard. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: In-solution sample digestion Alternate Protocol: Automated sample digestion Basic Protocol 2: Reversed-phase chromatographic separation and detection by HRAM-MS (RPLC-HRAM MS) Basic Protocol 3: Data processing and reporting.

Fully Unattended Online Protein Digestion and LC-MS Peptide Mapping

LC-MS based peptide mapping, i.e., proteolytic digestion followed by LC-MS/MS analysis, is the method of choice for protein primary structural characterization. Manual proteolytic digestion is usually a labor-intensive procedure. In this work, a novel method was developed for fully automated online protein digestion and LC-MS peptide mapping. The method generates LC-MS data from undigested protein samples without user intervention by utilizing the same HPLC system that performs the chromatographic separation with some additional modules. Each sample is rapidly digested immediately prior to its LC-MS analysis, minimizing artifacts that can grow over longer digestion times or digest storage times as in manual or automated offline digestion methods. In this report, we implemented the method on an Agilent 1290 Infinity II LC system equipped with a Multisampler. The system performs a complete digestion workflow including denaturation, disulfide reduction, cysteine alkylation, buffer exchange, and tryptic digestion. We demonstrated that the system is capable of digesting monoclonal antibodies and other proteins with excellent efficiency and is robust and reproducible and produces fewer artifacts than manually prepared digests. In addition, it consumes only a few micrograms of material as most of the digested sample protein is subjected to LC-MS analysis.

Compliance and regulatory considerations for the implementation of the multi-attribute-method by mass spectrometry in a quality control laboratory

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).

Recent applications of quantitative mass spectrometry in biopharmaceutical process development and manufacturing

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.

A novel filter-assisted protein precipitation (FAPP) based sample pre-treatment method for LC-MS peptide mapping for biosimilar characterization

Establishing analytical and functional comparability serves as the foundation of biosimilar development. A critical part of this exercise is sequence similarity search and categorization of post-translational modifications (PTMs), often by peptide mapping using liquid chromatography-mass spectrometry (LC-MS). When performing bottom-up proteomic sample preparation, efficient digestion of the protein and extraction of peptides for subsequent mass spectrometric analysis can be a challenge. Conventional sample preparation strategies face the risk of allowing interference of chemicals which are essential for extraction but are likely to interfere with digestion, resulting in complex chromatographic profiles due to semi-cleavages, insufficient peptide cleavages, and other unwanted reactions. Further, peptide cleanup through commonly used immobilized C-18 pipette tips can cause significant peptide loss as well as variability in individual peptide yields, thereby causing artifacts of various product-related modifications. In this study, we proposed a simple enzymatic digestion technique by incorporating different molecular weight filters and protein precipitation, with the objective to minimize interference of denaturing, reducing, and alkylating agents throughout overnight digestion. As a result, the need for peptide cleanup is significantly reduced and results in higher peptide yield. The proposed FAPP approach outperformed the conventional method across multiple metrics including, 30% more peptides, 8.19% more fully digested peptides, 14% higher sequence coverage rate, and 11.82% more site-specific alterations. Quantitative and qualitative repeatability of the proposed approach have been demonstrated. It can be concluded that the filter-assisted protein precipitation (FAPP) protocol proposed in this study offers an effective substitute for the traditional approach.

Expanding the Analytical Toolbox: Developing New Lys-C Peptide Mapping Methods with Minimized Assay-Induced Artifacts to Fully Characterize Antibodies

Peptide mapping is an important tool used to confirm that the correct sequence has been expressed for a protein and to evaluate protein post-translational modifications (PTMs) that may arise during the production, processing, or storage of protein drugs. Our new orally administered drug (Ab-1), a single-domain antibody, is highly stable and resistant to proteolysis. Analysis via the commonly used tryptic mapping method did not generate sufficient sequence coverage. Alternative methods were needed to study the Ab-1 drug substance (75 mg/mL) and drug product (3 mg/mL). To meet these analytical needs, we developed two new peptide mapping methods using lysyl endopeptidase (Lys-C) digestion. These newly developed protein digestion protocols do not require desalting/buffer-exchange steps, thereby reducing sample preparation time and improving method robustness. Additionally, the protein digestion is performed under neutral pH with methionine acting as a scavenger to minimize artifacts, such as deamidation and oxidation, which are induced during sample preparation. Further, the method for low-concentration samples performs comparably to the method for high-concentration samples. Both methods provide 100% sequence coverage for Ab-1, and, therefore, enable comprehensive characterization for its product quality attribute (PQA) assessment. Both methods can be used to study other antibody formats.

