1
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Evans AR, Mulholland J, Lewis MJ, Hu P. Targeted CQA analytical control strategy for commercial antibody products: Replacing ion-exchange chromatography methods for charge heterogeneity with multi-attribute monitoring. MAbs 2024; 16:2341641. [PMID: 38652517 DOI: 10.1080/19420862.2024.2341641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
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.
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Affiliation(s)
- Adam R Evans
- Therapeutics Development & Supply - Analytical Development, Janssen Pharmaceuticals Research and Development, Malvern, PA, USA
| | - Joseph Mulholland
- Therapeutics Development & Supply - Analytical Development, Janssen Pharmaceuticals Research and Development, Malvern, PA, USA
| | - Michael J Lewis
- Therapeutics Development & Supply - Analytical Development, Janssen Pharmaceuticals Research and Development, Malvern, PA, USA
| | - Ping Hu
- Therapeutics Development & Supply - Analytical Development, Janssen Pharmaceuticals Research and Development, Malvern, PA, USA
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2
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Millán-Martín S, Jakes C, Carillo S, Bones J. Multi-Attribute Method (MAM) Analytical Workflow for Biotherapeutic Protein Characterization from Process Development to QC. Curr Protoc 2023; 3:e927. [PMID: 37929772 DOI: 10.1002/cpz1.927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
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.
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Affiliation(s)
| | - Craig Jakes
- National Institute for Bioprocessing Research and Training, Dublin, Ireland
| | - Sara Carillo
- National Institute for Bioprocessing Research and Training, Dublin, Ireland
| | - Jonathan Bones
- National Institute for Bioprocessing Research and Training, Dublin, Ireland
- School of Chemical and Bioprocess Engineering, University College Dublin, Dublin, Ireland
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3
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Gervais A, Dirksen EHC, Pohl T, Bechtold-Peters K, Burkitt W, D'Alessio V, Greven S, Lennard A, Li X, Lössner C, Niu B, Reusch D, O'Riordan T, Shearer JW, Spencer D, Xu W, Yi L. Compliance and regulatory considerations for the implementation of the multi-attribute-method by mass spectrometry in a quality control laboratory. Eur J Pharm Biopharm 2023; 191:57-67. [PMID: 37582411 DOI: 10.1016/j.ejpb.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/04/2023] [Accepted: 08/12/2023] [Indexed: 08/17/2023]
Abstract
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).
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Affiliation(s)
- Annick Gervais
- Analytical Development Sciences for Biologicals, UCB, Chemin du Foriest, 1420 Braine L'Alleud, Belgium.
| | - Eef H C Dirksen
- Analytical Development and Quality Control, Byondis, Microweg 22, 6545 CM, Nijmegen, the Netherlands
| | - Thomas Pohl
- Biologics Analytical Development, Novartis Pharma AG, Klybeckstrasse 141, CH-4057 Basel, Switzerland
| | - Karoline Bechtold-Peters
- Biologics Drug Product Development, Novartis Pharma AG, Klybeckstrasse 141, CH-4057 Basel, Switzerland
| | - Will Burkitt
- Biological Characterisation Product Development Sciences, UCB, 216 Bath Road, Slough SL1 3WE, UK
| | - Valerio D'Alessio
- Analytical Development & Innovation NBE, Merck Serono S.p.A, Via Luigi Einaudi, 11, 00012 Guidonia Montecelio - Rome, Italy
| | - Simone Greven
- Pharmaceuticals, Biological Development, Bayer AG, Friedrich-Ebert-Strasse 217-333, 42117 Wuppertal, Germany
| | - Andrew Lennard
- Amgen Ltd, 4 Uxbridge Business Park, Sanderson Road, Uxbridge, UB8 1DH, UK
| | - Xue Li
- Biologics Development, Bristol Myers Squibb, 1 Squibb Drive, New Brunswick, NJ 08901, USA
| | - Christopher Lössner
- Analytical Dev. Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Ben Niu
- Biotherapeutics, Bristol Myers Squibb, 4224 Campus Point Court, San Diego, CA 92121, USA
| | - Dietmar Reusch
- Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377 Penzberg, Germany
| | - Tomás O'Riordan
- Eli Lilly Kinsale Limited, Dunderrow, Kinsale, Co. Cork, P17NY71, Ireland
| | - Justin W Shearer
- Analytical Development, GSK, 709 Swedeland Road, King of Prussia, PA 19406, USA
| | - David Spencer
- BioPharmaceutical Development, Ipsen Biopharm Limited, 9 Ash Road, Wrexham Industrial Estate, Wrexham LL13 9UF, UK
| | - Wei Xu
- Analytical Sciences, BioPharmaceuticals R&D, AstraZeneca, One Medimmune Way, Gaithersburg, MD 20878, USA
| | - Linda Yi
- Analytical Development, Biogen, 5000 Davis Drive, Research Triangle Park, NC 27709, USA
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4
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Oyugi M, Wang X, Yang X, Wu D, Rogstad S. Method validation and new peak detection for the liquid chromatography-mass spectrometry multi-attribute method. J Pharm Biomed Anal 2023; 234:115564. [PMID: 37451094 DOI: 10.1016/j.jpba.2023.115564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/27/2023] [Accepted: 07/04/2023] [Indexed: 07/18/2023]
Abstract
The multi-attribute method (MAM) is a liquid chromatography-mass spectrometry (LC-MS) peptide mapping technique that has been proposed as a replacement for several conventional quality control (QC) methods for therapeutic proteins. In addition to quantification of multiple product quality attributes (PQAs), MAM can also monitor impurities using a new peak detection (NPD) feature. Here, results are provided from method validation and NPD studies of an MAM approach applied to rituximab as a model monoclonal antibody (mAb). Twenty-one rituximab PQAs were monitored, including oxidation, pyroglutamination, deamidation, lysine clipping, and glycosylation. The PQA monitoring aspect of the method was validated according to ICH Guidance. Accuracy, precision, specificity, detection and quantitation limits, linearity, range, and robustness were demonstrated for this MAM approach with minimal issues. All PQAs were successfully validated except for several oxidation sites, which did not pass intermediate precision criteria. The variability found in oxidation measurements was attributed to artificial oxidation during sample preparation and could likely be alleviated through several approaches. The NPD aspect of the method was also evaluated. A spike-in approach was used to assess the limits of detection and quantitation (LOD/LOQ) of the NPD feature of MAM. For NPD, the peak intensity threshold was found to be the most critical parameter for accurate detection of impurities since a low threshold can result in false positives while a high threshold can obscure the detection of true peaks. Overall, the MAM approach presented and validated here has been demonstrated to be suitable for both targeted monitoring of rituximab PQAs and non-targeted detection of new peaks that represent impurities.
