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Host cell proteins (HCPs) are process‐related impurities derived from host organisms, which need to be controlled to ensure adequate product quality and safety. In this study, product quality attributes were tracked for several monoclonal antibodies (mAbs) under the intended storage and accelerated stability conditions. One product quality attribute not expected to be stability indicating is the N‐glycan heterogeneity profile. However, significant N‐glycan degradation was observed for one mAb under accelerated and stressed stability conditions. The root cause for this instability was attributed to hexosaminidase B (HEXB), an enzyme known to remove terminal N‐acetylglucosamine (GlcNAc). HEXB was identified by liquid chromatography–mass spectrometry (LC–MS)‐based proteomics approach to be enriched in the impacted stability batches from mAb‐1. Subsequently, enzymatic and targeted multiple reaction monitoring (MRM) MS assays were developed to support process and product characterization. A potential interaction between HEXB and mAb‐1 was initially observed from the analysis of process intermediates by proteomics among several mAbs and later supported by computational modeling. An improved bioprocess was developed to significantly reduce HEXB levels in the final drug substance. A risk assessment was conducted by evaluating the in silico immunogenicity risk and the impact on product quality. To the best of our knowledge, HEXB is the first residual HCP reported to have impact on the glycan profile of a formulated drug product. The combination of different analytical tools, mass spectrometry, and computational modeling provides a general strategy on how to study residual HCP for biotherapeutics development.

The mass spectrometry based multi-attribute method (MAM) has gained popularity in the field of biopharmaceutical analysis as it promises a single method for comprehensive monitoring of multiple product quality attributes (PQAs) and product purity. Sample preparation for protein digestion and peptide separation are critical considerations for a reduced peptide mapping-based MAM. To avoid desalting steps required in most tryptic protein digestion and in order to improve peptide separation for hydrophilic peptides, we developed an improved robust sample preparation using lysyl endopeptidase (Lys-C) for high-throughput MAM testing. Additionally, this method optimizes the peptide retention and separation of a stability-indicating VSNK peptide using a HSS T3 column for comprehensive PQA monitoring. A fully automated sample preparation had similar assay variations for PQAs monitoring compared to manual sample preparation. To the best of our knowledge, this is the first report of a high-resolution MS-based MAM using a streamlined Lys-C digestion without desalting with enhanced PQA monitoring for hydrophilic peptides. The improved, robust MAM workflow for protein digestion and peptide separation will pave the way for broader MAM qualification and its applications for the characterization and quality control of therapeutic monoclonal antibodies.

Polysorbate is widely used to maintain stability of biotherapeutic proteins in formulation development. Degradation of polysorbate can lead to particle formation in drug products, which is a major quality concern and potential patient risk factor. Enzymatic activity from residual enzymes such as lipases and esterases can cause polysorbate degradation. Their high activity, even at low concentration, constitutes a major analytical challenge. In this study, we evaluated and optimized the activity-based protein profiling (ABPP) approach to identify active enzymes responsible for polysorbate degradation. Using chemical probes to enrich active serine hydrolases, more than 80 proteins were identified in harvested cell culture fluid (HCCF) from monoclonal antibodies (mAb) production. A total of 8 known lipases were identified by ABPP, while only 5 lipases were identified by a traditional abundance-based proteomics (TABP) approach. Interestingly, phospholipase B-like 2 (PLBL2), a well-known problematic HCP was not found to be active in process-intermediates from two different mAbs. In a proof-of-concept study, phospholipase A2 group VII (PLA2G7) and sialic acid acetylesterase (SIAE) were identified by ABPP as possible root causes of polysorbate-80 degradation. The established ABBP approach can fill the gap between lipase abundance and activity, which enables more meaningful polysorbate degradation investigations for biotherapeutic development.

Host cell proteins (HCPs) are process-related impurities derived from host organisms, which need to be controlled to ensure adequate product quality and safety. In this study, product quality attributes were tracked for several mAbs under the intended storage and accelerated stability conditions. One product quality attribute not expected to be stability indicating is the N-glycan heterogeneity profile. However, significant N-glycan degradation was observed for one mAbs under accelerated and stressed stability conditions. The root cause for this instability was attributed to Hexosaminidase B (HEXB), an enzyme known to remove terminal N-acetylglucosamine (GlcNAc). HEXB was identified by liquid chromatography–mass spectrometry (LC-MS) based proteomics approach to be enriched in the impacted stability batches from mAb-1. Subsequently, enzymatic and targeted multiple reaction monitoring (MRM) MS assays were developed to support process and product characterization. A potential interaction between HEXB and mAb-1 was initially observed from the analysis of process intermediates by proteomics among several mAbs and later supported by computational modeling. An improved bioprocess was developed to significantly reduce HEXB levels in the final drug substance. A risk assessment was conducted by evaluating the in silico immunogenicity risk and the impact on product quality. To the best of our knowledge, HEXB is the first residual HCP reported to have impact on the glycan profile of a formulated drug product. The combination of different analytical tools, mass spectrometry and computational modeling provide a general strategy on how to study residual HCP for biotherapeutics development.

C.1

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300–1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.

Background Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. Results We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. Conclusion The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.