Multiattribute Monitoring of Aggregates and Charge Variants of Monoclonal Antibody through Native 2D-SEC-MS-WCX-MS

Monitoring of critical quality attributes such as size and charge-related heterogeneities is essential for biopharmaceutical manufacturers. Size-exclusion chromatography (SEC) is the preferred analytical technique for the quantification of aggregates and fragments in the product, whereas weak-cation exchange chromatography (WCX) is widely used for the characterization of charge variants of biotherapeutic products, in particular monoclonal antibodies (mAbs). Multiattribute monitoring offers the ability to monitor these attributes in a single run flow using two-dimensional liquid chromatography (2D-LC). Typically, in this approach, only the second-dimension samples are directly analyzed through mass spectrometry, as the first dimension has limitations concerning direct coupling with mass spectrometry. In the present study, a novel 2D-SEC-MS/WCX-MS workflow has been proposed, in which chromatography of both dimensions (D¹ and D²) was directly coupled with mass spectrometry, through which size-related and charge-related variants of monoclonal antibody mAb A were analyzed simultaneously in their native form. In comparison to stand-alone SEC and WCX methods, this method enables simultaneous analysis of size and charge variants in a single workflow without manual intervention, allowing analysis of low abundant variants. Further, this method has 75% less sample requirement and a shorter analysis time (25 min vs 90 min) when size and charge variants were analyzed individually. The proposed native 2D-LC-MS workflow was used to analyze a stressed sample of mAb A, in which D¹ analysis revealed the presence of aggregates (8-20%), which were primarily dimers, whereas D² analysis showed an increment in acidic variants (9-21%).

Technical considerations for the implementation of the Multi-Attribute-Method by mass spectrometry in a Quality Control laboratory

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).

A native multi-dimensional monitoring workflow for at-line characterization of mAb titer, size, charge, and glycoform heterogeneities in cell culture supernatant

With growing maturity of the biopharmaceutical industry, new modalities entering the therapeutic design space and increasing complexity of formulations such as combination therapy, the demands and requirements on analytical workflows have also increased. A recent evolution in newer analytical workflows is that of multi-attribute monitoring workflows designed on chromatography-mass spectrometry (LC-MS) platform. In comparison to traditional one attribute per workflow paradigm, multi-attribute workflows are designed to monitor multiple critical quality attributes through a single workflow, thus reducing the overall time to information and increasing efficiency and throughput. While the 1^st generation multi-attribute workflows focused on bottom-up characterization following peptide digestion, the more recent workflows have been focussing on characterization of intact biologics, preferably in native state. So far intact multi-attribute monitoring workflows suitable for comparability, utilizing single dimension chromatography coupled with MS have been published. In this study, we describe a native multi-dimensional multi-attribute monitoring workflow for at-line characterization of monoclonal antibody (mAb) titer, size, charge, and glycoform heterogeneities directly in cell culture supernatant. This has been achieved through coupling ProA in series with size exclusion chromatography in 1^st dimension followed by cation exchange chromatography in the 2^nd dimension. Intact paired glycoform characterization has been achieved through coupling 2D-LC with q-ToF-MS. The workflow with a single heart cut can be completed in 25 mins and utilizes 2D-liquid chromatography (2D-LC) to maximize separation and monitoring of titer, size as well as charge variants.

Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing

The multi-attribute method (MAM) is a liquid chromatography-mass spectrometry (LC-MS)-based method that is used to directly characterize and monitor numerous product quality attributes (PQAs) at the amino acid level of a biopharmaceutical product. MAM enables identity testing based on primary sequence verification, detection and quantitation of post-translational modifications and impurities. This ability to simultaneously and directly determine PQAs of therapeutic proteins makes MAM a more informative, streamlined and productive workflow than conventional chromatographic and electrophoretic assays. MAM relies on proteolytic digestion of the sample followed by reversed-phase chromatographic separation and high-resolution LC-MS analysis in two phases. First, a discovery study to determine quality attributes for monitoring is followed by the creation of a targeted library based on high-resolution retention time plus accurate mass analysis. The second aspect of MAM is the monitoring phase based on the target peptide library and new peak detection using differential analysis of the data to determine the presence, absence or change of any species that might affect the activity or stability of the biotherapeutic. The sample preparation process takes between 90 and 120 min, whereas the time spent on instrumental and data analyses might vary from one to several days for different sample sizes, depending on the complexity of the molecule, the number of attributes to be monitored and the information to be detailed in the final report. MAM is developed to be used throughout the product life cycle, from process development through upstream and downstream processes to quality control release or under current good manufacturing practices regulations enforced by regulatory agencies.