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Affiliation(s)
- Mercy Oyugi
- Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA; Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA
| | - Xiaoshi Wang
- Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA; Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA
| | - Xiangkun Yang
- Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA; Prime Medicine, Cambridge, MA 02139, USA
| | - Di Wu
- Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA; AbbVie, South San Francisco, CA 94080, USA
| | - Sarah Rogstad
- Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA.
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5
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Li X. Recent applications of quantitative mass spectrometry in biopharmaceutical process development and manufacturing. J Pharm Biomed Anal 2023; 234:115581. [PMID: 37494866 DOI: 10.1016/j.jpba.2023.115581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/27/2023] [Accepted: 07/12/2023] [Indexed: 07/28/2023]
Abstract
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.
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Affiliation(s)
- Xuanwen Li
- Analytical Research and Development, MRL, Merck & Co., Inc., 126 E. Lincoln Avenue, Rahway, NJ 07065, USA.
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6
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Zhang Z, Richardson J, Shah B. Method for detecting rare differences between two LC-MS runs. Anal Biochem 2023:115211. [PMID: 37302778 DOI: 10.1016/j.ab.2023.115211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/04/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
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.
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Affiliation(s)
- Zhongqi Zhang
- Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320, USA.
| | - Jason Richardson
- Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320, USA
| | - Bhavana Shah
- Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320, USA
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7
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Pohl T, Gervais A, Dirksen E, D'Alessio V, Bechtold-Peters K, Burkitt W, Cao L, Greven S, Lennard A, Li X, Lössner C, Niu B, Reusch D, O'Riordan T, Shearer J, Spencer D, Xu W, Yi L. Technical considerations for the implementation of the Multi-Attribute-Method by mass spectrometry in a Quality Control laboratory. Eur J Pharm Biopharm 2023:S0939-6411(23)00112-1. [PMID: 37146738 DOI: 10.1016/j.ejpb.2023.04.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/07/2023]
Abstract
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).
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Affiliation(s)
- Thomas Pohl
- Biologics Analytical Development, Novartis Pharma AG, Klybeckstrasse 141, CH-4057 Basel, Switzerland
| | - Annick Gervais
- Analytical Development Sciences for Biologicals, UCB, Chemin du Foriest, 1420 Braine L'Alleud, Belgium
| | - Eef Dirksen
- Analytical Development and Quality Control, Byondis, Microweg 22, 6545 CM, Nijmegen, The Netherlands
| | - Valerio D'Alessio
- Analytical Development Biotech, Merck Serono S.p.A., Via Luigi Einaudi, 11, 00012 Guidonia Montecelio - Rome, Italy
| | - Karoline Bechtold-Peters
- Biologics Drug Product Development, Novartis Pharma AG, Klybeckstrasse 141, CH-4057 Basel, Switzerland
| | - Will Burkitt
- Biological Characterisation Product Development Sciences, UCB, 216 Bath Road, Slough, SL1 3WE, UK
| | - Li Cao
- Strategic External Development, GSK, 1250 S. Collegeville Road, Collegeville, Pennsylvania 19426, USA
| | - Simone Greven
- Pharmaceuticals, Biological Development, Bayer AG, Friedrich-Ebert-Strasse 217-333, 42117 Wuppertal, Germany
| | - Andrew Lennard
- Amgen, 4 Uxbridge Business Park, Sanderson Road, Uxbridge, UB8 1DH, UK
| | - Xue Li
- Biologics Development, Bristol Myers Squibb, 1 Squibb Drive, New Brunswick, New Jersey 08901, USA
| | - Christopher Lössner
- Analytical Dev. Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany
| | - Ben Niu
- Biotherapeutics, Bristol Myers Squibb, 4224 Campus Point Court, San Diego, California 92121, USA
| | - Dietmar Reusch
- Pharma Technical Development, Roche Diagnostics GmbH, Nonnenwald 2, 82377 Penzberg, Germany
| | - Tomás O'Riordan
- Eli Lilly Kinsale Limited, Dunderrow, Kinsale, Co. Cork, P17NY71, Ireland
| | - Justin Shearer
- Analytical Development, GSK, 709 Swedeland Road, King of Prussia, Pennsylvania 19406, USA
| | - David Spencer
- BioPharmaceutical Development, Ipsen Biopharm Limited, 9 Ash Road, Wrexham Industrial Estate, Wrexham, LL13 9UF, UK
| | - Wei Xu
- Analytical Sciences, BioPharmaceuticals R&D, AstraZeneca, One Medimmune Way, Gaithersburg, Maryland 20878, USA
| | - Linda Yi
- Analytical Development, Biogen, 5000 Davis Drive, Research Triangle Park, North Carolina 27709, USA
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8
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Bhattacharya S, Joshi S, Rathore AS. A native multi-dimensional monitoring workflow for at-line characterization of mAb titer, size, charge, and glycoform heterogeneities in cell culture supernatant. J Chromatogr A 2023; 1696:463983. [PMID: 37054641 DOI: 10.1016/j.chroma.2023.463983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/26/2023] [Accepted: 04/07/2023] [Indexed: 04/15/2023]
Abstract
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 1st 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 1st dimension followed by cation exchange chromatography in the 2nd 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.
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Affiliation(s)
- Sanghati Bhattacharya
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Srishti Joshi
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India.
