An evaluation of instrument types for mass spectrometry-based multi-attribute analysis of biotherapeutics

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.

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.

Method for detecting rare differences between two LC-MS runs

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.

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).

Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning

Proteomics instrumentation and the corresponding bioinformatics tools have evolved at a rapid pace in the last 20 years, whereas the exploitation of deep learning techniques in proteomics is on the horizon. The ability to revisit proteomics raw data, in particular, could be a valuable resource for machine learning applications seeking new insight into protein expression and functions of previously acquired data from different instruments under various lab conditions. We map publicly available proteomics repositories (such as ProteomeXchange) and relevant publications to extract MS/MS data to form one large database that contains the patient history and mass spectrometric data acquired for the patient sample. The extracted mapped dataset should enable the research to overcome the issues attached to the dispersions of proteomics data on the internet, which makes it difficult to apply emerging new bioinformatics tools and deep learning algorithms. The workflow proposed in this study enables a linked large dataset of heart-related proteomics data, which could be easily and efficiently applied to machine learning and deep learning algorithms for futuristic predictions of heart diseases and modeling. Data scraping and crawling offer a powerful tool to harvest and prepare the training and test datasets; however, the authors advocate caution because of ethical and legal issues, as well as the need to ensure the quality and accuracy of the data that are being collected.

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.

A Robust Purity Method for Biotherapeutics Using New Peak Detection in an LC-MS-Based Multi-Attribute Method

New peak detection (NPD), as part of the LC-MS-based multi-attribute method (MAM), allows for sensitive and unbiased detection of new or changing site-specific attributes between a sample and reference that is not possible with conventional UV or fluorescence detection-based methods. MAM with NPD can serve as a purity test that can establish whether a sample and the reference are similar. The broad implementation of NPD in the biopharmaceutical industry has been limited by the potential presence of false positives or artifacts, which increase the analysis time and can trigger unnecessary investigations of product quality. Our novel contributions to the success of NPD are the curation of false positives, use of the known peak list concept, pairwise analysis approach, and the development of a NPD system suitability control strategy. In this report, we also introduce a unique experimental design utilizing sequence variant co-mixes to measure NPD performance. We show that NPD has superior performance relative to conventional control system methods in the detection of an unexpected change as compared with the reference. NPD is a new frontier in purity testing that reduces subjectivity, need for analyst intervention, and potential for missing unexpected product quality changes.

Engineering protein glycosylation in CHO cells to be highly similar to murine host cells

Since 2015 more than 34 biosimilars have been approved by the FDA. This new era of biosimilar competition has stimulated renewed technology development focused on therapeutic protein or biologic manufacturing. One challenge in biosimilar development is the genetic differences in the host cell lines used to manufacture the biologics. For example, many biologics approved between 1994 and 2011 were expressed in murine NS0 and SP2/0 cell lines. Chinese Hamster ovary (CHO) cells, however, have since become the preferred hosts for production due to their increased productivity, ease of use, and stability. Differences between murine and hamster glycosylation have been identified in biologics produced using murine and CHO cells. In the case of monoclonal antibodies (mAbs), glycan structure can significantly affect critical antibody effector function, binding activity, stability, efficacy, and in vivo half-life. In an attempt to leverage the intrinsic advantages of the CHO expression system and match the reference biologic murine glycosylation, we engineered a CHO cell expressing an antibody that was originally produced in a murine cell line to produce murine-like glycans. Specifically, we overexpressed cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) and N-acetyllactosaminide alpha-1,3-galactosyltransferase (GGTA) to obtain glycans with N-glycolylneuraminic acid (Neu5Gc) and galactose-α-1,3-galactose (alpha gal). The resulting CHO cells were shown to produce mAbs with murine glycans, and they were then analyzed by the spectrum of analytical methods typically used to demonstrate analytical similarity as a part of demonstrating biosimilarity. This included high-resolution mass spectrometry, biochemical, as well as cell-based assays. Through selection and optimization in fed-batch cultures, two CHO cell clones were identified with similar growth and productivity criteria to the original cell line. They maintained stable production for 65 population doubling times while matching the glycosylation profile and function of the reference product expressed in murine cells. This study demonstrates the feasibility of engineering CHO cells to express mAbs with murine glycans to facilitate the development of biosimilars that are highly similar to marketed reference products expressed in murine cells. Furthermore, this technology can potentially reduce the residual uncertainty regarding biosimilarity, resulting in a higher probability of regulatory approval and potentially reduced costs and time in development.

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.

