Applied Science, Faculty of
Chemical and Biological Engineering, Department of
DSpace
UBCV
Gupta, Shivani
Doctor of Philosophy - PhD
University of British Columbia
Since 2015 more than 39 biosimilars have been approved by the FDA, with over 20 launched, providing alternatives for widely used protein therapeutics. A major challenge in biosimilar development is the genetic differences in the host cell lines used to manufacture these biologics. Many biologics approved before 2011 were expressed in murine cell lines, whereas Chinese Hamster Ovary (CHO) cells have become the preferred hosts for production. Differences between glycosylation have been identified in biologics produced using murine CHO cell line. In the case of monoclonal antibodies (mAbs), differences in glycans can significantly impact critical antibody effector function, binding activity, efficacy, and in vivo half-life. To more closely match the reference biologic (RB) glycosylation, a mAb expressing CHO cell line was genetically engineered to express mAb with murine-like glycans. This was done by selectively co-expressing two key murine enzymes, α1,3-galactosyltransferase (GGTA1) and cytidine monophosphate-N-Acetylneuraminic acid hydroxylase (CMAH). These CHO cells were then analyzed using a spectrum of analytical methods to demonstrate biosimilarity, including high-resolution mass spectrometry and biochemical and cell-based assays. In fed-batch cultures, two CHO cell clones were identified that maintained stable production while matching the glycosylation profile and function of the reference product. This study demonstrates the feasibility of engineering CHO cells to express mAbs with murine glycans, to reduce the residual uncertainty regarding biosimilarity, resulting in a higher probability of regulatory approval and potentially reduced costs and time in development. In parallel, a high-throughput CHO-specific assay was designed and evaluated to quantify N-glycosylation-related enzyme mRNA expression levels. This assay is referred to as the N-glycan enzyme mRNA expression profiler. Two different mAbs produced by two CHO cell hosts were investigated to assess the relationships between glycan mRNA levels and mAb glycan profiles. Genetic understanding of the N-glycosylation pathway in the CHO cells helped identify gene expression patterns that correlated to the N-glycan profile of the produced mAb. The results indicated that this assay could identify clones with desirable genetic makeups early in cell line development and aid in designing cell line engineering strategies to obtain the desired glycan profiles for both novel biologic and biosimilar development.
ENGINEERING CHINESE HAMSTER OVARY CELL RECOMBINANT PROTEIN GLYCOSYLATION by Shivani Gupta M.S. Northwestern University, 2010 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Chemical and Biological Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2022 © Shivani Gupta, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Engineering Chinese Hamster Ovary Cell Recombinant Protein Glycosylation submitted by Shivani Gupta in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Chemical and Biological Engineering Examining Committee: James Piret, Professor, Chemical and Biological Engineering, UBC Supervisor Chetan Goudar, Vice President, Attribute Sciences, Amgen Inc. Supervisory Committee Member Stephen Withers, Professor, Department of Chemistry, UBC University Examiner Vikramaditya G. Yadav, Associate Professor, Chemical and Biological Engineering, UBC University Examiner Additional Supervisory Committee Members: Charles Haynes, Professor, Chemical and Biological Engineering, UBC Supervisory Committee Member iii Abstract Since 2015 more than 39 biosimilars have been approved by the FDA, with over 20 launched, providing alternatives for widely used protein therapeutics. A major challenge in biosimilar development is the genetic differences in the host cell lines used to manufacture these biologics. Many biologics approved before 2011 were expressed in murine cell lines, whereas Chinese Hamster Ovary (CHO) cells have become the preferred hosts for production. Differences between glycosylation have been identified in biologics produced using murine CHO cell line. In the case of monoclonal antibodies (mAbs), differences in glycans can significantly impact critical antibody effector function, binding activity, efficacy, and in vivo half-life. To more closely match the reference biologic (RB) glycosylation, a mAb expressing CHO cell line was genetically engineered to express mAb with murine-like glycans. This was done by selectively co-expressing two key murine enzymes, α1,3-galactosyltransferase (GGTA1) and cytidine monophosphate-N-Acetylneuraminic acid hydroxylase (CMAH). These CHO cells were then analyzed using a spectrum of analytical methods to demonstrate biosimilarity, including high-resolution mass spectrometry and biochemical and cell-based assays. In fed-batch cultures, two CHO cell clones were identified that maintained stable production while matching the glycosylation profile and function of the reference product. This study demonstrates the feasibility of engineering CHO cells to express mAbs with murine glycans, to reduce the residual uncertainty regarding biosimilarity, resulting in a higher probability of regulatory approval and potentially reduced costs and time in development. In parallel, a high-throughput CHO-specific assay was designed and evaluated to quantify N-glycosylation-related enzyme mRNA expression levels. This assay is referred to as the N-glycan enzyme mRNA expression profiler. Two different mAbs produced by two CHO cell hosts were iv investigated to assess the relationships between glycan mRNA levels and mAb glycan profiles. Genetic understanding of the N-glycosylation pathway in the CHO cells helped identify gene expression patterns that correlated to the N-glycan profile of the produced mAb. The results indicated that this assay could identify clones with desirable genetic makeups early in cell line development and aid in designing cell line engineering strategies to obtain the desired glycan profiles for both novel biologic and biosimilar development. v Lay Summary Biologics include protein-based medicines that are produced in living animal cells. The cells make the protein that is then extracted and purified to obtain the final biologic product. As the protein is generated, various sugars are attached depending on the cell type. In the early years of the biotechnology industry, many biologics were produced by mouse cells. However, since then, Chinese Hamster Ovary (CHO) cells have become the production cells of choice being far more robust and productive. However, the genetic codes of CHO and mouse cells are different, resulting in different attached sugars on the produced biologic. These differences in sugars are known to impact the effectiveness of treatments using the biologics. To address this, we have engineered the genetic code of CHO cells to express biologics with mouse-like sugars by overexpressing two key mouse enzymes in the sugar attachment pathways. After selection and optimization, two CHO cell lines were identified that met growth and productivity criteria while matching the sugar profile and function of the product that was produced in mouse cells. By better matching the sugar profiles in CHO cells, this technology can provide a higher probability of success as well as decrease development costs and time. vi Preface Each chapter included in this thesis corresponds to a manuscript that will be submitted for publication. I was the main contributor to the design and analysis of the project, supervised by James Piret. All the work was conducted at the Amgen, Thousand Oaks facility, and several Amgen colleagues contributed to my training. The detailed contributions were as follows: Chapter 1: The introduction of the thesis is written as a potential review article to be submitted to a peer-reviewed journal. I performed the literature search and drafted the manuscript, that was then reviewed by Elizabeth Pham, Julie Bailis, Malcolm Kennard and James Piret Chapter 2. The manuscript “Engineering Protein Glycosylation in CHO Cells to be Highly Similar to Murine Host Cells” will be submitted to a peer-reviewed journal. Shivani Gupta Prepared all experimental designs. Completed 70% of experimental work. Performed all data analysis and wrote the manuscript. Glenn Tan Helped design the vectors for the cell line engineering of the CHO cells Jiansong Xie and Devin Wakefield Trained me on immunofluorescence and confocal microscopy Bhavana Shah: Performed glycan and molecular attribute analyses (10% of the work). Coral Fung Shek: Performed the bioreactor cultures (5% of the work). Becky Chan: Performed the purification and Size Exclusion Ultra High Performance Liquid Chromatography (SE-UHPLC) (5% of the work) Drew Shami Trained me on the biological in-vitro bioassays Scott Kuhns: Edited manuscript vii Elizabeth Pham Edited manuscript Julie Bailis Edited manuscript Malcolm Kennard Edited manuscript James Piret: Contributed overall project supervision, edited manuscript Chapter 3. The manuscript “High-Throughput CHO Cell Specific Glycosylation Enzyme mRNA Expression Profiler.” will be submitted to a peer-reviewed journal. Shivani Gupta: Prepared all experimental designs. Completed 100% of experimental work. Performed all data interpretation and wrote all the text in the manuscript. Andres Plata Stapper: Contributed data analysis and edited manuscript Sumedha Roy: Contributed data analysis Malcolm Kennard Manuscript edition James Piret: Supervision of Shivani Gupta and edited manuscript Ethical approval All work performed with biohazard (level 2) in this thesis was approved by Amgen Biosafety Committee. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xiv Acknowledgments .................................................................................................................... xviii Dedication .....................................................................................................................................xx Chapter 1: Chinese Hamster Ovary Cell line Engineering And Biosimilar Production ........1 1.1 Biosimilar development .................................................................................................. 1 1.2 Factors influencing successful biosimilar manufacturing............................................... 7 1.3 The complexity of biologics and biosimilars .................................................................. 8 1.4 Analytical and biological characterization ...................................................................... 9 1.5 Glycosylation ................................................................................................................ 14 1.5.1 Sialylation ................................................................................................................. 16 1.5.2 High mannose glycan ................................................................................................ 17 1.5.3 Fucosylation .............................................................................................................. 21 1.5.4 Galactosylation ......................................................................................................... 24 1.6 Choice of host cell ........................................................................................................ 24 1.6.1 Cell line engineering ................................................................................................. 26 1.6.2 Cell line engineering toolbox .................................................................................... 27 Chapter 2: Engineering Protein Glycosylation in CHO Cells to be Highly Similar to Murine Host Cells ........................................................................................................................29 ix 2.1 Introduction ................................................................................................................... 29 2.2 Materials and Methods:................................................................................................. 32 2.2.1 Media and Reagents .................................................................................................. 32 2.2.2 Design of Vectors for CHO Cell Line Engineering .................................................. 32 2.2.3 Engineering of mAb Expressing CHO Cell Line to Express GGTA1 and CMAH .. 33 2.2.4 Single-cell cloning .................................................................................................... 34 2.2.5 Fed-Batch Cultures ................................................................................................... 34 2.2.6 Glycan and Molecular Attribute Analysis ................................................................ 