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Butré CI, D'Atri V, Diemer H, Colas O, Wagner E, Beck A, Cianferani S, Guillarme D, Delobel A. Interlaboratory Evaluation of a User-Friendly Benchtop Mass Spectrometer for Multiple-Attribute Monitoring Studies of a Monoclonal Antibody. Molecules 2023; 28:molecules28062855. [PMID: 36985827 PMCID: PMC10053224 DOI: 10.3390/molecules28062855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
In the quest to market increasingly safer and more potent biotherapeutic proteins, the concept of the multi-attribute method (MAM) has emerged from biopharmaceutical companies to boost the quality-by-design process development. MAM strategies rely on state-of-the-art analytical workflows based on liquid chromatography coupled to mass spectrometry (LC-MS) to identify and quantify a selected series of critical quality attributes (CQA) in a single assay. Here, we aimed at evaluating the repeatability and robustness of a benchtop LC-MS platform along with bioinformatics data treatment pipelines for peptide mapping-based MAM studies using standardized LC-MS methods, with the objective to benchmark MAM methods across laboratories, taking nivolumab as a case study. Our results evidence strong interlaboratory consistency across LC-MS platforms for all CQAs (i.e., deamidation, oxidation, lysine clipping and glycosylation). In addition, our work uniquely highlights the crucial role of bioinformatics postprocessing in MAM studies, especially for low-abundant species quantification. Altogether, we believe that MAM has fostered the development of routine, robust, easy-to-use LC-MS platforms for high-throughput determination of major CQAs in a regulated environment.
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Affiliation(s)
- Claire I Butré
- Quality Assistance sa, Technoparc de Thudinie 2, 6536 Thuin, Belgium
| | - Valentina D'Atri
- School of Pharmaceutical Sciences, University of Geneva, CMU-Rue Michel Servet 1, 1211 Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU-Rue Michel Servet 1, 1211 Geneva, Switzerland
| | - Hélène Diemer
- Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France
- Infrastructure Nationale de Protéomique ProFI-FR2048, 67087 Strasbourg, France
| | - Olivier Colas
- Biologics CMC and Developability, IRPF, Centre d'Immunologie Pierre Fabre, 5 Avenue Napoleon III, 74160 Saint-Julien en Genevois, France
| | - Elsa Wagner
- Biologics CMC and Developability, IRPF, Centre d'Immunologie Pierre Fabre, 5 Avenue Napoleon III, 74160 Saint-Julien en Genevois, France
| | - Alain Beck
- Biologics CMC and Developability, IRPF, Centre d'Immunologie Pierre Fabre, 5 Avenue Napoleon III, 74160 Saint-Julien en Genevois, France
| | - Sarah Cianferani
- Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France
- Infrastructure Nationale de Protéomique ProFI-FR2048, 67087 Strasbourg, France
| | - Davy Guillarme
- School of Pharmaceutical Sciences, University of Geneva, CMU-Rue Michel Servet 1, 1211 Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU-Rue Michel Servet 1, 1211 Geneva, Switzerland
| | - Arnaud Delobel
- Quality Assistance sa, Technoparc de Thudinie 2, 6536 Thuin, Belgium
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Sadek M, Moore BN, Yu C, Ruppe N, Abdun-Nabi A, Hao Z, Alvarez M, Dahotre S, Deperalta G. A Robust Purity Method for Biotherapeutics Using New Peak Detection in an LC-MS-Based Multi-Attribute Method. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:484-492. [PMID: 36802331 DOI: 10.1021/jasms.2c00355] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Monica Sadek
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Benjamin Nathan Moore
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Christopher Yu
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Nicholas Ruppe
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Austin Abdun-Nabi
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Zhiqi Hao
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Melissa Alvarez
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Sanket Dahotre
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Galahad Deperalta
- Protein Analytical Chemistry, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
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11
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Li X, Pierson NA, Hua X, Patel BA, Olma MH, Strulson CA, Letarte S, Richardson DD. 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. J Pharm Sci 2023; 112:691-699. [PMID: 36279953 DOI: 10.1016/j.xphs.2022.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/15/2022] [Accepted: 10/15/2022] [Indexed: 11/06/2022]
Abstract
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.
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Affiliation(s)
- Xuanwen Li
- Analytical Research & Development, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, United States.
| | - Nicholas A Pierson
- Analytical Research & Development, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, United States
| | - Xiaoqing Hua
- Analytical Research & Development, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, United States
| | - Bhumit A Patel
- Analytical Research & Development, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, United States
| | - Michael H Olma
- Analytical Research & Development, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, United States
| | - Christopher A Strulson
- Analytical Research & Development, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, United States
| | - Simon Letarte
- Analytical Research & Development, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, United States
| | - Douglas D Richardson
- Analytical Research & Development, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, United States
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12
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Development and optimization of a LC-MS based multi-attribute method (MAM) workflow for characterization of therapeutic Fc-fusion protein. Anal Biochem 2023; 660:114969. [PMID: 36343663 DOI: 10.1016/j.ab.2022.114969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/21/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022]
Abstract
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.
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13
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Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing. Nat Protoc 2022; 18:1056-1089. [PMID: 36526726 DOI: 10.1038/s41596-022-00785-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 10/04/2022] [Indexed: 12/23/2022]
Abstract
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.
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14
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Mouchahoir T, Schiel JE, Rogers R, Heckert A, Place BJ, Ammerman A, Li X, Robinson T, Schmidt B, Chumsae CM, Li X, Manuilov AV, Yan B, Staples GO, Ren D, Veach AJ, Wang D, Yared W, Sosic Z, Wang Y, Zang L, Leone AM, Liu P, Ludwig R, Tao L, Wu W, Cansizoglu A, Hanneman A, Adams GW, Perdivara I, Walker H, Wilson M, Brandenburg A, DeGraan-Weber N, Gotta S, Shambaugh J, Alvarez M, Yu XC, Cao L, Shao C, Mahan A, Nanda H, Nields K, Nightlinger N, Niu B, Wang J, Xu W, Leo G, Sepe N, Liu YH, Patel BA, Richardson D, Wang Y, Tizabi D, Borisov OV, Lu Y, Maynard EL, Gruhler A, Haselmann KF, Krogh TN, Sönksen CP, Letarte S, Shen S, Boggio K, Johnson K, Ni W, Patel H, Ripley D, Rouse JC, Zhang Y, Daniels C, Dawdy A, Friese O, Powers TW, Sperry JB, Woods J, Carlson E, Sen KI, Skilton SJ, Busch M, Lund A, Stapels M, Guo X, Heidelberger S, Kaluarachchi H, McCarthy S, Kim J, Zhen J, Zhou Y, Rogstad S, Wang X, Fang J, Chen W, Yu YQ, Hoogerheide JG, Scott R, Yuan H. Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1659-1677. [PMID: 36018776 PMCID: PMC9460773 DOI: 10.1021/jasms.2c00129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 05/23/2023]
Abstract
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.
