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

Song, Yvonne; Dubois, H.; Hoffmann, M.; D́Eri, Stephen; Fromentin, Yann; Wiesner, Jan; Pfenninger, Anja; Clavier, Séverine; Pieper, Annette; Duhau, Laurent; Roth, Udo

doi:10.1016/j.jchromb.2021.122540

Cited by 38 publications

(34 citation statements)

References 46 publications

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“…This is highly relevant since we used LC gradients of approximately 60 min length, meaning that when a high number of samples need to be analysed, the last ones in the sample queue will stay at 4°C for a significant amount of time. Although we did follow all the PQAs reported in Tables 3 and 4 , oxidation levels were the ones where this factor showed higher impact, with several values above the 5% threshold ( Fig 2A ) , probably because these PQA levels are reported as highly susceptible to artificial modifications [ 22 , 31 , 32 ]. Indeed, mAb S oxidation (and deamidation) levels are highly affected by the time the sample is waiting in the auto-sampler, presenting different fold changes depending on the amino acid where the modification is found ( Fig 2 ).…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, MAM has also the ability to search for sequence variants, critical for mAb biological function [ 11 , 20 , 21 ]. Therefore, by streamlining monitoring and quality control analysis, MAM has the potential to accelerate mAbs’ process development and manufacturing, contributing to reduce the risk of failure and costs associated with analytical characterization during bioprocess development, product release, and in-process control [ 6 , 9 , 22 ]. MAM has been increasingly used for routine PQA analysis [ 18 ] keeping up with the growth and increased acceptance of MS methods in the biopharmaceutical field.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, according to a recent US Food and Drug Administration study, most of the biotherapeutics (with license applications approved between 2000 and 2015) use MS methods for their characterization. In line with this, recently, Song et al published the development and implementation of a MAM platform focused on global harmonization and intersite comparability [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…The complexity of LC-MS systems for glycopeptide analysis has necessitated the use of simplified mass spectrometers and analytical workflows capable of performing analyses at intact subunit, peptide and released glycan level with optimised compliance-ready workflows (Rogers et al, 2018). These workflows are known as multi-attribute monitoring (MAM) and they can be performed with mass spectrometers and have been used to identify biotherapeutic attributes such as deamidation, oxidation, pyroglutamate formation, lysine clipping, aspartate isomerization and glycosylation in one analysis (Song et al, 2021). In most instances, the accurate mass of the peptides and PTMs are compared and validated by cross-referencing these measurements to a curated database.…”

Section: Introductionmentioning

confidence: 99%

“…In most instances, the accurate mass of the peptides and PTMs are compared and validated by cross-referencing these measurements to a curated database. MAM is being increasingly adopted within the biopharmaceutical industry because in principle the analytical workflows which include sample preparation and data processing techniques and instrumentation are robust enough to be harmonized across the biologic development value chain and across different manufacturing sites (Song et al, 2021). This harmonization decreases the cost of assay development; however, there is the potential challenge that these techniques can miss unexpected PTMs such as glycosylation which may not be described in the analytical databases.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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

Wasalathanthri

Shah

Ding

et al. 2021

Biotechnol Progress

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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 highthroughput 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.

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

Cited by 38 publications

References 46 publications

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

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

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

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

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