35 2.2.7 Expression Analysis of CMAH and GGTA1............................................................ 36 2.2.8 Immunofluorescence and Confocal Microscopy ...................................................... 36 2.2.9 Stability Assessment ................................................................................................. 37 2.2.10 Bioreactor Cultures ............................................................................................... 38 2.2.11 Functional Bioassays ............................................................................................ 38 2.3 Results ........................................................................................................................... 40 2.3.1 Characterization of the RB Glycan Profile ............................................................... 40 2.3.2 Genetic Engineering of CHO Cells with Murine GGTA1 and CMAH Genes ......... 41 2.3.3 Overexpression of GGTA1 and CMAH Increases Both Fab and Fc alpha-gal and NGNA in Engineered CHO Cell Lines ................................................................................. 44 2.3.4 Localization of Exogenous GGTA1 and CMAH in Engineered CHO Cells............ 49 For each clone, upper images show staining for FLAG-GGTA1 and GPP130 with colocalized fluorescence in the Golgi. For each clone, bottom images show staining for GFP, FLAG-GGTA1, and CMAH-V5 with cytoplasmic localization of GFP and CMAH-V5. ......................................................................................................................................... 51 x 2.3.5 Cell Line Stability ..................................................................................................... 52 2.3.6 Evaluation of Two Selected Clones in Bench-Scale Bioreactors with Acceptable Productivity and Desired Product Attributes ........................................................................ 54 2.3.7 Clones A-29 and A-52 Retain Similar Biological Activity to the RB ...................... 59 2.4 Discussion ..................................................................................................................... 60 2.5 Conclusions ................................................................................................................... 62 Chapter 3: High-Throughput CHO Cell Specific Glycosylation Enzyme mRNA Expression Profiler ..........................................................................................................................................64 3.1 Introduction ................................................................................................................... 64 3.2 Methods and Materials .................................................................................................. 70 3.2.1 Media and Reagents .................................................................................................. 70 3.2.2 Selection of Genes and Probe Design ....................................................................... 70 3.2.3 Cell Culture ............................................................................................................... 71 3.2.4 Small-Scale Production ............................................................................................. 72 3.2.5 mRNA Expression Analysis, Normalization, and Statistics ..................................... 73 3.2.6 Glycan Analysis ........................................................................................................ 74 3.3 Results ........................................................................................................................... 74 3.3.1 Growth and Glycan Differences are Observed in CHO-A and CHO-B cell lines .... 74 3.3.2 CHO-A and CHO-B cell lines show genetic divergence .......................................... 81 3.4 Discussion ..................................................................................................................... 88 Chapter 4: Conclusion and Future Directions ..........................................................................88 Bibliography .................................................................................................................................96 xi List of Tables Table 1-1. List of FDA biosimilar submissions (those in blue font are approved) ........................ 2 Table 1-2. Structural and physiochemical methods used for biosimilar ABP 501 (Amjevita) and SB 5 in comparison to the Adalimumab reference biologic ......................................................... 11 Table 1-3. Key methods for mAb biological characterization and reference biologic comparison....................................................................................................................................................... 13 Table 1-4. Summary of sialylation improvement strategies based on host cell engineering reported previously. ...................................................................................................................... 18 Table 1-5. Summary of host cell line engineering strategies to increase high mannose glycans . 20 Table 1-6. Summary of host cell line engineering strategies for non-fucosylated biologic production ..................................................................................................................................... 22 Table 2-1. Glycan distribution from US and EU lots of the reference biologic (RB) that has Fc and Fab glycosylation sites ........................................................................................................... 41 Table 2-2. LC- MS/MS analysis performed on pepsin digest of purified protein on day 10 fed-batch harvest samples for clones A29 and A-52 for glycan analysis and molecular attributes (relative %).................................................................................................................................... 58 xii List of Figures Figure 1-1. Schematic representation of therapeutic mAb IgG structure and glycan composition........................................................................................................................................................ 15 Figure 2-1. Schematic of the relevant parts of the N-glycosylation pathway in murine myeloma cell lines and the CHO cells engineered in this work. .................................................................. 31 Figure 2-2. Strategy for biosimilar cell line development. ........................................................... 43 Figure 2-3. Profiling of GGTA and CMAH expression and functionality in cell clones. ............ 46 Figure 2-4. LC- MS/MS analysis was performed for glycan analysis on day 10 of fed-batch harvest samples. ............................................................................................................................ 48 Figure 2-5. Selection and workflow of the clone selection. ......................................................... 49 Figure 2-6. Localization of exogenous GGTA1 and CMAH in stably transfected clones. .......... 50 Figure 2-7. CL1-A clones #7, #33, #35 display expected localization of endogenously expressed GGTA1 and CMAH ...................................................................................................................... 51 Figure 2-8. Impact of generational age cell line stability attributes ................................................. Figure 2-9. Comparison of process performance across scales .................................................... 57 Figure 2-10. Summary of binding and biological activity of antibodies produced from clones A-29, and A-52 as compared to the reference biologic (RB) ........................................................... 60 Figure 3-1. Schematic of the N-glycosylation pathway................................................................ 66 Figure 3-2. Luminex xMAP bead technology MagPlex® and bDNA technology to quantify mRNA expression. ........................................................................................................................ 69 Figure 3-3. Schematic showing the study plan for comparing the two hosts CHO-A and CHO-B for two different mAb producing cell lines. .................................................................................. 76 xiii Figure 3-4. Performance of N-glycan enzyme mRNA expression profiler assay ........................ 77 Figure 3-5. Comparison of two host cell lines expressing mAb AMG 1 and AMG 2 in 10 day fed-batch. ...................................................................................................................................... 78 Figure 3-6. Glycan distribution profile of AMG 1 and AMG 2 expressed in CHO-A and CHO-B in 10-day fed-batch production. .................................................................................................... 80 Figure 3-7. Genetic divergence in the CHO-A and CHO-B host. ................................................ 82 Figure 3-8. Transcriptional similarity of cell lines from three individual time points -passaging, Day 8, and Day 10 of production. ................................................................................................. 84 Figure 3-9. Expression of genes involved in the biosynthesis of M5 glycan. .............................. 86 Figure 3-10. Expression pattern of genes on days 8 and 10 of production of AMG 1 and AMG 2 from CHO-A and CHO-B cell lines.............................................................................................. 87 xiv List of Abbreviations Abbreviation Definition ADCC Antibody-dependent cell-mediated cytotoxicity ADCP Antibody-dependent cell-mediated phagocytosis AUC-SV Analytical ultracentrifugation sedimentation velocity B4GALTs beta-1,4-galactosyltransferases BAP Biosimilar Action Plan bDNA Branched-DNA technology BHK Baby hamster kidney CCR4 C-C chemokine receptor CDC Complement-dependent cytotoxicity CDER Center for Drug Evaluation and Research CDRs Complementarity -determining regions CE Capillary electrophoresis CEX-HPLC Cation exchange-high-performance liquid chromatography Ch Constant domain, Heavy Chain CHO Chinese Hamster Ovary Cl Constant domain, Light Chain CMAH Cytidine monophospho-N-acetylneuraminic acid hydroxylase CQAs Critical quality attributes CRISPR Clustered Regularly Interspaced Short Palindromic Repeats CRISPR-Cas9 CRISPR-associated protein 9 xv CS Chondroitin sulfate DLS Dynamic light scattering DMP1 Dol-P-Man synthase 1 DMP2 Dol-P-Man synthase 2 DMP3 Dol-P-Man synthase 3 dPCR Digital PCR DS Dermatan sulfate DSC Differential scanning calorimetry Fc Fragment crystallizable FDA Food and Drug Administration FDR False Discovery Rate FTIR Fourier-transformed infrared Fuc Fucose FUT8 Fucosyltransferase 8 GAGs Glycosaminoglycan Gal Galactose GGTA1 Alpha-1,3-galactosyltransferase GlcNAc N-acetyl glucosamine GnT-III N-acetylglucosaminyltransferase III GS Glutamine synthetase Gusb Glucuronidase Beta HEK 293 Human embryonic kidney cells 293 xvi HILIC Hydrophilic interaction chromatography HM High Mannose HPLC High-performance liquid chromatography KO Knockout LC-MS liquid chromatography−MS mAb Monoclonal antibodies MALDI Matrix-assisted laser desorption/ionization MAM Multiattribute method Man Mannose MFI Mean fluorescence intensity MFI Micro-flow imaging MOA Mechanism of action MS Mass spectrometry NANA N-acetylneuraminic acid NGNA N-glycolyl neuraminic acid NGS Next-generation sequencing NS NanoString OE Overexpression PAT Process analytical technology PCA Principal component analysis PQAs Product quality attributes QbD Quality-by-design xvii qRT-PCR Quantitative real-time polymerase chain reaction QTPP Quality target product profile Quality Control Quality control RB Reference Biologic SE-UHPLC Size Exclusion Ultra High Performance Liquid Chromatography Sia Sialic acid SOP Standard operating procedures SRP Signal recognition particle ST3GAL1 ST3 beta-galactoside alpha-2,3-sialyltransferase 1 TALEN Transcription Activator like Effector Nuclease TBD To be determined Tbp TATA-binding protein UMG Uridine, manganese chloride and galactose UV-CD Near ultraviolet circular dichroism Vh Variable region, Heavy Chain Vl Variable region, Light Chain ZFN Zinc Finger Nuclease α(2,6) SiaT α2–6-sialyltransferase xviii Acknowledgments I want to thank the Department of Chemical & Biological Engineering, University of British Columbia, and Amgen Inc. for supporting this thesis. I am grateful for the support, understanding, and wisdom imparted by my thesis advisor, James Piret. I want to pay special gratitude to Chetan Goudar, who championed for me during my Ph.D. and was instrumental in the success of my thesis. I want to thank Prof. Charles Haynes for his valuable guidance and encouragement throughout the years. I appreciate the support and guidance from my