Analytical Performance Evaluation of Identity, Quality-Attribute Monitoring and new Peak Detection in a Platform Multi-Attribute Method Using Lys-C Digestion for Characterization and Quality Control of Therapeutic Monoclonal Antibodies

The use of multi-attribute method (MAM) for identity and purity testing of biopharmaceuticals offers the ability to complement and replace multiple conventional analytical technologies with a single mass spectrometry (MS) method. Phase-appropriate method validation is one major consideration for the implementation of MAM in a current Good Manufacturing Practice (cGMP) environment. We developed a MAM workflow for therapeutic monoclonal antibodies (mAbs) with optimized sample preparation using lysyl endopeptidase (Lys-C) digestion. In this study, we evaluated the assay performances of this platform MAM workflow for identity, product quality attributes (PQAs) monitoring and new peak detection (NPD) for single and coformulated mAbs. An IgG4 mAb-1 and its coformulations were used as model molecules in this study. The assay performance evaluation demonstrated the full potential of the platform MAM approach for its intended use for characterization and quality control of single mAb-1 and mAb-1 in its coformulations. To the best of our knowledge, this is the first performance evaluation of MAM for mAb identity, PQA monitoring, and new peak detection (NPD) in a single assay, featuring 1) the first performance evaluation of MAM for PQA monitoring using Lys-C digestion with a high-resolution MS, 2) a new approach for mAb identity testing capable of distinguishing single mAb from coformulations using MAM, and 3) the performance evaluation of NPD for MAM with Lys-C digestion. The developed platform MAM workflow and the MAM performance evaluation paved the way for its GMP qualification and enabled clinical release of mAb-1 in GMP environment with MAM.

Development and optimization of a LC-MS based multi-attribute method (MAM) workflow for characterization of therapeutic Fc-fusion protein

The growing complexity of novel biopharmaceutical formats, such as Fc-fusion proteins, in increasingly competitive environment has highlighted the need of high-throughput analytical platforms. Multi-attribute method (MAM) is an emerging analytical technology that utilizes liquid chromatography coupled with mass spectrometry to monitor critical quality attributes (CQAs) in biopharmaceuticals. MAM is intended to supplement or replace the conventional chromatographic and electrophoretic approaches used for quality control and drug release purpose. In this investigation, we have developed an agile sample preparation approach for deploying MAM workflow for a complex VEGFR-targeted therapeutic Fc-fusion protein. Initially, a systematic time course evaluation of tryptic digestion step was performed to achieve maximum amino acid sequence coverage of >96.5%, in a short duration of 2 h, with minimum assay artifacts. This approach facilitated precise identification of five sites of N-glycosylation with successful monitoring of other CQAs such as deamidation, oxidation, etc. Subsequently, the developed MAM workflow with suitable tryptic digestion time was qualified according to the International council for harmonisation (i.e. ICH) Q2R1 guidelines for method validation. Post-validation, the analytical workflow was also evaluated for its capability to identify unknown moieties, termed as 'New Peak Detection' (i.e. NPD), and assess fold change between the reference and non-reference samples, in a representative investigation of pH stress study. The study, thus, demonstrated the suitability of the MAM workflow for characterization of heavily glycosylated Fc-fusion proteins. Moreover, its NPD feature could offer an all-encompassing view if applied for forced degradation and stability studies.

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.

State-of-the-art and emerging trends in analytical approaches to pharmaceutical-product commercialization

The biopharmaceutical landscape continues to evolve rapidly, and associated modality complexity and the need to improve molecular understanding require concomitant advances in analytical approaches used to characterize and release the product. The Product Quality Attribute Assessment (PQAA) and Quality Target Product Profile (QTPP) frameworks help catalog and translate molecular understanding to process and product-design targets, thereby enabling reliable manufacturing of high-quality product. The analytical target profile forms the basis of identifying best-fit analytical methods for attribute measurement and continues to be successfully used to develop robust analytical methods for detailed product characterization as well as release and stability testing. Despite maturity across multiple testing platforms, advances continue to be made, several with the potential to alter testing paradigms. There is an increasing role for mass spectrometry beyond product characterization and into routine release testing as seen by the progress in multi-attribute methods and technologies, applications to aggregate measurement, the development of capillary zone electrophoresis (CZE) coupled with mass spectrometry (MS) and capillary isoelectric focusing (CIEF) with MS for measurement of glycans and charged species, respectively, and increased application to host cell protein measurement. Multitarget engaging multispecific modalities will drive advances in bioassay platforms and recent advances both in 1- and 2-D NMR approaches could make it the method of choice for characterizing higher-order structures. Additionally, rigorous understanding of raw material and container attributes is necessary to complement product understanding, and these collectively can enable robust supply of high-quality product to patients.

Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study

The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment.

[Quality Evaluation of Therapeutic Antibodies by Multi-attribute Method]

In the development of therapeutic monoclonal antibodies (mAbs), it is essential to characterize the modifications causing structural heterogeneity because certain modifications are associated with safety and efficacy. However, the rapid structural analysis of mAbs remains challenging due to their structural complexity. The multi-attribute method (MAM) is a structural analytical method based on peptide mapping using LC/MS, and has drawn attention as a new quality control method for therapeutic mAbs instead of conventional structural heterogeneity analyses using several chromatographic techniques. Peptide mapping, which is regarded as an identification test method, is used to confirm that the amino acid sequence corresponds to that deduced from the gene sequence for the desired product. In contrast, MAM is used for simultaneously monitoring the modification rates of individual amino acid residues of therapeutic mAbs, indicating that MAM is used as quantitative test rather than identification test. In this review, we summarized the typical structural heterogeneities of mAbs and the general scheme of MAM. We also introduced our optimized sample preparation method for MAM, and examples of simultaneous monitoring of several modifications including deamidation, oxidation, N-terminal pyroglutamination, C-terminal clipping and glycosylation by our MAM system.

Analytical Applications of Immobilized Enzyme Reactors (IMERs) Coupled to LC–MS/MS for Bottom- and Middle-Up Characterization of Proteins

Identification, monitoring, and, more importantly, linkage of critical quality attributes (CQAs) in processing parameters in a biopharmaceutical product is required to ensure the quality and manufacturing consistency of the product, but also its safety and efficacy during clinical and commercial development. Recently, bottom-up and middle-up liquid chromatography–mass spectrometry (LC–MS) characterization strategies using immobilized enzyme reactors (IMERs) in combination with multidimensional liquid chromatography coupled with high-resolution MS (MDLC–HRMS), as well as sophisticated software solutions, have been added to the analytical toolbox. These strategies not only allow faster characterization of post-translational modifications (PTMs) present in biotherapeutic proteins but also have the potential to provide a fully automated and unified bottom-up, middle-up, and intact LC–MS characterization approach.

Analytical Similarity Assessment of Biosimilars: Global Regulatory Landscape, Recent Studies and Major Advancements in Orthogonal Platforms

Biopharmaceuticals are one of the fastest-growing sectors in the biotechnology industry. Within the umbrella of biopharmaceuticals, the biosimilar segment is expanding with currently over 200 approved biosimilars, globally. The key step towards achieving a successful biosimilar approval is to establish analytical and clinical biosimilarity with the innovator. The objective of an analytical biosimilarity study is to demonstrate a highly similar profile with respect to variations in critical quality attributes (CQAs) of the biosimilar product, and these variations must lie within the range set by the innovator. This comprises a detailed comparative structural and functional characterization using appropriate, validated analytical methods to fingerprint the molecule and helps reduce the economic burden towards regulatory requirement of extensive preclinical/clinical similarity data, thus making biotechnological drugs more affordable. In the last decade, biosimilar manufacturing and associated regulations have become more established, leading to numerous approvals. Biosimilarity assessment exercises conducted towards approval are also published more frequently in the public domain. Consequently, some technical advancements in analytical sciences have also percolated to applications in analytical biosimilarity assessment. Keeping this in mind, this review aims at providing a holistic view of progresses in biosimilar analysis and approval. In this review, we have summarized the major developments in the global regulatory landscape with respect to biosimilar approvals and also catalogued biosimilarity assessment studies for recombinant DNA products available in the public domain. We have also covered recent advancements in analytical methods, orthogonal techniques, and platforms for biosimilar characterization, since 2015. The review specifically aims to serve as a comprehensive catalog for published biosimilarity assessment studies with details on analytical platform used and critical quality attributes (CQAs) covered for multiple biotherapeutic products. Through this compilation, the emergent evolution of techniques with respect to each CQA has also been charted and discussed. Lastly, the information resource of published biosimilarity assessment studies, created during literature search is anticipated to serve as a helpful reference for biopharmaceutical scientists and biosimilar developers.