Tracking the physicochemical stability of teduglutide (Revestive®) clinical solutions over time in different storage containers

Teduglutide, the active ingredient of the medicine Revestive® (5 mg), is a recombinant therapeutic peptide that mimics the effects of the endogenous glucagon-like peptide 2 (GLP-2). It stimulates intestinal growth, adaptation and function in patients with Short Bowel Syndrome who are dependent on parenteral nutrition. The Summary of Product Characteristics recommends immediate use of the reconstituted solutions and the discarding of any subsequent surplus. This study aims to carry out a long-term stability study that reproduces hospital conditions of use which provide sound evidence regarding the use of teduglutide surplus beyond the Summary Product Characteristics recommendations. We conducted a stability study of teduglutide solutions prepared from a 5 mg vial of Revestive®. Some of the solutions were stored in their original vial after reconstitution, while others were repackaged in plastic syringes to evaluate their physicochemical stability over time. For this purpose, we applied a set of previously validated analytical methodologies to evaluate the main critical quality attributes of teduglutide, i.e., primary (including post-tralational modifications), secondary and tertiary structures, aggregates, particulate, concentration and pH. The results indicate that the solutions maintain high physicochemical stability over time, regardless of the storage temperature (4ºC or -20ºC) or the storage container (vials or syringes). This research provides new data on the stability of Revestive® that will be of great value to hospital pharmacists. This comprehensive assessment of the physicochemical long-term stability of TGT has demonstrated that under the storage conditions and over the period studied here, the medicine maintains its quality, efficacy and safety profiles.

Interlaboratory Studies Using the NISTmAb to Advance Biopharmaceutical Structural Analytics

Biopharmaceuticals such as monoclonal antibodies are required to be rigorously characterized using a wide range of analytical methods. Various material properties must be characterized and well controlled to assure that clinically relevant features and critical quality attributes are maintained. A thorough understanding of analytical method performance metrics, particularly emerging methods designed to address measurement gaps, is required to assure methods are appropriate for their intended use in assuring drug safety, stability, and functional activity. To this end, a series of interlaboratory studies have been conducted using NISTmAb, a biopharmaceutical-representative and publicly available monoclonal antibody test material, to report on state-of-the-art method performance, harmonize best practices, and inform on potential gaps in the analytical measurement infrastructure. Reported here is a summary of the study designs, results, and future perspectives revealed from these interlaboratory studies which focused on primary structure, post-translational modifications, and higher order structure measurements currently employed during biopharmaceutical development.

New Aspects in the Integration of MS Technologies in the Biopharmaceutical Industry

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.

Development of a comprehensive approach for performance evaluation of a quantitative multi-attribute method as a quality control method

The multi-attribute method has been recognized as an elegant quantification tool for post-translational modifications (PTMs) of therapeutic proteins, since it can evaluate several attributes spontaneously and site-specifically. Here, the abundance of PTMs calculated by three different types of formula were compared and there was little difference among the results. For the method evaluation, two different kinds of peptides were used as internal standards (ISs) and one of the IS was used as the "standard peak" to define the signal strength of MS. They are also used for system suitability testing to verify whether the condition or sensitivity of mass spectrometry are high enough to evaluate the minor components by confirming the recovery rate of one IS to the another. This system is beneficial that since we have defined the limit of quantification as a certain ratio to IS, consistent MS intensity is applied as the threshold across all detected peaks.

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.

Enabling development, manufacturing, and regulatory approval of biotherapeutics through advances in mass spectrometry

Rapid technological advances have significantly improved the capability, versatility, and robustness of mass spectrometers which has led to them playing a central role in the development, characterization, and regulatory filings of biopharmaceuticals. Their application spans the entire continuum of drug development, starting with discovery research through product development, characterization, and marketing authorization and continues well into product life cycle management. The scope of application extends beyond traditional protein characterization and includes elements like clone selection, cell culture physiology and bioprocess optimization, investigation support, and process analytical technology. More recently, advances in the MS-based multi-attribute method are enabling the introduction of MS in a cGMP environment for routine release and stability testing. While most applications of MS to date have been for monoclonal antibodies, the successes and learnings should translate to the characterization of next-gen biotherapeutics where modalities like multispecifics could be more prevalent. In this review, we describe the most significant advances in MS and correlate them to the broad spectrum of applications to biotherapeutic development. We anticipate rapid technological improvements to continue that will further accelerate widespread deployment of MS, thereby elevating our overall understanding of product quality and enabling attribute-focused product development.

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.

New Peak Detection Performance Metrics from the MAM Consortium Interlaboratory Study

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.