Amgen managers, Kristi Daris, Julie Bailis, and Elizabeth Pham. They gave me the freedom to continue my thesis work and pushed me toward my thesis goals. I am grateful for Amgen's open and collaborative culture, where I had great scientific discussions with various subject matter experts, Zhongqi Zhang on glycosylation, Mark Daris on vector design, and Scott Kuhn on quality attributes. These discussions helped me make strategies that led to the success of the thesis. I am also thankful to Malcolm Kennard, UBC, for his critical feedback and editing of the thesis. Thank you to all my colleagues who have contributed to the discussion, work, and helped me learn new techniques, Glenn Tan, Jiansong Xie, Devin Wakefield, Coral Fung Shek, Becky Chan, Drew Shami, Andres Plata Stapper, and Sumedha Roy. In particular, I would like to thank Bhavana Shah for her hard work, time, and commitment to generating all the glycan analysis data. Without her contribution, the primary biological findings of this thesis would not have been possible. It was a pleasure to have brainstorming sessions with my colleagues at Amgen, Kim Le, Huong Le, Chun Chen, and Anupama Sahoo. They have pushed me to be stronger both personally and professionally and I am grateful to have been among them during this program. xix Lastly, I want to thank my family for their continuing support and encouragement. My boys, for their understanding when I say, "Mumma also has homework." Throughout the past several years, my husband, Ashish Agrawal, has patiently listened as I have talked his ear off on topics ranging from biosimilars, cell lines, cloning, and drug development, to experimental failure and success. I am thankful for his patience and unwavering support of me. xx Dedication I dedicate this thesis to my family: my parents, Anil and Rashmi; my mother-in-law, Bimla; my husband, Ashish; and my kids, Arnav and Ishaan. Your continuous support through the years has made this work possible. I also dedicate this thesis to all the great women who want to pursue higher education despite having family responsibilities and other commitments. I always say, "If I can do a Ph.D. while having a full-time job and giving birth to two boys, anyone can do it." Having supporting mentors, friends, and family is the key. 1 Chapter 1: Chinese Hamster Ovary Cell line Engineering And Biosimilar Production 1.1 Biosimilar development Biologics include large molecules produced through recombinant technology. These include hormones, fusion proteins, monoclonal antibodies (mAbs) and bispecific antibodies with complex and often varied physiochemical properties. Biologics play a critical role in the healthcare industry by providing a targeted means for treating cancer, autoimmune disorders, and other difficult-to-treat diseases. Insulin (Humulin from Genentech) was the first biologic, approved in 1982 by the United States Food and Drug Administration (FDA). Since then, FDA approvals have increased to an average of 12 biologics annually approved from 2015 through 2020 [1]. For example, in 2020, the FDA’s Center for Drug Evaluation and Research (CDER) approved 53 new molecular entities, of which 13, or 25%, were biologics excluding vaccines and gene therapy products. Notably, 9 of these 13 biologics were orphan drugs with breakthrough designation, thus reflecting the growing need for novel biologics to enhance the quality of life and increase patient survival [2]. Unfortunately, these therapies have a high cost associated with them. For example, it is estimated that the average cost of treatment with a biologic is ~$100,000/yr and even higher for those that treat cancer [3]. In 2010, the Biologics Price Competition and Innovation Act provided 12 years of patent and market exclusivity to new biologics [4]. Following patent expiry and other exclusivity rights, the market is then open for biosimilars to compete. Biosimilars are alternative versions of novel biologics with the same amino acid sequence and comparable safety and efficacy profiles. 2 Table 1-1. List of FDA biosimilar submissions (those in blue font are approved) Reference biologic Biosimilar name Biosimilar Manufacturer Approval Status Marketed Humira (Adalimumab) Amjevita (adalimumab-atto) Amgen Approved September 23, 2016 No Cyltezo (adalimumab-adbm) Boehringer Ingelheim Approved August 29, 2017, and interchangeable October 2021 No Hyrimoz (adalimumab-adaz) Sandoz Approved October 31, 2018 No Hadlima (adalimumab-bwwd) Samsung Bioepis Approved July 22, 2019 No Abrilada (adalimumab-afzb) Pfizer Approved November 18, 2019 No Hulio (adalimumab-fkjp) Viatris/Fujifilm Kyowa Kirin Biologics Approved July 6, 2020 No Yusimry (adalimumab-aqvh) Coherus Approved December 20, 2021 No AVT02 Alvotech/Teva FDA decision pending CT-P17 Celltrion FDA decision pending Eylea (Aflibercept) MYL-1701P Viatris FDA decision pending Avastin (Bevacizumab) Mvasi (bevacizumab-awwb) Amgen/Allergan Approved September 14, 2017 Yes Zirabev (bevacizumab-bvzr) Pfizer Approved June 27, 2019 Yes Alymsys (bevacizumab-maly) Amneal/mAbxience Approved April 14, 2022 Yes 3 MYL-1402O Viatris/Biocon FDA decision pending SB-8 Samsung Bioepis FDA decision pending BAT-1706 Bio-Thera Solutions FDA decision pending CT-P16 Celltrion FDA decision pending Epogen (Epoetin) Retacrit (epoetin-epbx) Pfizer (Hospira) Approved May 15, 2018 Yes Enbrel (Etanercept) Erelzi (etanercept-szzs) Sandoz Approved August 31, 2016 No Eticovo (etanercept–ykro) Samsung Bioepis Approved April 25, 2019 No Neupogen (Filgrastim) Zarxio (filgrastim-sndz) Sandoz Approved March 6, 2015 Yes TBD Apotex No FDA action reported Nivestym (filgrastim-aafi) Pfizer FDA approved July 20, 2018 Yes Releuko (filgrastim-ayow) Amneal/Kashiv Approved March 1, 2022 No TBD Tanvex Biologics FDA decision pending Remicade (Infliximab) Inflectra (infliximab-dyyb) Celltrion/Pfizer Approved April 5, 2016 Yes Renflexis (infliximab-abda) Samsung Bioepis/Merck Approved April 21, 2017 Yes Ixifi (infliximab-qbtx) Pfizer Approved December 14, 2017, It will not be marketed in the US No Avasola (infliximab-axxq) Amgen Approved December 6, 2019 Yes 4 Lantus (Insulin Glargine) Semglee Viatris Approved. Received interchangeable July 2021 Yes Rezvoglar (insulin glargine-aglr) Lilly Approved December 17, 2021 No NovoLog (Insulin Aspart) TBD Viatris FDA decision pending Neulasta (Pegfilgrastim) Ziextenzo(pegfilgrastim-bmez) Sandoz Approved November 5, 2019 Yes TBD Apotex FDA decision pending Udenyca (pegfilgrastim-cbqv) Coherus Biosciences Approved November 2, 2018 Yes Fulphila (pegfilgrastim-jmdb) Viatris/Biocon Approved June 4, 2018 Yes Nyvepria (pegfilgrastim-apgf) Pfizer Approved June 11, 2020 Yes Fylnetra (pegfilgrastim-pbbk) Amneal/Kashiv Approved May 27, 2022 No MSB11455 Fresenius Kabi FDA decision pending TBD Lupin FDA decision pending Lucentis (Ranibizumab) Byooviz (ranibizumab-nuna Samsung Bioepis Approved September 20, 2021 Yes FYB-201 Coherus Biosciences/Bioeq/ Formycon AG FDA decision pending 5 TBD Xbrane Biopharm FDA decision pending Rituxan (Rituximab) Truxima (rituximab-abbs) Teva/Celltrion Approved November 28, 2018 Yes Ruxience (rituximab-pvvr) Pfizer Approved July 23, 2019 Yes Riabni (trastuzumab-arrx) Amgen/Allergan Approved December 17, 2020 Yes Rixathon Sandoz FDA decision pending Herceptin (Trastuzumab) Trazimera (trastuzumab-qyyp) Pfizer Approved March 11, 2019 Yes Ogivri (trastuzumab-dkst) Viatris/Biocon Approved December 3, 2017 Yes Herzuma (trastuzumab-pkrb) Teva/Celltrion Approved December 14, 2018 Yes Kanjinti (trastuzumab-anns) Amgen/Allergan Approved June 13, 2019 Yes Ontruzant (trastuzumab-dttb) Samsung Bioepis Approved January 18, 2019 Yes TBD Tanvex Biopharma FDA decision pending HD201 Prestige Biopharma FDA decision pending TBD Sandoz/EirGenix FDA decision pending 6 The novel biologic to which a biosimilar is compared to is called the ‘innovator’, ‘reference product’ or ‘reference biologic’. However, biosimilars do not have to be completely identical to the reference biologic (RB) as they are often produced by competitors using different hosts and manufacturing processes [5]. For a biological product to be considered a biosimilar, it needs to have the same mechanism of action and be intended for the same indication as the RB. Approval is based on evidence that the biosimilar is not inferior to the RB in terms of safety and effectiveness. Nevertheless, compared to novel biologics, biosimilars have a significantly reduced development risk since the target is already known, and the molecular entities have previously been validated for safety and efficacy. In addition, biosimilar manufacturers can focus on a streamlined analytical characterization and safety dataset to enable abbreviated clinical testing [6]. Thus, the biosimilar development cost is considerably lower than that for a novel biologic. Therefore, with the possibility that market competition will drive down the costs of treatment, biosimilars are expected to offer increased access to biologics for a wider population worldwide [7]. Since the first biosimilar was approved by the FDA in 2015 (fligrastim-sndz, Zarxio, Sandoz), 31 biosimilars have been approved with 20 of these biosimilars launched by 2021 (Table 1-1). Furthermore, the FDA is actively updating policies to foster more rapid biosimilar development and approval. In 2018, the FDA established its Biosimilar Action Plan (BAP) with the key goal of accelerating biosimilar competition and uptake of biosimilars by physicians and patients [8]. A systematic review of 178 studies on switching from 12 RBs to biosimilars concluded no differences in immunogenicity, safety, and efficacy [9]. Thus, clinical and real-world data have consistently shown that patients can safely and effectively switch from RBs to their corresponding biosimilars. Remarkably, in July 2021, the FDA approved the first biosimilar product with ‘interchangeable’ status, Semglee, for diabetes treatment [10]. Later, in October 7 2021, another interchangeable designation was given to Cyltezo, the first interchangeable mAb (to Humira) [11]. Interchangeable status means pharmacists can prescribe Semglee or Cyltezo to patients previously taking the RBs Lantus or Humira without the need for a physician's prescription. The approval and designation of interchangeable status of these two biosimilars was an important step towards the broader adoption of biosimilars. In addition, it illustrates the FDA’s commitment to foster a competitive marketplace for the development of interchangeable biosimilars. 1.2 Factors influencing successful biosimilar manufacturing Biosimilar market success involves three main factors: 1) implementing a robust and high yield manufacturing process, 2) matching all critical quality attributes, and 3) enabling a competitive speed to market. This chapter will focus on the challenges for matching the critical quality attributes and manufacturing options that may accelerate the speed to market. The manufacturing of biosimilars is a highly complex process, starting with the selection of a host cell line. Most biopharmaceutical companies use their own platform host cell lines for which they already have established high-performing standard operating procedures (SOPs), genetic engineering approaches, and large-scale manufacturing. Thus, differences between the cell line used to manufacture the RB and the biosimilar can make the development of biosimilars challenging, mainly due to their inherently different genetic makeup that can result in different glycan profiles. These different glycan structures can cause changes affecting the protein half-life, binding activity, stability, and efficacy of a biologic [12, 13]. However, a biosimilar must sufficiently match the glycan structures to achieve optimal similarity to the original RB. Understanding these differences and modifying the production cell line for biosimilars can be challenging, time-consuming, and expensive. Therefore, to understand the industry-wide strategy 8 to develop biosimilars, in this chapter, I have reviewed the complexity of biologics and biosimilars and state-of-the-art analytical and biological characterization techniques employed by the industry to evaluate biosimilarity. In particular, the review focuses on glycosylation, the most challenging CQA that a biosimilar must meets. Additionally, I have discussed emerging technologies for cell line development and cell line engineering that can reduce cycle time for biosimilar development. 