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Affiliation(s)
- Trina Mouchahoir
- National
Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, Maryland 20899, United States
- Institute
for Bioscience and Biotechnology Research, 9600 Gudelsky Dr, Rockville, Maryland 20850, United States
| | - John E. Schiel
- National
Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, Maryland 20899, United States
- Institute
for Bioscience and Biotechnology Research, 9600 Gudelsky Dr, Rockville, Maryland 20850, United States
| | - Rich Rogers
- Just-Evotech
Biologics, Inc., 401
Terry Ave N., Seattle, Washington 98109, United States
| | - Alan Heckert
- National
Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, Maryland 20899, United States
| | - Benjamin J. Place
- National
Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, Maryland 20899, United States
| | - Aaron Ammerman
- AbbVie, 1000 Gateway
Blvd, South San Francisco, California 94080, United States
| | - Xiaoxiao Li
- AbbVie, 1000 Gateway
Blvd, South San Francisco, California 94080, United States
| | - Tom Robinson
- AbbVie, 1000 Gateway
Blvd, South San Francisco, California 94080, United States
| | - Brian Schmidt
- AbbVie, 1000 Gateway
Blvd, South San Francisco, California 94080, United States
| | - Chris M. Chumsae
- AbbVie, 100 Research Drive, Worcester, Massachusetts 01605, United States
| | - Xinbi Li
- AbbVie, 100 Research Drive, Worcester, Massachusetts 01605, United States
| | - Anton V. Manuilov
- AbbVie, 100 Research Drive, Worcester, Massachusetts 01605, United States
| | - Bo Yan
- AbbVie, 100 Research Drive, Worcester, Massachusetts 01605, United States
| | - Gregory O. Staples
- Agilent
Technologies, 5301 Stevens Creek Blvd, Santa Clara, California 95008, United States
| | - Da Ren
- Amgen, One Amgen Center Dr, Thousand
Oaks, California 91320, United States
| | - Alexander J. Veach
- Amgen, One Amgen Center Dr, Thousand
Oaks, California 91320, United States
| | - Dongdong Wang
- BioAnalytix, 790 Memorial Dr, Cambridge, Massachusetts 02139, United States
| | - Wael Yared
- BioAnalytix, 790 Memorial Dr, Cambridge, Massachusetts 02139, United States
| | - Zoran Sosic
- Biogen, 125 Broadway, Cambridge, Massachusetts 02142, United States
| | - Yan Wang
- Biogen, 125 Broadway, Cambridge, Massachusetts 02142, United States
| | - Li Zang
- Biogen, 125 Broadway, Cambridge, Massachusetts 02142, United States
| | - Anthony M. Leone
- Bristol-Myers
Squibb, 311 Pennington-Rocky Hill Road, Pennington, New Jersey 08534, United States
| | - Peiran Liu
- Bristol-Myers
Squibb, 311 Pennington-Rocky Hill Road, Pennington, New Jersey 08534, United States
| | - Richard Ludwig
- Bristol-Myers
Squibb, 311 Pennington-Rocky Hill Road, Pennington, New Jersey 08534, United States
| | - Li Tao
- Bristol-Myers
Squibb, 311 Pennington-Rocky Hill Road, Pennington, New Jersey 08534, United States
| | - Wei Wu
- Bristol-Myers
Squibb, 311 Pennington-Rocky Hill Road, Pennington, New Jersey 08534, United States
| | - Ahmet Cansizoglu
- Charles
River Laboratories, 8
Henshaw Street, Shrewsbury, Massachusetts 01801, United States
| | - Andrew Hanneman
- Charles
River Laboratories, 8
Henshaw Street, Shrewsbury, Massachusetts 01801, United States
| | - Greg W. Adams
- FUJIFILM
Diosynth Biotechnologies, 101 J. Morris Commons Ln, Morrisville, North Carolina 27560, United States
| | - Irina Perdivara
- FUJIFILM
Diosynth Biotechnologies, 101 J. Morris Commons Ln, Morrisville, North Carolina 27560, United States
| | - Hunter Walker
- FUJIFILM
Diosynth Biotechnologies, 101 J. Morris Commons Ln, Morrisville, North Carolina 27560, United States
| | - Margo Wilson
- FUJIFILM
Diosynth Biotechnologies, 101 J. Morris Commons Ln, Morrisville, North Carolina 27560, United States
| | | | - Nick DeGraan-Weber
- Genedata, 750 Marrett Road, One Cranberry
Hill, Lexington, Massachusetts 02421, United States
| | - Stefano Gotta
- Genedata, Margarethenstrasse 38, Basel, 4053, Switzerland
| | - Joe Shambaugh
- Genedata, 750 Marrett Road, One Cranberry
Hill, Lexington, Massachusetts 02421, United States
| | - Melissa Alvarez
- Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - X. Christopher Yu
- Genentech, 1 DNA Way, South San Francisco, California 94080, United States
| | - Li Cao
- GSK, 709
Swedeland Rd, King of Prussia, Pennsylvania 19406, United States
| | - Chun Shao
- GSK, 709
Swedeland Rd, King of Prussia, Pennsylvania 19406, United States
| | - Andrew Mahan
- Janssen, 1400 McKean Road, Springhouse, Pennsylvania 19477, United States
| | - Hirsh Nanda
- Janssen, 1400 McKean Road, Springhouse, Pennsylvania 19477, United States
| | - Kristen Nields
- Janssen, 1400 McKean Road, Springhouse, Pennsylvania 19477, United States
| | - Nancy Nightlinger
- Just-Evotech
Biologics, Inc., 401
Terry Ave N., Seattle, Washington 98109, United States
| | - Ben Niu
- AstraZeneca, One MedImmune Way, Gaithersburg, Maryland 20878, United
States
| | - Jihong Wang
- AstraZeneca, One MedImmune Way, Gaithersburg, Maryland 20878, United
States
| | - Wei Xu
- AstraZeneca, One MedImmune Way, Gaithersburg, Maryland 20878, United
States
| | - Gabriella Leo
- EMD Serono an affiliate of Merck KGaA, Darmstadt, Germany, Via Luigi Einaudi 11, Guidonia Montecelio (Roma), 00012, Italy
| | - Nunzio Sepe
- EMD Serono an affiliate of Merck KGaA, Darmstadt, Germany, Via Luigi Einaudi 11, Guidonia Montecelio (Roma), 00012, Italy
| | - Yan-Hui Liu
- Merck
& Co., Inc.., 2000 Galloping Hill Rd, Kenilworth, New Jersey 07033, United States
| | - Bhumit A. Patel
- Merck
& Co., Inc.., 2000 Galloping Hill Rd, Kenilworth, New Jersey 07033, United States
| | - Douglas Richardson
- Merck