Automated multi-attribute method sample preparation using high-throughput buffer exchange tips

RATIONALE

The multi-attribute method (MAM) has become a valuable mass spectrometry (MS)-based tool that can identify and quantify the site-specific product attributes and purity information for biotherapeutics such as monoclonal antibodies (mAbs) and fusion molecules in recent years. As we expand the use of the MAM at various stages of drug development, it is critical to enhance the sample preparation throughput without additional chemical modifications and variability.

METHODS

In this study, a fully automated MAM sample preparation protocol is presented, where rapid desalting in less than 15 minutes is achieved using miniaturized size-exclusion chromatography columns in pipette tips on an automated liquid handler. The peptide samples were analyzed using an electrospray ionization (ESI) orbitrap mass spectrometer coupled to an ultra-high-performance liquid chromatography (UHPLC) system with a dual column switching system.

RESULTS

No significant change was observed in product attributes and their quantities compared with manual, low-artifact sample preparation. The sample recovery using the buffer exchange tips was greatly enhanced over the manual spin cartridges while still demonstrating excellent reproducibility for a wide variety of starting sample concentrations. Unlike a plate desalting system, the individual columns provide flexibility in the number of samples prepared at a time and sample locations within plates.

CONCLUSIONS

This automated protocol enables the preparation of up to 96 samples with less "at-bench" time than the manual preparation of a smaller batch of samples, thereby greatly facilitating support of process development and the use of the MAM in quality control.

Multi attribute method implementation using a High Resolution Mass Spectrometry platform: From sample preparation to batch analysis

Quality control of biopharmaceuticals such as monoclonal antibodies (mAbs) has been evolving and becoming more challenging as the requirements of the regulatory agencies increase due to the demanding complexity of products under evaluation. Mass Spectrometry (MS)-based methods such as the multi-attribute method (MAM) are being explored to achieve a deeper understanding of the attributes critical for the safety, efficacy, and quality of these products. MAM uses high mass accuracy/high-resolution MS data that enables the direct and simultaneous monitoring of relevant product quality attributes (PQAs, in particular, chemical modifications) in a single workflow, replacing several orthogonal methods, reducing time and costs associated with these assays. Here we describe a MAM implementation process using a QTOF high resolution platform. Method implementation was accomplished using NIST (National Institute for Standards and Technology) mAb reference material and an in-process mAb sample. PQAs as glycosylation profiles, methionine oxidation, tryptophan dioxidation, asparagine deamidation, pyro-Glu at N-terminal and glycation were monitored. Focusing on applications that require batch analysis and high-throughput, sample preparation and LC-MS parameters troubleshooting are discussed. This MAM workflow was successfully explored as reference analytical tool for comprehensive characterization of a downstream processing (DSP) polishing platform and for a comparability study following technology transfer between different laboratories.

Multi-attribute method performance profile for quality control of monoclonal antibody therapeutics

Multi-attribute method (MAM) using peptide map analysis with high resolution mass spectrometry is increasingly common in product characterization and the identification of critical quality attributes (CQAs) of biotherapeutic proteins. Capable of providing structural information specific to amino acid residues, quantifying relative abundance of product variants or degradants, and detecting profile changes between product lots, a robust MAM can replace multiple traditional methods that generate profile-based information for product release and stability testing. In an effort to provide informative and efficient analytical monitoring for monoclonal antibody (mAb) products, from early development to manufacturing quality control, we describe the desired MAM performance profile and address the major scientific challenges in MAM method validation. Furthermore, to support fast speed investigational product development, we describe a platform method validation strategy and results of an optimized MAM workflow. This strategy is applied to support the use of MAM for multiple mAb products with similar structures and physicochemical properties, requiring minimal product-specific method validation activities. Three mAb products were used to demonstrate MAM performance for common and representative product quality attributes. Method validation design and acceptance criteria were guided by the Analytical Target Profile concept, as well as relevant regulatory guidelines to ensure the method is fit-for-purpose. A comprehensive system suitability control strategy was developed, and reported here, to ensure adequate performance of the method including sample preparation, instrument operation, and data analysis. Our results demonstrated sufficient method performance for the characteristics required for quantitative measurement of product variants and degradants.