1.3 The complexity of biologics and biosimilars Making biologics, and, therefore, biosimilars start with stable transfection of the DNA sequence encoding the recombinant protein, followed by clonal selection of the cells. Next, the clones are characterized to identify the ones with the desired growth, productivity, and critical quality attributes (CQA)s. The post-translational modifications (PTMs), such as glycosylation, phosphorylation, and carboxylation, etc., can impact the biological function, efficacy, stability, and safety of the biologic. Finally, the final purified protein is made at the manufacturing scale from the top clone after sequential steps of purification, validation, and formulation [14]. Since biologics are produced in living cells, even minor variations in the process can lead to variations in the end product, for example, a lot to lot variability for the same product. Therefore, relying solely on publically available knowledge of the sequence and structure of the RB is not sufficient for making a biosimilar. Instead, biosimilar manufacturers have to extensively characterize variability arising in different lots of the same RB to understand the range of acceptable variability in the product quality attributes (PQA’s). A subset of the PQAs are known as CQAs. For example, specific glycans, aggregation, and chemical modifications can directly impact clinical outcomes. Based on the CQAs of the RB, a quality target product profile (QTPP) is established as the first step in making a biosimilar. In the QTPP, the CQAs are divided into three tiers (high, medium, and low) based on their impact on clinical safety and efficacy [15, 16]. QTPP serves as the 9 foundation and guide for selecting the host cell line, an expression construct, and subsequent manufacturing steps. Thus, to establish similarity, as per the QTPP, a biosimilar manufacturer needs to ensure comprehensive analytical and rigorous biological characterization to compare the proposed biosimilar and the RB throughout the manufacturing process. In fact, all regulatory agencies require the CQAs of the biosimilar to be maintained within defined QTPP ranges. 1.4 Analytical and biological characterization Analytical characterization involves the assessment of primary and higher-order protein structures, PTMs, and chemical modifications. In addition, biological characterization is critical to demonstrate the similarity in biological activity and potency of the proposed biosimilar with the RB. This data package informs the structure-function relationship that will support similarity for safety and efficacy as the RB. With the increased competition in the biosimilar space, there is a need for analytical techniques that are fast, flexible, cost-effective, and have high-resolution capability. Biosimilar manufacturers may implement different analytical characterization techniques compared to those used for the original RB. For example, Amjevita (ABP 501) and Hadlima (SB5) are biosimilars to Adalimumab, but the analytical methods used to show similarity are different (Table 1-2) [17, 18]. Mass spectrometry (MS) provides information on the primary structure of the protein as well as in-depth information of the PTMs of different isoforms by enzymatic digestion. In addition, some complementary techniques such as matrix-assisted laser desorption/ionization MALDI-MS, tandem MS (MS/MS), or MS followed by chromatographic or electrophoretic separation techniques like capillary electrophoresis (CE) or fluorescence detection, are used for high-resolution structural analysis of biologics and biosimilars [19]. However, these analytical techniques do not give site-specific modification information, critical for biologics with several glycosylation sites. Therefore, the multiattribute method (MAM) based on quantitative 10 liquid chromatography−MS (LC−MS) peptide mapping is widely used to quantify site-specific glycan modification, sequence variance (mutations and misincorporations), and impurities resulting from the host cell protein [20, 21]. The MAM approach provides comprehensive, high-fidelity information at the peptide level and is valuable for evaluating biosimilars in quality control (QC) and current good manufacturing practice (cGMP) environments [21]. Recently, advanced MS technology was coupled with process analytical technology (PAT), such as a small footprint PAT-LC/MS system enabling automated real-time monitoring of process performance and various PQAs [22]. In addition, in situ Raman spectroscopy is used for real-time monitoring of biologic site-specific glycosylation [23]. Similarly, the biopharmaceutical industry has adopted real-time monitoring technologies with integrated software for fast and accurate interpretation of the CQA data the past few years [24]. Such technologies can enable biosimilar manufacturers to have the high-throughput capability for clone screening for the desired QTPP profile and understanding process and CQA relationship enabling quality-by-design (QbD). Biological characterization comprises all receptor/target binding and cell-based assays of the protein to understand the mechanism of action (MOA) of the biologic and predict its clinical safety and efficacy. As biologics may have different MOAs, different assay formats are used for biological characterization for biosimilar development. For example, Kanjinti (ABP 980) and Ontruzant (SB3) are biosimilars to Trastuzumab. However, different functional assays were conducted using different methodologies to characterize each biosimilar and provide the totality of the evidence for the functional activity of the molecules (Table 1-3) [25, 26] [27]. 11 Table 1-2. Structural and physiochemical methods used for biosimilar ABP 501 (Amjevita) and SB 5 in comparison to the Adalimumab reference biologic Category ABP 501 (Amjevita) SB5 (Hadlima) Primary Structure Molecular weight of intact protein Non-reduced condition Non-reduced condition Molecular weight of reduced protein Reduced condition Reduced condition Protein sequence Reduced peptide map LC-ESI-MS/MS Disulfide structure Non-reduced peptide map Non-reduced peptide map N-glycan map Hydrophilic interaction liquid chromatography-High-performance liquid chromatography (HILIC HPLC) Procainamide labelling and LC-ESI-MS/MS. 2-AB labelling and HILIC-UPLC Isoelectric point Capillary isoelectric focusing Capillary isoelectric focusing Extinction coefficient Amino acid analysis Amino acid analysis, SEC/UV/MALLS/RI Higher-order Structure Secondary structure Fourier-transformed infrared (FTIR) Far- and near-UV CD, FTIR spectroscopy Tertiary structure Near ultraviolet circular dichroism (UV-CD) Intrinsic and extrinsic fluorescence spectroscopy H/DX-MS, Antibody conformational array Thermodynamic properties Differential scanning calorimetry (DSC) DSC Particles and aggregates Subvisible particle concentrations Light obscuration Subvisible particle concentrations and morphology Micro-flow imaging (MFI) Submicron particle profile Dynamic light scattering (DLS) and Field flow fractionation-light scattering (FFF-LS) Aggregate profile Analytical ultracentrifugation sedimentation velocity (AUC-SV) Product-related impurities High molecular weight Size exclusion-high-performance liquid chromatography (SE-HPLC) SE-HPLC, SEC/MALLS, SV-AUC Low molecular weight Reduced and non-reduced CE-SDS Reduced and non-reduced CE-SDS 12 Charge variants Cation exchange-high-performance liquid chromatography (CEX-HPLC) CEX-HPLC, icIEF Host-cell impurities Host-cell protein ELISA, 2D-LC-MS, and 2D precast SDS polyacrylamide Residual DNA by qPCR Biological Activity Tumor Necrosis Factor a (TNFα) Binding ELISA FRET Neutralization of TNFα Bioactivity Cell based assay NF-κB reporter gene assay Fc Receptor Binding AlphaLISA SPR FcRn binding Cell based binding assay AlphaLISA Effector function ADCC and CDC (cell based) ADCC and CDC (cell based) Capillary electrophoresis-sodium dodecyl sulfate (CE-SDS); Enzyme-linked immunosorbent assay (ELISA); -Two dimensional liquid chromatography coupled with on-line mass spectrometry involving data-independent (2D-LC-MS) MSE acquisition; Two-dimensional (2D); Quantitative polymerase chain reaction (qPCR); Sodium dodecyl sulfate (SDS); Signal recognition particle (SRP) 13 The different strategies employed by different manufacturers signify that there are multiple options to demonstrate biosimilarity with respect to the RB, irrespective of other biosimilars for the same RB. With the increasing competition in the biosimilar space, manufacturers must ensure they choose state-of-the-art technology that gives high-resolution data that can support the approval of biosimilars. In particular, glycosylation, a CQA, exhibits a heterogenous population of glycans at each glycosylation site of the biologic. Therefore, biosimilar manufacturers spend considerable time and resources to characterize and optimize glycans to demonstrate biosimilarity and ensure manufacturing consistency and product safety. Table 1-3. Key methods for mAb biological characterization and reference biologic comparison Key functional activity Binding Assays Target binding Cell based binding affinity (ELISA) Soluble receptor-based SPR Fcgamma receptors (FcγRIa, FcγRIIa, FcγRIIb, FcγRIIIa, FcγRIIIa ELISA/ SPR/Cell based FcRn binding SPR/ cell based binding assay Complement binding affinity ELISA/ SPR/Cell based Functional Assays Fab-Mediated In vitro target neutralization Receptor activation Receptor blockade Fc-Mediated ADCC CDC ADCP Complement activation Antibody-dependent cell-mediated cytotoxicity (ADCC); Complement-dependent cytotoxicity (CDC); Antibody-dependent cell-mediated phagocytosis (ADCP); Enzyme-linked immunoassay ELISA; Signal recognition particle (SRP) 14 1.5 Glycosylation The biosynthesis of biologics involves a highly complex glycosylation process. Therapeutic mAbs or derivatives have an asparagine-X-Serine/Threonine (Asn-X-Ser/Thr - where X is any amino acid except proline) consensus sequence for N-glycosylation at Asn297 in the heavy chain of the CH2 constant domain. Some therapeutic mAbs, such as Cetuximab, also bear additional glycosylation in the Fab domain (Asn88 of the VH region) (Figure 1-1A) [28]. In addition, Fc-fusion molecules such as Etanercept and B cell-activating factor receptor 3 (BR3)–Fc also possess O-linked glycans [29]. A 15 Figure 1-1. Schematic representation of therapeutic mAb IgG structure and glycan composition. (A) The IgG structure is comprised of two heavy chains and two light chains. Each heavy chain has the variable region (VH), and the constant domain (CH), and each light chain has the variable region (VL) and the constant domain (CL). N-glycans are attached at Asn297 in the heavy chain of the CH2 constant domain. (B) Composition of complex-type N-linked glycan on IgG. Glycosylation is the most challenging CQA to demonstrate biosimilarity. The N-glycosylation pathways have previously been reviewed extensively [30-32]. Glycans are synthesized in the endoplasmic reticulum and the Golgi apparatus in mammalian cells. During the post-translation modification of the biologic, the protein gets decorated with different glycans. The N-glycans of human mAbs are typically complex biantennary structures (Figure 1-1B). There is a conserved core structure composed of two N-acetylglucosamine (GlcNAc), three mannose-with branching mannose residues in both α(1–3) and α(1–6) linkage forming two antennae, and two GlcNAc residues. Depending on the host cell used to manufacture human mAbs, additional fucose (Fuc), galactose (Gal), bisecting GlcNAc, terminal GlcNac and sialic acid, including N-acetylneuraminic acid (NANA) or N-glycolylneuraminic acid (NGNA) residues may also be B 16 present on the core [33]. Unlike nucleic acids and proteins, the biosynthesis of glycans has no known template. The heterogeneous mixture of glycans is the consequence of genes encoding proteins required for precursor sugar activation, glycosyltransferases, glycan-modifying glycosidases, and transporters. Glycosylation results in heterogeneous glycoforms that differ from species to species, clone to clone, and can be affected by minor changes in the bioprocessing conditions, such as temperature, pH, and medium composition [34, 35]. Importantly, changes in the glycan profile can cause conformational changes that can impact the biologic or biosimilar’s binding affinity to the target receptors, which in turn can affect immune effector functions, including antibody-dependent cell-mediated cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC) and antibody-dependent cell-mediated phagocytosis (ADCP) [36]. Each of the terminal glycan moieties-fucose, bisecting GlcNAc, mannose, galactose, and sialic acid on the Fab or Fc region of the biologic can affect the stability and function of the biologic [13, 37, 38]. Therefore, it is essential to understand the impact of each terminal glycan moiety and the strategies to control the glycan expression on biosimilars on expressed mAb. 1.5.1 Sialylation Sialic acid (Sia), a nine-carbon sugar acid, has an electronegative charge and is the terminal glycan in the sugar chain of a therapeutic protein. Therefore, it masks the penultimate sugar, for example, galactose, from the recognition and uptake of hepatocyte asialoglycoprotein receptors [39]. As a result of its negative carboxylate functionality and its position, sialic acid plays a critical role in serum half-life and the physical, chemical, and immunogenic properties and efficacy of the respective glycoprotein [40]. Studies have demonstrated that increased Fc sialylation enhances the anti-inflammatory properties of a biologic [41]. In contrast, it is also shown that increased Fc sialylation can result in decreased Fc-mediated binding to antigens and Fc receptors and decreased 17 ADCC activity [30]. Desialylated Pertuzumab generated by enzymatic hydrolysis showed increased ADCC and CDC with improved FcγRIIIa binding affinity [42]. Cell line engineering approaches to manipulate enzymes to increase the sialic acid content of a therapeutic protein have been widely explored, as summarized in Table 1-4. 