& Co., Inc.., 2000 Galloping Hill Rd, Kenilworth, New Jersey 07033, United States
| | - Yi Wang
- Merck
& Co., Inc.., 2000 Galloping Hill Rd, Kenilworth, New Jersey 07033, United States
| | - Daniela Tizabi
- National
Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, Maryland 20899, United States
- Institute
for Bioscience and Biotechnology Research, 9600 Gudelsky Dr, Rockville, Maryland 20850, United States
| | - Oleg V. Borisov
- Novavax,
Inc., 20 Firstfield Road, Gaithersburg, Maryland 20878, United States
| | - Yali Lu
- Novavax,
Inc., 20 Firstfield Road, Gaithersburg, Maryland 20878, United States
| | - Ernest L. Maynard
- Novavax,
Inc., 20 Firstfield Road, Gaithersburg, Maryland 20878, United States
| | | | | | | | | | - Simon Letarte
- Pfizer, 375 N Field Dr, Lake Forest, Illinois 60045, United
States
| | - Sean Shen
- Pfizer, 375 N Field Dr, Lake Forest, Illinois 60045, United
States
| | - Kristin Boggio
- Pfizer, 1 Burtt Rd, Andover, Massachusetts 01810, United States
| | - Keith Johnson
- Pfizer, 1 Burtt Rd, Andover, Massachusetts 01810, United States
| | - Wenqin Ni
- Pfizer, 1 Burtt Rd, Andover, Massachusetts 01810, United States
| | - Himakshi Patel
- Pfizer, 1 Burtt Rd, Andover, Massachusetts 01810, United States
| | - David Ripley
- Pfizer, 1 Burtt Rd, Andover, Massachusetts 01810, United States
| | - Jason C. Rouse
- Pfizer, 1 Burtt Rd, Andover, Massachusetts 01810, United States
| | - Ying Zhang
- Pfizer, 1 Burtt Rd, Andover, Massachusetts 01810, United States
| | - Carly Daniels
- Pfizer, 700 Chesterfield
Pkwy West, Chesterfield, Missouri 63017, United
States
| | - Andrew Dawdy
- Pfizer, 700 Chesterfield
Pkwy West, Chesterfield, Missouri 63017, United
States
| | - Olga Friese
- Pfizer, 700 Chesterfield
Pkwy West, Chesterfield, Missouri 63017, United
States
| | - Thomas W. Powers
- Pfizer, 700 Chesterfield
Pkwy West, Chesterfield, Missouri 63017, United
States
| | - Justin B. Sperry
- Pfizer, 700 Chesterfield
Pkwy West, Chesterfield, Missouri 63017, United
States
| | - Josh Woods
- Pfizer, 700 Chesterfield
Pkwy West, Chesterfield, Missouri 63017, United
States
| | - Eric Carlson
- Protein
Metrics, Inc., 20863
Stevens Creek Blvd, Cupertino, California 95014, United States
| | - K. Ilker Sen
- Protein
Metrics, Inc., 20863
Stevens Creek Blvd, Cupertino, California 95014, United States
| | - St John Skilton
- Protein
Metrics, Inc., 20863
Stevens Creek Blvd, Cupertino, California 95014, United States
| | - Michelle Busch
- Sanofi, 1 The Mountain Rd, Framingham, Massachusetts 01701, United States
| | - Anders Lund
- Sanofi, 1 The Mountain Rd, Framingham, Massachusetts 01701, United States
| | - Martha Stapels
- Sanofi, 1 The Mountain Rd, Framingham, Massachusetts 01701, United States
| | - Xu Guo
- SCIEX, 71 Four Valley Drive, Concord, ON L4K
4V8, Canada
| | | | | | - Sean McCarthy
- SCIEX, 500 Old Connecticut Path, Framingham, Massachusetts 01701, United States
| | - John Kim
- Teva, 145 Brandywine Pkwy, West Chester, Pennsylvania 19380, United States
| | - Jing Zhen
- Teva, 145 Brandywine Pkwy, West Chester, Pennsylvania 19380, United States
| | - Ying Zhou
- Teva, 145 Brandywine Pkwy, West Chester, Pennsylvania 19380, United States
| | - Sarah Rogstad
- U.S. Food
and Drug Administration, 10903 New Hampshire Ave, Silver Spring, Maryland 20993, United States
| | - Xiaoshi Wang
- U.S. Food
and Drug Administration, 10903 New Hampshire Ave, Silver Spring, Maryland 20993, United States
| | - Jing Fang
- Waters, 34 Maple St, Milford, Massachusetts 01757, United States
| | - Weibin Chen
- Waters, 34 Maple St, Milford, Massachusetts 01757, United States
| | - Ying Qing Yu
- Waters, 34 Maple St, Milford, Massachusetts 01757, United States
| | | | - Rebecca Scott
- Zoetis, 333 Portage St, Kalamazoo, Michigan 49007, United
States
| | - Hua Yuan
- Zoetis, 333 Portage St, Kalamazoo, Michigan 49007, United
States
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15
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Lalchandani DS, Paritala S, Gupta PK, Porwal PK. Application of Supervised and Unsupervised Learning Approaches for Mapping Storage Conditions of Biopharmaceutical Product-A Case Study of Human Serum Albumin. J Chromatogr Sci 2022:6640002. [PMID: 35817343 DOI: 10.1093/chromsci/bmac060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 06/15/2022] [Accepted: 06/24/2022] [Indexed: 11/14/2022]
Abstract
The stability of biopharmaceutical therapeutics over the storage period/shelf life has been a challenging concern for manufacturers. A noble strategy for mapping best and suitable storage conditions for recombinant human serum albumin (rHSA) in laboratory mixture was optimized using chromatographic data as per principal component analysis (PCA), and similarity was defined using hierarchical cluster analysis. In contrast, separability was defined using linear discriminant analysis (LDA) models. The quantitation was performed for rHSA peak (analyte of interest) and its degraded products, i.e., dimer, trimer, agglomerates and other degradation products. The chromatographic variables were calculated using validated stability-indicating assay method. The chromatographic data mapping was done for the above-mentioned peaks over three months at different temperatures, i.e., 20°C, 5-8°C and at room temperature (25°C). The PCA had figured out the ungrouped variable, whereas supervised mapping was done using LDA. As an outcome result of LDA, about 60% of data were correctly classified with the highest sensitivity for 25°C (Aq), 25°C and 5-8°C (Aq with 5% glucose as a stabilizer), whereas the highest specificity was observed for samples stored at 5-8°C (Aq with 5% glucose as a stabilizer).