A Fully Integrated Online Platform For Real Time Monitoring Of Multiple Product Quality Attributes In Biopharmaceutical Processes For Monoclonal Antibody Therapeutics

In response to FDA's call for Quality by Design (QbD) in biopharmaceutical product development, the biopharmaceutical industry has been developing highly sensitive and specific technologies in the monitoring and controlling of product quality attributes for bioprocesses. We previously published the successful application of an off-line multi-attribute method (MAM) to monitor more than 20 critical quality attributes (CQA) with superior sensitivity for the upstream process. To further remove the hurdles of laborious process sampling and sample preparation associated with the offline method, we present here a fully integrated MAM based online platform for automated real time online process monitoring. This integrated system includes Modular Automated Sampling Technology (MAST) based aseptic sampling, multi-function Sequential Injection Analysis (SIA) sample preparation, UHPLC separation and high-resolution mass spectrometry (HRMS) analysis. Continuous automated daily monitoring of a 17-day cell culture process was successfully demonstrated for a model monoclonal antibody (mAb) molecule with similar specificity and sensitivity as we reported earlier. To the best of our knowledge, this is the first report of an end-to-end automated online MAM system, which would allow the MAM to be applied to routine bioprocess monitoring, potentially replacing multiple conventional low resolution and low sensitivity off-line methods. The online HPLC or HPLC/MS platform could be easily adapted to support other processing steps such as downstream purification with minimal software re-configuration.

ID-MAM: A Validated Identity and Multi-Attribute Monitoring Method for Commercial Release and Stability Testing of a Bispecific Antibody

Post-translational modifications (PTMs) that impact the safety or efficacy of protein therapeutics are critical quality attributes (CQAs) that need to be controlled to ensure product quality. Peptide mapping with online mass spectrometry (MS) is a powerful tool that has been used for many years to monitor PTM CQAs during product development. However, operating peptide mapping methods with high-resolution mass spectrometers in GMP compliant, commercial quality control (QC) labs can be difficult. Peptide mapping is also required as an identity test in several countries. To address these two different needs, we utilized high-resolution peptide mapping for comprehensive characterization during development and then developed and validated a targeted multi-attribute monitoring (MAM) method using the low-resolution Waters QDa MS system with a fully automated data processing workflow that is suitable for identity (ID) testing, sequence variant control, and CQA quantitation in commercial QC labs. The ID-MAM method was validated for the quantitation of three selected PTM CQAs (CDR isomerization, Fc Met oxidation, and CDR Met oxidation) to ensure control of the oxidation and isomerization degradation pathways of a bispecific antibody (BsAb). This ID-MAM method was successfully validated in six labs (three analytical development and three QC labs) across four countries for commercial release and stability testing of a BsAb. CQA results obtained with the ID-MAM method were similar to results obtained using high-resolution peptide mapping, and the method was robust and reproducible. To our knowledge, this ID-MAM method is the first MS-based peptide mapping method implemented in GMP compliant QC labs for commercial release and stability testing of a biotherapeutic.

Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of Monoclonal Antibody Biologics

The glycosylation of antibody-based proteins is vital in translating the right therapeutic outcomes of the patient. Despite this, significant infrastructure is required to analyse biologic glycosylation in various unit operations from biologic development, process development to QA/QC in bio-manufacturing. Simplified mass spectrometers offer ease of operation as well as the portability of method development across various operations. Furthermore, data analysis would need to have a degree of automation to relay information back to the manufacturing line. We set out to investigate the applicability of using a semiautomated data analysis workflow to investigate glycosylation in different biologic development test cases. The workflow involves data acquisition using a BioAccord LC-MS system with a data-analytical tool called GlycopeptideGraphMS along with Progenesis QI to semi-automate glycoproteomic characterisation and quantitation with a LC-MS1 dataset of a glycopeptides and peptides. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 min with minimal user intervention. To demonstrate the effectiveness of the antibody and biologic glycopeptide assignment in various scenarios akin to biologic development activities, we demonstrate the effectiveness in the filtering of IgG1 and IgG2 subclasses from human serum IgG as well as innovator drugs trastuzumab and adalimumab and glycoforms by virtue of their glycosylation pattern. We demonstrate a high correlation between conventional released glycan analysis with fluorescent tagging and glycopeptide assignment derived from GraphMS. GraphMS workflow was then used to monitor the glycoform of our in-house trastuzumab biosimilar produced in fed-batch cultures. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.