1.5.2 High mannose glycan High mannose (HM) glycans are afucosylated N-glycans characterized by unsubstituted terminal mannose sugars of five to nine residues attached to the GlcNAc2 core [43]. The HM glycans attached to the CH2 domain of therapeutic mAbs influence the pharmacokinetic properties due to a faster serum clearance rate [44, 45]. They are associated with increased antibody-dependent cell-mediated cytotoxicity (ADCC) activity, impacting the antibody effector function and decreased CDC (complement-dependent cytotoxicity) [45-47]. Therefore, the HM content of a therapeutic protein is an important quality attribute. Several methods for controlling HM levels have been explored in recent years (Table 1-5) [31]. 18 Table 1-4. Summary of sialylation improvement strategies based on host cell engineering reported previously. Class of enzyme Enzyme Symbol Engineering tool-OE (+) / KO (-) cDNA Source Cell line used/recombinant protein of interest Described effect References Sialyl transferases β1,4-galactosyltransferase and/or α2,3-sialyltransferase β(1-4) GalT and/or ST3GalI OE (+) Human CHO/TNFR-IgG Increase in α2,3-sialic acid [48] β1,4-galactosyltransferase and/or α2,3-sialyltransferase β(1-4) GalT and/or ST3GalI OE (+) Human CHO/EPO Increase in α2,3-sialic acid [49] α2,3 sialyltransferase IV and/or α2,6 sialyltransferase I ST3GalIV and/or ST6GalI OE (+) Mouse ST3GalIV and Rat ST6GalI CHO/IFN-γ Increase in α2,3- and α2,6-sialic acid [50] α2,6 sialyltransferase I ST6GalI OE (+) Rat HEK293/human acetylcholinesterase Increase α2,6-sialic acid [51] α2,6 sialyltransferase I and β1,4-galactosyltransferase 1 ST6GalI OE (+) Human CHO/IgG1 Increase α2,6-sialic acid [52] α2,6 sialyltransferase I ST6GalI OE (+) CHO CHOZNR GS/IgG Increase α2,6-sialic acid [53, 54] 19 α2,6 sialyltransferase I ST6GalI OE (+) CHO CHO/bispecific Ab* 70% increase in sialic acid content [54] Sugar Transporter CMP-sialic acid transporter CST/CMP SAT OE (+) CHO CHOK1/ IFNγ 4%–16% increase in sialic acid content [55] Sugar metabolism UDP-GluNAc2-epimerase/ N-acetylmannosamine-kinase GNE OE (+) Rat CHO ~ 6-fold increase in Sialylation of rhEPO [56] UDP-GluNAc2-epimerase/ N-acetylmannosamine-kinase, CMP-sialic acid transporter and α2,3-sialyltransferase GNE, CSTand α2,3-ST OE (+) rat GNE/MNK, human α2,3-ST, and CHO CST CHO/EPO -43% sialic acid content. [57] α2,6 sialyltransferase I Targeted demethylation of the highly methylated ST6GAL1 promoter CRISPR (-) CHO (DucB11) Increase α2,6-sialic acid [58] Cytidine monophospho-N-acetylneuraminic acid hydroxylase CMAH asRNA (-) CHO CHO HCP NGNA decrease [59] Sialidase lysosomal sialidase and plasma membrane sialidase Neu1 and Neu3 shRNA (-) CHO CHO/ IFNγ Up to 33% [60] All the examples listed in the table are stable transfections20 Table 1-5. Summary of host cell line engineering strategies to increase high mannose glycans Enzyme Symbol Overexpressed (+) / knockout (-) cDNA source Cell line used/ recombinant protein of interest Described effect References N-acetylglucosaminyl transferase I GnTI/Mgat1 - CHO CHO Lec 1 cell line yielding high mannose glycans [61, 62] - CHO Dhfr- CHO–DUKX Predominantly Man5- GlcNAc2 and they do not contain an α1,6-fucose linked to the innermost GlcNAc [46] - CHO CHOZN GS-/-*/ Anti-rabies IgG Predominant Man5 glycans [63] * glutamine synthetase (GS) 21 1.5.3 Fucosylation The glycans at the constant region (Fc) of a therapeutic mAb contain a core fucose residue in the α1,6-position linked to the core GlcNAc residue. Biosynthesis of core-fucosylated glycans results from the transfer of a fucose residue from GDP-Fucose mediated by α1,6-fucosyltransferase in the trans-Golgi [64]. Compared to fucosylated IgGs, non-fucosylated forms exhibit dramatically enhanced ADCC due to the enhancement of FcγRIIIa binding capacity and activating natural killer cells [65-67]. For example, defucosylated anti-CD20 IgG1 (Rituxan) shows >50-fold higher ADCC compared to fucosylated Rituxan, and defucosylated anti-Her2 IgG1 (Herceptin) shows enhanced ADCC with improved FcγRIIIa binding [65, 68]. However, >90% of IgGs produced in normal CHO cells are fucosylated [69], and several strategies have been developed to reduce fucosylation of IgGs (Table 1-6). Potelligent® Technology is used to generate 100% defucosylated antibodies by knocking out the Fut8 gene. In addition, Mogamulizumab, a mAb that targets the C-C chemokine receptor (CCR4), is generated using this technology [70]. 22 Table 1-6. Summary of host cell line engineering strategies for non-fucosylated biologic production Enzyme Symbol Overexpressed (+) / knockout (-) Cell line used/ recombinant protein of interest Result References GDP-mannose 4,6-dehydratase GMD - CHO LEc13 cell line defective in the conversion of GDP-mannose to GPD-fucose with enhanced antibody-dependent cellular cytotoxicity (ADCC) [71] α1,6 fucosyltransferase FUT8 siRNA KD (CHO)/DG44/ anti-CCR4 60% defucoslylated antibody with enhanced ADCC [72] GDP-fucose transporter GFT - CHO/ antithrombin III 75% reduction of Golgi-GDP fucose, resulting in 10–40% increase in defucosylated AT-III [73] Β 1,4-N-acetylglucosaminyltransferase III and/or α-mannosidase II GnTIII and/or ManII + HEK293-EBNA/ anti-CD20 Increased bisecting GlcNAc, increase in non-fucosylated and hybrid glycans [74] α1,6 fucosyltransferase and GDP-mannose 4,6-dehydratase FUT8 and GMD - CHO-DG44/mouse human chimeric IgG Fully non-fucosylated antibodies with enhanced ADCC [75] GDP-mannose 4,6-dehydratase GMD - CHO-DG44/ anti-CD20 rituximab Fully non-fucosylated antibodies with enhanced ADCC [76] α1,6 fucosyltransferase Fut8 ZFN (-) CHO (K1) Fully non-fucosylated antibodies [77] 23 20 genes ZFN KO (19 genes); overexpression ST6GalI CHOK1/EPO Decrease in fucosylation, galactosylation and microheterogeneity; increase in α-2,6-sialic acid [78] GDP-6-deoxy-d-talose synthetase GTS + CHO K1/IgG1 >80% afucosylated IgG1; enhanced FcγRIIIa binding and ADCC [79] 24 1.5.4 Galactosylation The Fc glycans produced in CHO cells may contain 0, 1, or 2 terminal galactose (Gal) residues (G0, G1, or G2). The terminal Gal plays an important role in ADCC [68]. In addition, it has been shown that changes in galactosylation impact CDC activity [80, 81]. For Rituximab, an increase in Gal residues increased CDC activity and complement C1q binding twofold [80, 82]. In another example, removing a Gal residue reduced the CDC activity in Alemtuzumab [83]. Enhanced ADCC activity was shown when a terminal Gal residue is specifically present on afucosylated IgG1 mAbs, whereas terminal Gal had minimal influence on ADCC when present on fucosylated glycans [37]. Interestingly, this variability in the glycan profile can result from differential expression of enzymes associated with glycosylation pathways (glycosyltransferases, glycosidases, transporters), nucleotide sugar, co-factor concentrations, and culture conditions [84, 85]. For biologics, specifically biosimilars, to achieve the desired glycan profile of the RB, an in-depth understanding of the glycosylation enzyme machinery of the host cell, coupled with the nucleotide sugar profile, is paramount. This knowledge can help the biosimilar manufacturer select the most appropriate host cell line, apply the necessary cell line engineering strategies, and perform media optimization with the QTPP in mind. 1.6 Choice of host cell Chinese hamster ovary (CHO) cells, baby hamster kidney (BHK) cells, NS0 myeloma cells, Sp2/0 hybridoma mouse cell lines, human embryonic kidney cells 293 (HEK293), and HT-1080 human cells are various mammalian cell lines that have been used for the production of approved biologics [13]. CHO cells have become the preferred hosts for biologic production. CHO cells offer robust and stable biologics production at manufacturing scales with a long history of 25 regulatory approvals [35]. The glycosylation profile of a biologic and a biosimilar depends upon the expression of multiple glycosyltransferase enzymes and nucleotide sugar transporters present in the endoplasmic reticulum and Golgi apparatus. Differences in the relative activity of these enzymes among host cells can result in significant variations in the glycosylation profile. CHO cells lack N-acetylglucosaminyltransferase III (GnT-III) expression and, therefore, do not transfer the bisecting GlcNAC to glycan structure [86]. CHO cells also lack a functional α1,3 fucosyltransferase, which prevents the addition of peripheral fucose residues [87]. Cell lines such as CHO and BHK also lack a functional α2–6-sialyltransferase (α(2,6) SiaT) and exclusively make α(2,3)-linked N-acetyl-neuraminic acid (NANA), unlike human and murine cells that express both α(2,3) SiaT and α(2,6) SiaT enzymes. In addition, the glycosyltransferase isoforms can be differentially expressed among cell lines. As an example, production of EPO in six different cell lines showed that CHO-K1 and BHK-21 cells sialylated recombinant EPO more effectively than HEK293, or NS0 cells, primarily due to lower expression of the sialyltransferase isoform genes ST3GalIII and ST3GalIV in HEK29 and NS0 cells [88]. Differences in sialic acid structure with N-glycolyl-neuraminic acid (NGNA) found in murine cell lines compared to human cell lines, is due to a mutation in the cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) enzyme [69]. NGNA is the predominant sialic acid in murine cells in addition to N-acetyl-neuraminic acid (NANA), but CHO-produced glycoproteins have NANA predominantly with only a small proportion ( 15% or less) of NGNA [89]. Similarly, murine cells express the enzyme α1,3 galactosyltransferase that generates Gal α1,3-Galb1,4-GlcNAc residues highly immunogenic in humans [87]. Since CHO and BHK cells have inactive α1,3 galactosyltransferase enzymes, these cell lines have are preferred cell line hosts.. 26 Several different CHO sublines have been derived from the original parental CHO cells, for example CHO-S, CHO-K1, CHO-KISV, DXB11, and DG44 (Golabgir 2016). A comprehensive transcriptome and proteome profiling of three of these cell lines revealed that CHO-K1 and DG44 have more complex tetra‐/tri‐antennary N-glycoforms than DXB11. In addition, biologics produced in the CHO-K1 cell line have more sialylated structures given high expression of relevant enzymes as well as substrates. Similarly, host-specific differences in the N-glycosylation pattern were found when comparing CHO-K1, CHO-S, and CHO-DG44 cell lines with the parent CHO cell line [90]. In a comprehensive RNA-seq-derived analysis of different CHO hosts, the differences in the N-glycan profile were attributed to differentially expressed genes [91]. The studies above show that glycosylation machinery is significantly different even across CHO cell lines variants, emphasizing the need to select host cells rationally for biosimilar cell line development. Knowing the differential expressed genes and the correlation to the desired glycan profile can help to design an appropriate engineering strategy for biosimilar development. 1.6.1 Cell line engineering When making a biosimilar, the manufacturer has two options: (i) use the cell line that was used to generate the original RB or (ii) use one of their existing platform cell lines. The advantage of the latter scenario is that the biosimilar manufacturer can utilize the knowledge and years of experience with their platform cell line to fine-tune the production process and engineer the host cell line to mimic the properties of the RB to achieve defined CQA ranges. However, significantly differentially expressed glycosylation enzymes between the different host cell lines might make it impossible for a manufacturer to achieve certain glycoforms with simple media optimization alone. To address thisproblem, it may be possible to glycoengineer the already established host cell line. The advantages of this glycoengineering are:- (i) extensive experience with the cell line (e.g., 27 media, yield, genetic characteristics), (ii) reduced direct cost (e.g. optimized medium compositions), (iii) well known and robust SOPs, (iv) established genetic engineering approaches, (v) ease of process validation and characterization, (vi) established scale up and scale down models, and (vii) established manufacturing facilities. A mechanistic understanding of the repertoire of glycosyltransferases and other glycan modifying enzymes is essential to guide cell line engineering and glycoengineering to tailor the glycan profiles of biologics [33]. Depending on the defined CQA, the manufacturer can choose a previously reported approach or develop a novel engineering approach based on the knowledge of the glycosylation pathway. Table 6 (a-c) summarizes host cell engineering approaches that have been reported and reviewed previously [33]. With the recent advances in cell line engineering approaches, biologic manufacturers have access to a toolbox of options to custom tailor their host cell lines. 