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Affiliation(s)
- Dimple S Lalchandani
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Guwahati (NIPER-G), Sila Katamur (Halugurisuk), Changsari, Guwahati, Assam 781101, India
| | - Sreeteja Paritala
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Guwahati (NIPER-G), Sila Katamur (Halugurisuk), Changsari, Guwahati, Assam 781101, India
| | - Pawan Kumar Gupta
- Department of Pharmaceutical Chemistry, Amity Institute of Pharmacy, Amity University Maharajpura, Gwalior, Madhya Pradesh 474 005, India
| | - Pawan Kumar Porwal
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Guwahati (NIPER-G), Sila Katamur (Halugurisuk), Changsari, Guwahati, Assam 781101, India
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16
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Intact multi-attribute method (iMAM): a flexible tool for the analysis of monoclonal antibodies. Eur J Pharm Biopharm 2022; 177:241-248. [PMID: 35840072 DOI: 10.1016/j.ejpb.2022.07.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/08/2022] [Accepted: 07/09/2022] [Indexed: 11/22/2022]
Abstract
The availability of rapid methods that can accurately define and quantify biopharmaceutical critical quality attributes has been the driving force for the implementation of mass spectrometry techniques throughout the development and production pipeline. While the multi-attribute method (MAM) has become widely adopted and developed, some critical information cannot be monitored through this workflow, such as correct chain assembly or the presence of fragments or aggregates. In this study, we combine intact mass spectrometry and the multi-attribute method to create an intact multi-attribute method - or iMAM. Using a CFR Part 11 compliant data system, we evaluated the proposed workflow using several intact analysis approaches under both denaturing and native conditions. As for the standard MAM approach, iMAM involves the creation of an intact protein target workbook which is created from a reference sample, with ID confirmation obtained from deconvolution results and chromatographic retention times while quantitation is obtained from the intensities of the m/z of most abundant charge states. The created processing method is then applied to any other sample. New peak detection can also be performed, monitoring the number of components revealed after each analysis. The entire data process can be automated to generate a report within the chromatography data system software. Three case studies presented herein show the potential of iMAM for implementation at different stages of the production pipeline, from product development to stability testing and batch release.
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17
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Rose B, May JC, Reardon AR, McLean JA. Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1229-1237. [PMID: 35653638 PMCID: PMC9516683 DOI: 10.1021/jasms.2c00067] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Structures for lossless ion manipulation-based high-resolution ion mobility (HRIM) interfaced with mass spectrometry has emerged as a powerful tool for the separation and analysis of many isomeric systems. IM-derived collision cross section (CCS) is increasingly used as a molecular descriptor for structural analysis and feature annotation, but there are few studies on the calibration of CCS from HRIM measurements. Here, we examine the accuracy, reproducibility, and practical applicability of CCS calibration strategies for a broad range of lipid subclasses and develop a straightforward and generalizable framework for obtaining high-resolution CCS values. We explore the utility of using structurally similar custom calibrant sets as well as lipid subclass-specific empirically derived correction factors. While the lipid calibrant sets lowered overall bias of reference CCS values from ∼2-3% to ∼0.5%, application of the subclass-specific correction to values calibrated with a broadly available general calibrant set resulted in biases <0.4%. Using this method, we generated a high-resolution CCS database containing over 90 lipid values with HRIM. To test the applicability of this method to a broader class range typical of lipidomics experiments, a standard lipid mix was analyzed. The results highlight the importance of both class and arrival time range when correcting or scaling CCS values and provide guidance for implementation of the method for more general applications.
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18
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Nagornov KO, Kozhinov AN, Gasilova N, Menin L, Tsybin YO. Characterization of the Time-Domain Isotopic Beat Patterns of Monoclonal Antibodies in Fourier Transform Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1113-1125. [PMID: 35638743 DOI: 10.1021/jasms.1c00336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The time-domain transients in the Fourier transform mass spectrometry (FTMS) analysis of monoclonal antibodies (mAbs) are known to exhibit characteristic isotopic beat patterns. These patterns are defined by the isotopic distributions of all gaseous mAb ions present in the FTMS mass analyzer, originating from single or multiple charge states, and from single or multiple proteoforms. For an isolated charge state of a single proteoform, the mAb isotopic beat pattern resembles narrow splashes of signal amplitude (beats), spaced periodically in the time-domain transient, with broad (often exceeding 1 s) "valleys" between them. Here, we reinforce the importance of isotopic beat patterns for the accurate interpretation and presentation of FTMS data in the analysis of mAbs and other large biopolymers. An updated, mAb-grade version of the transient-mediated FTMS data simulation and visualization tool, FTMS Simulator is introduced and benchmarked. We then apply this tool to evaluate the charge-state dependent characteristics of isotopic beats in mAbs analyses with modern models of Orbitrap and ion cyclotron resonance (ICR) FTMS instruments, including detection of higher-order harmonics. We demonstrate the impact of the isotopic beat patterns on the analytical characteristics of the resulting mass spectra of individual and overlapping mAb proteoforms. The results reported here detail highly nonlinear dependences of resolution and signal-to-noise ratio on the time-domain transient period, absorption or magnitude mode spectra representation, and apodization functions. The provided description and the demonstrated ability to routinely conduct accurate simulations of FTMS data for large biopolymers should aid the end-users of Orbitrap and ICR FTMS instruments in the analysis of mAbs and other biopolymers, including viruses.