1.6.2 Cell line engineering toolbox In recent years, next-generation sequencing (NGS) technologies combined with multi-omics approach have revolutionized molecular biology by comprehensively extracting genomic and proteomic data at a level that was not possible before. These state-of-the-art technologies have helped enhance the mechanistic understanding of different host cell metabolic pathways, including in industrial protein production. As a result, successful mammalian cell line engineering has been used to modify host cell metabolic pathways to increase productivity and improve post-translational modifications. The use of sequence-specific endonucleases such as meganucleases, Zinc Finger Nuclease (ZFN), and Transcription Activator-like Effector Nuclease (TALEN) has been the pillar for permanent knockout (KO) genome editing. Knockout of the α-fucosyltransferase FUT8 was demonstrated in the CHO-K1 cell line via the simultaneous ZFN- or 28 TALEN-mediated integration of an antibody cassette [92]. Unfortunately, these approaches are resource-intensive, time-consuming, and expensive [93]. Recently, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-associated protein 9 (CRISPR-Cas9) has emerged as a simple, efficient, and versatile genetic engineering tool used for both knockout and knockin of transgenes. Furthermore, CRISPR/Cas9 multiplexing to target several genes simultaneously has proven added advantages. For example, CRISPR/Cas9 mediated knockout of targeting FUT8, and the apoptosis pathway genes Bcl-2 homologous antagonist killer (BAK), and Bcl-2-associated X (BAX) generated an afucosylated antibody with improved production [94]. In another example, non-galactosylated glycoproteins were achieved by multiplexing ten genes in the CHO-S cell line used to produce erythropoietin (EPO) and Rituximab [95]. Biosimilars are a significant advancement in treating complex diseases at an affordable price for the healthcare system. They recognize and reward the innovator's upfront work and provide access to these therapies to patients worldwide. As discussed in this chapter, the science involved in creating biosimilars is not trivial. Biosimilar manufacturers have to adopt state-of-the-art technologies for analytical and functional characterization. In addition, it is critical to have an enhanced mechanistic understanding of the glycosylation pathway to make the desired glycan profile. In the next chapter, I demonstrate that with the knowledge of the glycan pathway and cell line engineering tools, it is possible to engineer CHO cells to express mAbs with murine glycans to facilitate the development of biosimilars that are highly similar to RBs expressed in murine cells. 29 Chapter 2: Engineering Protein Glycosylation in CHO Cells to be Highly Similar to Murine Host Cells 2.1 Introduction Recombinant therapeutic proteins, also known as biologics, have been successfully used to treat a wide range of diseases, including cancers, inflammatory disorders, and cardiovascular diseases [96]. Biosimilars with comparable efficacy and safety profiles have emerged as alternative options to innovator therapies. A biosimilar is a biologic that is highly similar to an originator biologic drug (“reference biologic” or RB) with no clinically meaningful differences from the RB. Regulations in the United States and European Union provide for abbreviated approval pathways for biosimilars, and the US Food and Drug Administration (FDA) [6] and European Medicines Agency (EMA) [97] have issued guidelines further describing regulatory expectations for biosimilars development [6]. Successful development of a biosimilar requires achieving product characteristics that are similar to the characteristics of the RB. In particular, protein glycosylation, the attachment of carbohydrates to a protein structure, can significantly impact binding affinity, immune effector functions, stability, and safety [13]. Thus, a critical component of biosimilar development is achieving a glycan profile that is highly similar to that of the RB. In many cases, optimization of the culture medium, such as by adding glycan precursors, can modulate glycosylation [84] [98]. However, achieving the similarity of the complex and varied post-translational modifications can be challenging, especially when different production cells and manufacturing processes are used for a biosimilar compared to the RB. Of note, Chinese Hamster Ovary (CHO) are currently the 30 host cells of choice industry-wide because of generally well-established procedures resulting in robust and stable production of biologics and because of the long-established history of regulatory approval for producing safe and efficacious biologics [33]. Increasingly, mechanistic understanding of the repertoire of glycosyltransferases and other glycan modifying enzymes have guided cell-line engineering to tailor the glycan profiles of biologics [33]. Here we investigated the production of a biosimilar candidate, a monoclonal antibody (“mAb”), for an approved recombinant human-mouse chimeric originator biologic produced SP2/0 cell line. This RB contains a conserved glycan fragment on both crystallizable fragment (Fc) and antigen-binding (Fab) regions. Notably, murine SP2/0 cells express GGTA1 and CMAH, which are responsible for the presence of α-Gal and NGNA, respectively (Figure 2-1). However, genes encoding these two enzymes are absent in CHO cells [35]. In addition, SP2/0 cells express both α-2,3-sialyltransferase (ST3GAL1) and α-2,6-sialyltransferase (ST6GAL1), the critical enzymes involved in the addition of the sialic group, N-acetyl-neuraminic acid (NANA or NGNA) to the mAb. In contrast, CHO cells lack functional ST6GAL1 and CMAH and, therefore, exclusively produce α (2,3)-linked NANA [35]. Hence, to produce a glycan profile in CHO cells that was similar to that of the RB, it was essential to compensate for differences in glycosylation enzymes between the murine and CHO cell lines. 31 Figure 2-1. Schematic of the relevant parts of the N-glycosylation pathway in murine myeloma cell lines and the CHO cells engineered in this work. Cytidine-5′-monophospho-N-Glycolylneuraminic acid (CMP-NGNA) is synthesized in the cytosol by the CMP-Neu5Ac hydroxylase (CMAH) hydroxylation of the N-acetyl group of Cytidine-5′-monophospho-N-acetylneuraminic acid (CMP-NANA). In the Golgi, CMP-NGNA is transferred to galactose-containing glycan substrates by sialyltransferases denoted by ST. The Golgi glycosylation enzyme GGTA1 transfers galactose from UDP-gal to the terminal galactose residue, producing the α-Gal glycan. In this work, the CHO cells were genetically engineered to express CMAH and GGTA1. The main hypothesis of this chapter was that we can genetically engineer CHO cells to express murine cell glycosylation in a recombinant antibody protein while maintaining the cell line production stability and product critical quality attributes required for biosimilar manufacturing. To investigate this goal, a CHO cell line expressing the RB mAb was engineered to produce murine-like glycans. Specifically, cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) and N-acetyllactosaminide alpha-1,3-galactosyltransferase (GGTA1) to obtain glycans with N-glycolylneuraminic acid (Neu5Gc; NGNA) and galactose-α-1,3-galactose 32 (alpha gal; α-Gal) were overexpressed. A diverse set of clones was then screened to find stable CHO production cell lines. These were tested in fed-batch cultures to determine their production performance. Then a wide range of assays was performed to determine whether the desired complex critical quality attributes (CQAs) of the target biosimilar candidate were maintained. 2.2 Materials and Methods: 2.2.1 Media and Reagents Company-proprietary vector and media (Amgen Inc., Thousand Oaks, CA) were used throughout the study unless otherwise specified. An Amgen generated CHO cell line expressing mAb with the same amino acid sequence as RB, designated CL1 was used. 2.2.2 Design of Vectors for CHO Cell Line Engineering The expression vector was selected for its high expression potential of target genes and efficient recovery of transfected cell lines through puromycin selection. Gene fragments for the mouse genes encoding the glycosylation enzymes CMAH and GGTA1 were synthesized (Geneart, Thermo Fisher. USA). In the initial vector strategy individual CMAH and GGTA1 vectors with fluorescent tags added to either the N-terminus or the C-terminus of the genes for convenient expression monitoring were designed. For final vector design strategy, the gene fragments were designed such that the CMAH and GGTA1 genes would be expressed in a bi-cistronic vector configuration, with or without fluorescent tags added to either the N-terminus or the C-terminus of the genes for convenient expression monitoring. Tags were also needed for immunofluorescence, as there were no commercially available CMAH and GGTA1 specific antibodies. Green fluorescent protein (GFP) with a 5' IRES (an internal ribosome entry site) was incorporated into all constructs to select GFP-positive cells expressing the genes of interest and 33 monitor their cellular localization. Lyophilized DNA fragments in an intermediate cloning vector were reconstituted in 8 µL of water and digested with SalI and NotI restriction endonucleases to release the desired fragment, then separated on an agarose gel (1%). Each fragment was ligated into the vector between SalI and NotI sites. The ligation reaction was used to transform chemically competent E.coli TOP10 cells. Two to four individual colonies were picked for Qiagen miniprep. After positive sequence verification of the miniprep, one clone was selected for large-scale plasmid preparation (Qiagen maxiprep). The whole plasmid was sequenced and confirmed to have the correct orientation. Additionally, a control vector with GFP expression cassette-only was generated. 2.2.3 Engineering of mAb Expressing CHO Cell Line to Express GGTA1 and CMAH Using electroporation, individual vector constructs were transfected into the CL1 cell line. The cells were cultured in a selective medium containing puromycin and methotrexate (MTX) three days post-transfection. Cells were cultured in either 24-well or 96-well plates (Corning, NY), 125 mL shake flasks (Corning, NY), or 50 mL TubeSpin® tubes (TPP, Trasadingen, Switzerland) at 370C, 5% CO2, and 85% humidity. Cells were passaged twice a week at 4x106- 5x106 cells/mL concentrations. Once pools achieved >90% viability and maintained consistent doubling times, they were evaluated in fed-batch cultures to evaluate critical quality attributes. The doubling time (td) was calculated based on the formula below: td = 𝑙𝑙𝑙𝑙2µ Where µ = (𝑉𝑉𝑉𝑉𝑉𝑉1/𝑉𝑉𝑉𝑉𝑉𝑉0)/(𝑡𝑡1 − 𝑡𝑡0) Where: VCD0: (cells/mL) is the viable cell density at the time of inoculation. 34 VCD1: (cells/mL) is the viable cell density in a culture sample. t0: (h) is the time of inoculation. t1: (h) is the time of sampling. 2.2.4 Single-cell cloning GFP-expressing CHO cells were sorted on a BD FACSAriaTM cell sorter (Becton Dickinson, Franklin Lakes, NJ) into 96-well plates with one cell per well to identify single-cell clones. Plates were imaged on a high-throughput CloneSelectTM Imager (Molecular Devices, Sunnyvale, CA) to verify clonality on Day 0 and monitor clone expansion on Days 7 and 14 post-sort before scaling up to 50 mL spin tubes. Once the clones achieved >90% viability and maintained consistent doubling times, they were evaluated in fed-batch cultures. 2.2.5 Fed-Batch Cultures Fed-batch cultivation was performed in 50 mL TubeSpin® tubes (TPP, Trasadingen, Switzerland) to determine the critical quality attributes (productivity, glycans and gene expression) exhibited by the clones. Briefly, individual tubes were set up with a working volume of 15 mL of production medium, incubated at 360C, 5% CO2, 85% relative humidity, and shaken at 225 rpm with a 50 mm orbital diameter in an ISF4-X incubator (Kuhner AG, Basel, Switzerland). Cultures were inoculated at 106 cells/mL and fed a bolus on Days 3, 6, and 8. Additionally, on day 3, 1 mM uridine, 2 µM MnCl2, and 5 mM galactose (UMG cocktail) were added to the bolus feed [99]. During cultivation, samples were taken on Days 0, 3, 6, 8 and 10 to analyze cell and mAb concentrations, mRNA expression, RNA sequence, and glycans. Cell concentrations and viability were measured using a Vi-Cell XR counter (Beckman Coulter, Brea, CA). Antibody concentrations were determined by protein A affinity high-performance liquid chromatography 35 (Protein A HPLC; WatersTM, Milford, MA). Cell culture supernatants were purified via affinity chromatography (Atoll GmbH, Weingarten, Germany). Size Exclusion Ultra High-Performance Liquid Chromatography (SE-UHPLC) was performed using WatersTM Acquity H-Class UPLC® with an Acquity BEH 200 column, 200Å, 1.7 μm, 4.6 mm X 150 mm (WatersTM, #186005225) monitoring at 220 nm with a tubular ultraviolet (TUV) lamp. 2.2.6 Glycan and Molecular Attribute Analysis Liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis was performed for glycan and molecular attribute analysis following titer determination and protein purification. The pepsin digest of purified protein was optimized using appropriate enzyme ratio and digestion times to release one prominent glycopeptide. The digest was prepared either manually or by an automated method using Agilent’s UHPLC (1290 Infinity) autosampler. The separation of glycopeptides derived from the Fab and Fc were optimized to determine the site-specific glycan distribution with a short HPLC gradient for peptide mapping. A high-resolution mass spectrometer (Thermo Scientific LTQ-OrbitrapTM or Themo Scientific QE) was used to generate MS/MS fragments of glycopeptides. The glycopeptides at each site were identified and quantified from the LC-MS/MS data using the MassAnalyzer program [100]. MassAnalyzer identified peptides by comparing the experimental MS/MS to their theoretically predicted MS/MS. Algorithms used by MassAnalyzer for automated peptide identification and quantitation have been described in literature [101] For molecular attribute analysis, the sequence of the RB is known. The theoretical masses of unmodified and post-translationally modified peptides were calculated using bioinformatics tools [102]. Site specification modifications were confirmed based on comparing MS/MS fragmentation data with predicted fragmentation data [103]. 36 2.2.7 Expression Analysis of CMAH and GGTA1 For mRNA expression analysis, a QuantiGeneTM RNA MultiPlex (Thermo Fisher Scientific) gene expression assay was used [104]. Briefly, cell pellets from 5 x 105 viable cells were lysed using 1X lysis buffer, supplemented with proteinase K (stock concentration 50 mg/mL) and incubated at 50°C for 1 hour. Cell lysates were stored at -800C. A gene-specific probe set and a capture bead set (Panel 41211-CMAH, GGTA1, Gusb, Tbp) were custom synthesized (Affymetrix, ThermoFisher). According to the manufacturer's protocol, frozen lysates were thawed and processed for mRNA quantification in 96 well plates. The plates were read using the FLEXMAP 3D® (ThermoFisher Scientific) instrument and xPONENT® 4.2 software. Signals recorded as the mean fluorescence intensity (MFI) generated from each bead are proportional to the amount of each mRNA captured on the surface of each generated specific probe set. Next, gene expression was normalized using the expression of the constitutive genes TATA-binding protein (TBP) and glucuronidase beta (GUSB). Using normalized mean fluorescence intensity (MFI) data, the fold expression in engineered clones was compared to control clones generated by transfecting the CL1 cell line that contained the GFP vector alone. 2.2.8 Immunofluorescence and Confocal Microscopy To determine if the tagged exogenous enzymes, GGTA1 and CMAH, expressed as expected in the engineered host, immunofluorescence staining and confocal microscopy were performed. Primary antibodies were: anti-GFP (ab13970; Abcam; 1:1000), anti-FLAG (14793, Cell Signaling Technology; 1:800), anti-V5 (R960-25, ThermoFisher; 1:500), and as a Golgi marker anti-GPP130 (610823, BD Biosciences; 3:1000). Corresponding secondary antibodies were: Alexa Fluor 488 F(ab')2-donkey anti-chicken IgY (703-546-155, Jackson ImmunoResearch; 37 1:500), Alexa Fluor 568 F(ab')2-goat anti-rabbit IgG (A-21069, ThermoFisher; 1:500), and APC F(ab′)2-goat anti-mouse IgG (A10539, ThermoFisher; 1:250). Cells were first rinsed with phosphate buffered saline (PBS, pH 7.4) and fixed in 4% paraformaldehyde (diluting 8% PFA, 157-8-100; Electron Microscopy Sciences) for 15 minutes. Rinsing twice with PBS, cells were then permeabilized using 0.05% Triton X-100 (93418; Sigma-Aldrich) in PBS for 15 minutes. After again rinsing twice with PBS, a blocking step was performed using equal parts mouse Fc and human Fc (at 1:100) in 1% goat serum in PBS for 30 minutes. Incubation with primary antibodies was then carried out in 1% goat serum in PBS for 2 hours. After rinsing twice with PBS, cells were incubated with secondary antibodies and the DNA stain Hoechst (1:1000) in 1% goat serum in PBS for 30 minutes. Cells were rinsed twice with PBS and stored in PBS until ready for imaging. Images were captured on an Opera Phenix High Content Screening system (PerkinElmer) using a 63x objective with z-stack acquisition. 2.2.9 Stability Assessment To evaluate the stability of critical quality attributes (transgene, expression, and product quality) as a function of in vitro cell age a stability assessment was performed. For a stability study, a subset of 20 randomly chosen clones with high productivity (>1g/L) and varying glycan profiles was selected. A single frozen cell bank vial for each of the 20 clones at predetermined population doubling levels (PDLs) were thawed and cultured in a growth medium containing MTX and puromycin. Cells were frozen at 15, 30, 50, and 65 PDLs were thawed and propagated in the selective medium for an additional 25 PDLs to emulate the large-scale seed train. Subsequently, these clones were tested in 10 mL fed-batch cultures in 50 ml spin tubes using a nonselective medium ( no MTX and puromycin) in spin tubes. 38 2.2.10 Bioreactor Cultures All benchtop studies were performed in 3 L stirred tank bioreactors (Applikon, Foster City, CA) with an initial 1.5 L working volume in fed-batch mode. Cultures were fed a bolus on Days 3, 6, and 8. Additionally, on Day 3, UMG was added to the bolus feed. Bioreactor parameters, dissolved oxygen (DO) concentration, pH, and temperature, were monitored and controlled using an Applikon in-Control (Applikon, Delft, Netherlands) controller. The pH was controlled through CO2 or 1 M Na2CO3 addition. Dissolved oxygen was maintained by sparging oxygen through a drilled pipe and a sintered sparger. Viable cell density and viability were measured using a Cedex HiRes Analyzer (Roche CustomBiotech, Penzberg, Germany). Metabolites, glucose, lactate, glutamate, glutamine, Na+, K+, Ca2+, and ammonia were analyzed using the Cedex Bio HT Analyzer (Roche CustomBiotech, Penzberg, Germany). Osmolality was measured based on freezing point depression using the Advanced Instruments OsmoPRO (Advanced Instruments, Norwood, MA). External pH, pCO2 and pO2 were analyzed using a Siemens RAPIDLab 1260 (Siemens, Forchheim, Germany). mAb titers were measured using an HPLC Protein A method, and mRNA was profiled using the QuantiGene® based assay described above. 2.2.11 Functional Bioassays The potency of the mAb produced by selected clones- A-29 and A-52 from the small-scale spin tube and benchtop bioreactor were assessed in antibody-dependent cellular cytotoxicity (ADCC) assays and for binding to FcγRIIIa (158V) and cell surface antigen. ADCC was evaluated using the Eurofins DiscoverX Killing Immune-Lysis Reaction (KILR) assay method [105]. Briefly, the assay used the commercially available target cells, U2OS KLR, (Eurofins DiscoverX, Fremont, CA, USA) that overexpress the cell surface target and a KILR reporter protein, a 39 housekeeping protein tagged with enhanced ProLabel (ePL),and a β-galactosidase (β-gal) enzyme fragment. The target cells were opsonized with a concentration range of purified mAb and RB. The opsonized target cells were incubated with the natural killer effector cell lines, NK92-M1, stably transduced to express human CD16. The tagged KILR protein released from lysed target cells in the media was detected by detection reagents containing the enzyme acceptor fragment of the β-gal reporter. This leads to the formation of the active β-gal enzyme that hydrolyzes the substrate to give a chemiluminescent output, detected on a luminometer. Dose-response curves were fit to a four-parameter logistical regression line using SoftMax® Pro Software (Molecular Devices, Sunnyvale, CA, USA), and relative binding was determined by comparing the half-maximal effective concentration (EC50) of a sample to that of a reference standard. All samples were tested in triplicate for each assay, with the mean values reported as relative ADCC activity. FcγRIIIa (158V) activity was determined using AlphaLISA assays (Perkin Elmer). FcgRIIIa (158V) GST fusion protein and biotinylated human IgG1 was generated at Amgen, Inc., USA. The assay was performed as described previously [106]. The sample binding relative to the RB was determined using four-parameter logistical regression with SoftMax® Pro Software (Molecular Devices). Results were reported as percent relative binding values. Relative cell surface target binding was determined using flow cytometry. Briefly, CHO cells overexpressing the cell surface target were incubated with increasing concentrations of purified mAb from the two selected clones and RB at 40C for one hour. After washing with wash buffer (phosphate buffer saline (PBS) with 2% fetal bovine serum (FBS), cells were incubated with secondary antibody, allophycocyanin (APC) AffiniPure Goat Anti-Human IgG, Fcγ fragment specific (Jackson Immuno Research Laboratories) for 30 minutes at 40C. Cells were then washed 40 and resuspended in a wash buffer containing the dead cell stain SytoxTM Blue (Thermo Fisher). Samples were analyzed on a Becton Dickinson LSR FortessaTM Cell Analyzer and the APC MFI was analyzed using FlowJoTM software (FlowJo, LLC). Dose-response curves were fit to a four-parameter logistical regression line using SoftMax® Pro Software (Molecular Devices, Sunnyvale, CA, USA). Relative binding was determined by comparing the half-maximal inhibitory concentration (IC50) of a sample to that of a reference standard. All samples were tested in triplicate for each assay, with the mean values reported as relative target binding. 2.3 Results 2.3.1 Characterization of the RB Glycan Profile The type and distribution of glycorms in the RB were analyzed for different lots of RB (Table 2-1). Glycans located at the two glycosylation sites on the Fab and Fc were a complex mixture of glycoforms. Besides the terminal glycoforms commonly found on antibodies derived from CHO-based processes, namely high mannose (HM), terminal sialic acid, N-Acetylneuraminic acid (NANA), galactose, and fucosylated species, the RB also contained Fab and Fc terminal glycans characteristic of murine cell products, particularly Galα-1,3-Gal (α-Gal) and N-glycolylneuraminic acid (NGNA). Interestingly, the Fc glycans were mainly complex N-glycans terminating with GlcNAc and galactose residues, while the Fab glycans were predominantly α-glycosylated structures. Nearly all glycans contained the core α1,6-fucose. 41 Table 2-1. Glycan distribution from US and EU lots of the reference biologic (RB) that has Fc and Fab glycosylation sites % Glycan Distribution Fab Fc afuc (Complex+Hybrid) 0.6 - 1.1 1.7 - 2.1 HM (M5-M8) 0.8 - 1.4 7US - 12EU Terminal Galactose (all) 85 - 89 43EU - 48US α-Gal 76 - 89 2.6 - 3.2 Sialylated (NANA) 1.0EU - 3.9US 0 Sialylated (NGNA) 29EU - 39US 0.3 - 0.4 The subscripts indicate the EU or US RBs where the results were different. α-Gal and Sialylated (NGNA) are not made in CHO cell lines. N-Acetylneuraminic acid (NANA), N-glycolylneuraminic acid (NGNA 2.3.2 Genetic Engineering of CHO Cells with Murine GGTA1 and CMAH Genes CHO cells were engineered to express relevant glycosylation enzymes that would produce proteins with glycan profiles similar to that of the murine-derived RB. As overexpression of ST6GAL1 can lead to undesirable elevated sialylation on both the Fc and Fab sites [53, 54, 107], we only introduced CMAH and GGTA1. GGTA1 transfers galactose from uridine diphosphate galactose (UDP-gal) to the terminal galactose residue of N-acetylgalactosamine, producing α-Gal, the dominant glycan form in the RB. There was also a possibility that ST6GAL1 would compete with GGTA1 for the same terminal β-1,4-linked galactose to transfer sialic acid from CMP-sialic acid. Finally, it was considered that adding a third glycosylation-associated gene could adversely impact cell line stability and productivity due to metabolic overload. 