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Affiliation(s)
| | | | - Natalia Gasilova
- Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Laure Menin
- Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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19
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Li X, Rawal B, Rivera S, Letarte S, Richardson DD. Improvements on sample preparation and peptide separation for reduced peptide mapping based multi-attribute method analysis of therapeutic monoclonal antibodies using lysyl endopeptidase digestion. J Chromatogr A 2022; 1675:463161. [DOI: 10.1016/j.chroma.2022.463161] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 12/14/2022]
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20
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Lombard-Banek C, Pohl KI, Kwee EJ, Elliott JT, Schiel JE. A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies. J Proteome Res 2022; 21:1229-1239. [PMID: 35404046 PMCID: PMC9087334 DOI: 10.1021/acs.jproteome.1c00887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Indexed: 11/29/2022]
Abstract
Mass spectrometry (MS)-based proteomic measurements are uniquely poised to impact the development of cell and gene therapies. With the adoption of rigorous instrumental performance qualifications (PQs), large-scale proteomics can move from a research to a manufacturing control tool. Especially suited, data-independent acquisition (DIA) approaches have distinctive qualities to extend multiattribute method (MAM) principles to characterize the proteome of cell therapies. Here, we describe the development of a DIA method for the sensitive identification and quantification of proteins on a Q-TOF instrument. Using the improved acquisition parameters, we defined a control strategy and highlighted some metrics to improve the reproducibility of SWATH acquisition-based proteomic measurements. Finally, we applied the method to analyze the proteome of Jurkat cells that here serves as a model for human T-cells. Raw and processed data were deposited in PRIDE (PXD029780).
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Affiliation(s)
- Camille Lombard-Banek
- National
Institute of Standards and Technology, Material and Measurements Laboratory, Gaithersburg, Maryland 20899, United States
- Institute
for Bioscience and Bioengineering Research, Rockville, Maryland 20850, United States
| | | | - Edward J. Kwee
- National
Institute of Standards and Technology, Material and Measurements Laboratory, Gaithersburg, Maryland 20899, United States
| | - John T. Elliott
- National
Institute of Standards and Technology, Material and Measurements Laboratory, Gaithersburg, Maryland 20899, United States
| | - John E. Schiel
- National
Institute of Standards and Technology, Material and Measurements Laboratory, Gaithersburg, Maryland 20899, United States
- Institute
for Bioscience and Bioengineering Research, Rockville, Maryland 20850, United States
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21
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Yandrofski K, Mouchahoir T, De Leoz ML, Duewer D, Hudgens JW, Anderson KW, Arbogast L, Delaglio F, Brinson RG, Marino JP, Phinney K, Tarlov M, Schiel JE. Interlaboratory Studies Using the NISTmAb to Advance Biopharmaceutical Structural Analytics. Front Mol Biosci 2022; 9:876780. [PMID: 35601836 PMCID: PMC9117750 DOI: 10.3389/fmolb.2022.876780] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 03/21/2022] [Indexed: 01/18/2023] Open
Abstract
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.
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Affiliation(s)
- Katharina Yandrofski
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
- *Correspondence: Katharina Yandrofski,
| | - Trina Mouchahoir
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
| | | | - David Duewer
- National Institute of Standards and Technology, Gaithersburg, MD, United States
| | - Jeffrey W. Hudgens
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
| | - Kyle W. Anderson
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
| | - Luke Arbogast
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
| | - Frank Delaglio
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
| | - Robert G. Brinson
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
| | - John P. Marino
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
| | - Karen Phinney
- National Institute of Standards and Technology, Gaithersburg, MD, United States
| | - Michael Tarlov
- National Institute of Standards and Technology, Gaithersburg, MD, United States
| | - John E. Schiel
- Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, Rockville, MD, United States
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22
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Nupur N, Joshi S, Gulliarme D, Rathore AS. Analytical Similarity Assessment of Biosimilars: Global Regulatory Landscape, Recent Studies and Major Advancements in Orthogonal Platforms. Front Bioeng Biotechnol 2022; 10:832059. [PMID: 35223794 PMCID: PMC8865741 DOI: 10.3389/fbioe.2022.832059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/07/2022] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Neh Nupur
- Department of Chemical Engineering, IIT Delhi, Hauz Khas, New Delhi, India
| | - Srishti Joshi
- Department of Chemical Engineering, IIT Delhi, Hauz Khas, New Delhi, India
| | - Davy Gulliarme
- Institute of Pharmaceutical Sciences of Western Switzerland (ISPSO), University of Geneva, Geneva, Switzerland
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
| | - Anurag S. Rathore
- Department of Chemical Engineering, IIT Delhi, Hauz Khas, New Delhi, India
- *Correspondence: Anurag S. Rathore,
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23
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Carvalho SB, Gomes RA, Pfenninger A, Fischer M, Strotbek M, Isidro IA, Tugçu N, Gomes-Alves P. Multi attribute method implementation using a High Resolution Mass Spectrometry platform: From sample preparation to batch analysis. PLoS One 2022; 17:e0262711. [PMID: 35085302 PMCID: PMC8794205 DOI: 10.1371/journal.pone.0262711] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/01/2022] [Indexed: 11/18/2022] Open
Abstract
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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Affiliation(s)
- Sofia B. Carvalho
- iBET, Instituto de Biologia Experimental e Tecnologica, Oeiras, Portugal
- ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Ricardo A. Gomes
- iBET, Instituto de Biologia Experimental e Tecnologica, Oeiras, Portugal
- ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Anja Pfenninger
- Sanofi R&D, Biologics Development, Industriepark Höchst, Frankfurt am Main, Germany
| | - Martina Fischer
- Sanofi R&D, Biologics Development, Industriepark Höchst, Frankfurt am Main, Germany
| | - Michaela Strotbek
- Sanofi R&D, Biologics Development, Industriepark Höchst, Frankfurt am Main, Germany
| | - Inês A. Isidro
- iBET, Instituto de Biologia Experimental e Tecnologica, Oeiras, Portugal
- ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Nihal Tugçu
- Mammalian Platform, Global CMC Development, Sanofi, Framingham, MA, United States of America
| | - Patrícia Gomes-Alves
- iBET, Instituto de Biologia Experimental e Tecnologica, Oeiras, Portugal
- ITQB-NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
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24
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Sitasuwan P, Powers TW, Medwid T, Huang Y, Bare B, Lee LA. Enhancing the multi-attribute method through an automated and high-throughput sample preparation. MAbs 2021; 13:1978131. [PMID: 34586946 PMCID: PMC8489909 DOI: 10.1080/19420862.2021.1978131] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The multi-attribute method (MAM), a recent advance in the application of liquid chromatography-mass spectrometry within the pharmaceutical industry, enables the simultaneous monitoring of multiple product quality attributes in a single analytical method. While MAM is coupled with automated data processing and reporting, the sample preparation, based on proteolytic peptide mapping, remains cumbersome and low throughput. The standard sample preparation for MAM relies on protein denaturation, reduction, and alkylation prior to proteolytic digestion, but often a desalting step is required to maintain enzymatic activity. While most of the sample preparation can be automated on a standard robotic liquid handling system, a streamlined approach for protein desalting and temperature modulation is required for a viable, fully automated digestion. In this work, for the first time, a complete tip-based MAM sample preparation is automated on a single robotic liquid handling system, leveraging a deck layout that integrates both heating and cooling functionalities. The fully automated method documented herein achieves a high-throughput sample preparation for MAM, while maintaining superior method performance. Abbreviations: MAM: multi-attribute method; PQAs: product quality attributes; CE: capillary electrophoresis; IEX: ion-exchange chromatography; HILIC-FLR: hydrophilic interaction liquid chromatography coupled to a fluorescence detector; RP-LC/UV: reversed-phase liquid chromatography coupled to a UV detector; MS: mass spectrometry; NPD: new peak detection; GdnHCl: guanidine hydrochloride; TIC: total ion current; pAb: polyclonal antibody; IgG: immunoglobulin G; DTT: dithiothreitol; IAA: iodoacetic acid; TFA: trifluoroacetic acid; A280: absorbance at 280 nm wavelength; 96MPH: 96-channel multi-probe head; CPAC: Cold Plate Air Cooled; HHS: Hamilton Heater Shaker; DWP: Deep-Well Plate; PCR: Polymerase Chain Reaction; NTR: Nested Tip Rack; Met: methionine; Trp: tryptophan; N-term pQ: N-terminal glutamine cyclization; Lys: lysine; PAM: peptidylglycine α-amidating monooxygenase; G0F: asialo-, agalacto-, bi-antennary, core substituted with fucose; G1F: asialo-, mono-galactosylated bi-antennary, core substituted with fucose; G2F: asialo-, bi-galactosylated bi-antennary, core substituted with fucose; G0: asialo-, agalacto-, bi-antennary; Man5: oligomannose 5; Man8: oligomannose 8; TriF: asialo-, tri-galactosylated tri-antennary, core substituted with fucose.
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Affiliation(s)
| | | | | | | | | | - L Andrew Lee
- Integrated Micro-Chromatography Systems, Inc, Irmo, SC, USA
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25
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Evans AR, Hebert AS, Mulholland J, Lewis MJ, Hu P. ID-MAM: A Validated Identity and Multi-Attribute Monitoring Method for Commercial Release and Stability Testing of a Bispecific Antibody. Anal Chem 2021; 93:9166-9173. [PMID: 34161073 DOI: 10.1021/acs.analchem.1c01029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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.
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Affiliation(s)
- Adam R Evans
- BioTherapeutics Development & Supply-Analytical Development, Janssen Research and Development, LLC, Malvern, Pennsylvania 19355, United States
| | - Alexander S Hebert
- BioTherapeutics Development & Supply-Analytical Development, Janssen Research and Development, LLC, Malvern, Pennsylvania 19355, United States
| | - Joseph Mulholland
- BioTherapeutics Development & Supply-Analytical Development, Janssen Research and Development, LLC, Malvern, Pennsylvania 19355, United States
| | - Michael J Lewis
- BioTherapeutics Development & Supply-Analytical Development, Janssen Research and Development, LLC, Malvern, Pennsylvania 19355, United States
| | - Ping Hu
- BioTherapeutics Development & Supply-Analytical Development, Janssen Research and Development, LLC, Malvern, Pennsylvania 19355, United States
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26
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Oliva A, Llabrés M. New Quality-Range-Setting Method Based on Between- and Within-Batch Variability for Biosimilarity Assessment. Pharmaceuticals (Basel) 2021; 14:ph14060527. [PMID: 34205892 PMCID: PMC8226621 DOI: 10.3390/ph14060527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/29/2021] [Accepted: 05/30/2021] [Indexed: 11/18/2022] Open
Abstract
Analytical biosimilarity assessment relies on two implicit conditions. First, the analytical method must meet a set of requirements known as fit for intended use related to trueness and precision. Second, the manufacture of the reference drug product must be under statistical quality control; i.e., the between-batch variability is not larger than the expected within-batch variability. In addition, the quality range (QR) method is based on one sample per batch to avoid biased standard deviations in unbalanced studies. This, together with the small number of reference drug product batches, leads to highly variable QR bounds. In this paper, we propose to set the QR bounds from variance components estimated using a two-level nested linear model, accounting for between- and within-batch variances of the reference drug product. In this way, the standard deviation used to set QR is equal to the square root of the sum of between-batch variance plus the within-batch variance estimated by the maximum likelihood method. The process of this method, which we call QRML, is as follows. First, the condition of statistical quality control of the manufacture process is tested. Second, confidence intervals for QR bounds lead to an analysis of the reliability of the biosimilarity assessment. Third, after analyzing the molecular weight and dimer content of seven batches of a commercial bevacizumab drug product, we concluded that the QRML method was more reliable than QR.
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