42 Native GGTA1 is a type II Golgi membrane-bound protein with a short amino-terminal cytoplasmic tail, a single transmembrane domain, and a large COOH-terminal domain in the Golgi lumen [108]; whereas CMAH is present in the cytoplasm. Glycosylation follows a sequential, step-wise pathway. Therefore, the relative localization of glycosylation-related enzymes significantly impacts the types of glycan structures produced by the cell. Thus, a vector was designed to express GGTA1 and CMAH with N- and C-terminus tags (Flag and V5) to allow monitoring of proper subcellular localization. (Construct A; Figure 2-2A). As the tag sequences could potentially interfere with the proper folding or activity of the enzymes, a vector containing CMAH and GGTA1 without tags was also designed (Construct D; Figure 2-2B). In both vectors, CMAH and GGTA1 were joined by the self-cleaving peptide furin-2A, to maintain the 1:1 stoichiometry of expression of the two genes. The 2A peptide is an 18 amino acid long peptide. The 2A linked genes are expressed in one single open reading frame and ‘‘self-cleavage’’ occurs co-translationally between the last two amino acids, glycine and proline, at the C-terminus of the 2A polypeptide, giving rise to equal amounts of co-expressed proteins. Insertion of a furin recognition sequence between the first gene (CMAH) and 2A enables the removal of the 2A residues from the upstream gene. Vectors also included GFP flanked with an N-terminal IRES downstream of the gene of interest. The presence of IRES allowed the expression of both upstream genes and GFP from a single promoter. Therefore, visualization of GFP in a cell would signify the presence of both CMAH and GGTA1 in 1:1 stoichiometry. Additionally, a control vector that contained GFP alone was designed as control (Construct E; Figure 2-2C). The CHO cell line CL1 was then transfected with the three constructs A, D and E, generating pools CL1-A, CL1-D, and the control, CL1-E. The stably transfected pools were 43 evaluated in a fed-batch production assay for various critical quality attributes, e.g., productivity, glycan, and expression of engineered genes. Optimal culture performance, including end-of-production titer, viable cell density (VCD), and cell viability, was observed from all pools (data not shown). LC-MS/MS analysis of purified mAbs confirmed an up to 50 and 20-fold increase in the relative percentage of Fab α-Gal and NGNA glycans, respectively, when compared to the control pools that only expressed GFP, indicating that the engineered cell lines successfully expressed the intended enzymes (Figure 2-2D). Figure 2-2. Strategy for biosimilar cell line development. Both constructs A and D contain mouse CMAH and GGTA-1 . The two genes were joined by the furin 2A self-cleaving peptide. A) In Construct A, CMAH was fused with a C-terminal V5 tag and GGTA1 was fused with 44 an N-terminal FLAG tag. Green fluorescent protein (GFP) with a 5’ IRES (internal ribosome entry site) was incorporated in Constructs A and D. B) Construct D was similar to Construct A without Flag and V5 tags. C) Construct E was engineered to express GFP only as control. D) LC- MS/MS analysis was performed to determine the glycan profiles (relative %) from day 10 fed-batch harvest samples from the cell pools generated by transfection of constructs A and D (CL1-A and CL-D) respectively. Fold change of % relative glycans Fab (α-Gal and NGNA) and Fc (α-Gal and NGNA) when compared to CL1-E control pools. The error bars of the fold changes were derived from the relative errors of control and pools generated from different transfections of individual vectors (N=2). 2.3.3 Overexpression of GGTA1 and CMAH Increases Both Fab and Fc alpha-gal and NGNA in Engineered CHO Cell Lines Single cell clones were identified by FACS sorting on GFP positivity and scaled up to 20mL scale. A total of 72 clones with an optimal and consistent doubling time (<30 hours), even in the presence of double selection (MTX and puromycin), were selected for further evaluation. The 72 single clones comprised 67 experimental clones (CL1-A and CL1-D) and 5 control clones (CL1-E)). These clones were evaluated in a 10-day fed-batch culture for growth, productivity, and glycan profile. To increase both Fab and Fc galactosylation, a bolus feed of UMG substrates was added on Day 3. During the production run, time-course mRNA profiling (Days 3, 6, 8, and 10) revealed a consistent mRNA pattern of the exogenous genes CMAH and GGTA1 throughout the process. As expected, control clones did not express CMAH and GGTA1, whereas experimental clones expressed 2 to 43-fold higher normalized mRNA levels of CMAH and GGTA1 normalized to the constitutive genes Gusb and Tbp (Figures 2-3A and B). Using a self-cleaving furin-2A, a bicistronic configuration of the two genes successfully demonstrated the precisely controlled and reliable 1:1 stoichiometry of CMAH and GGTA1 expression (Figure 2-3C). Comparable growth 45 patterns between control and experimental clones were observed, demonstrating that expression of exogenous CMAH and GGTA1 was not detrimental to cell viability or growth (data not shown). Only clone 31 showed a poor growth profile throughout the fed-batch experiment. Clone 31 was able to reach a maximum viable cell density of 5x106 cell/mL during the fed-batch. In contrast, all other clones ranged from 10-26x106 cell/mL. This observation could be explained by the exceptionally high mRNA levels of GGTA1 and CMAH, potentially leading to increased metabolic load and, in turn, poor growth performance (Figure 2-3C). Next, the glycan profile on the Day 10 harvested mAb was assessed by measuring the terminal glycan sugar moieties. Despite the 1:1 stoichiometry of CMAH and GGTA1 mRNA expression, the majority of clones had high α-Gal and lower NGNA, which was consistent with the RB profile. Specifically, most experimental clones had high levels of Fab α-Gal (ranging from 0.9% - 98%), with overall lower levels of Fab NGNA (ranging from 0.2% - 87%), similar to the RB (Figures 2-4A and B). The reason for high α-Gal and lower NGNA could be due to a number for reasons. Given the complexity of the N-glycosylation pathway that includes enzymes, isozymes, nucleotide sugar substrates, transporter and cofactors that play critical roles, the overexpression of an enzyme does not necessarily translate into functionality. Also, it is well known that mRNA levels and their expressed protein levels can be poorly correlated [109]. Upon collectively looking at the Fab α-Gal, NGNA and NANA, an inverse relationship was observed (Figure 2-4C). In contrast of Fab glycans, Fc α-Gal ranged from 0%-25%, with a negligible expression of Fc NGNA (Figures 2-4D and E). The high level of Fabs α-Gal and NGNA dramatically increased the possibility of a clone producing an antibody within the RB glycan profile range. The selection of clones and workflow is shown in Figure 2-5. 46 Figure 2-3. Profiling of GGTA and CMAH expression and functionality in cell clones. (A, B) Day 8 mRNA transcript expression of overexpressed genes in CHO CL1 derived clones. The mRNA expression was normalized to constitutive genes Gusb and Tbp. Overexpression of (A) GGTA1 and (B) CMAH was determined in fed-batch cultures across multiple clones generated from Constructs A and D compared to Control E. (C) Bi-cistronic vector configuration resulted in linked expression of both GGTA1 and CMAH. Day 8 mRNA transcript expression of overexpressed genes in CHO CL1 derived clones. The mRNA expression was normalized to constitutive genes Gusb and Tbp. Clone 31 is shown in the red circle. 47 48 Figure 2-4. LC- MS/MS analysis was performed for glycan analysis on day 10 of fed-batch harvest samples. (A) Fab-α-Gal and (B) Fab-NGNA was observed in clones overexpressing the two genes of interest compared to the controls. (C) Inverse relation between Fab % α-Gal-NGNA-NANA was observed. (D) Compared to the control (E clones), an increased level of Fc-α-Gal was observed in various clones overexpressing the two genes of interest. (E) There was a negligible presence of Fc-NGNA for all the clones. Each data represents culture samples run in duplicate; error bars represent standard deviations. 49 Figure 2-5. Selection and workflow of the clone selection. 2.3.4 Localization of Exogenous GGTA1 and CMAH in Engineered CHO Cells Confocal imaging was used to confirm the proper subcellular localization of endogenously expressed GGTA1 in the Golgi membrane and CMAH in the cytoplasm. Due to the lack of commercially available antibodies against GGTA1 and CMAH, FLAG-GGTA1 and CMAH-V5 fusion protein constructs were utilized (Figure 2-2A). From CL1-A, four experimental clones were randomly selected, and from construct CL1-E, one control clone was selected. All clones were stained for the cis-Golgi membrane protein GPP130. In the CL1-E control clone, GGTA1 (Figure 2-6A, top row) and CMAH (Figure 2-6A, bottom row) were not detected, with only GFP signal visible. In the CL1-A clones, FLAG-GGTA1 was detected in the Golgi region and colocalized with GPP130 (Figure 2-6B, top row), whereas CMAH-V5 was found localized through the 50 cytoplasm (Figure 2-6B, bottom row). Images from the other three experimental clones are provided in Figure 2-7. These results are consistent with other reports in the literature [110, 111], indicating that the localization of the exogenous enzymes GGTA1 and CMAH was similar to that of the endogenous enzymes. Figure 2-6. Localization of exogenous GGTA1 and CMAH in stably transfected clones. (A) CL1-E clone #E2 shows only the presence of GFP. Top row: staining with mouse monoclonal anti-FLAG M2 and rabbit polyclonal anti-GPP130 detects GPP130 but not FLAG-GGTA1. Bottom row: additional staining with mouse anti-GFP and rabbit monoclonal anti-V5 (instead of anti-GPP130) detects GFP but not CMAH-V5. (B) Expression of FLAG-GGTA1 and CMAH-V5 is detected in CL1-A clone #31. Top row: Colocalization of FLAG-GGTA1 and GPP130 is observed. . Bottom row: staining for GFP and V5 show strong cytoplasmic localization for most cells. Hoechst was used to visualize cell nuclei in all cases. All scale bars are 10 μm. 51 Figure 2-7. CL1-A clones #7, #33, and #35 display the expected localization of endogenously expressed GGTA1 and CMAH For each clone, upper images show staining for FLAG-GGTA1 and GPP130 with colocalized fluorescence in the Golgi. For each clone, bottom images show staining for GFP, FLAG-GGTA1, and CMAH-V5 with cytoplasmic localization of GFP and CMAH-V5. 52 2.3.5 Cell Line Stability A critical criterion for the successful generation of an engineered cell line is the stability of the transgene expression upon extended culture [112]. Stability is generally evaluated for a course of 50-60 PDLs, which is equivalent to the time required to generate a master cell bank (MCB), a working cell bank (WCB), and execute at a commercially relevant production scale (Figure 2-8A). [113] [114]. To investigate the genotypic and phenotypic stability of the engineered cell lines, a subset of 20 randomly chosen clones (18 clones from the CL1-A and CL1-D cell lines with high productivity (>1g/L) and varying glycan profiles and 2 control clones, CL1-E clones E-4 and E-5) were selected for a stability study using conditions mimicking typical commercial manufacturing conditions (Figure 2-8A). Cell banks for different clones at 15, 30, 50, and 65 PDLs were thawed and carried for an additional 25 PDLs to mimic the manufacturing scale seed train and then used in small-scale fed-batch experiments in parallel. The coefficient of variance (%CV) was evaluated in various cell performance and critical quality attributes, including growth, titer, mRNA of exogenous genes and glycan profile, across PDLs. After extensive screening and characterization, 7 of 20 clones demonstrated <20% CV for titer and <25% CV for other attributes, including mRNA expression of exogenous genes and the desired glycan profile (Figure 2.8B). The data suggest that only less than half of the total 20 clones were stable, emphasizing the importance of a thorough stability study. Two of the seven stable clones, E-4 and E-5 were stable and had lesser %CV for attributes compared to the other five stable experimental clones (Figure 2.8B). As experimental clones are overexpressing two exogenous genes, a metabolic overload or other impacts may result in comparatively more %CV compared to control clones. 53 Cell line stability for 20 clones was assessed by evaluating changes in titer, mRNA expression of the genes of interest, and glycan profile over defined duration and intervals of Population Doubling Levels (PDLs). 20 clones included 18 clones from the CL1-A or CL1-D and 2 clones from the CL1-E control cell line . (A) Schematic of the stability study design. (B) The coefficient of variance (%CV) across different PDL for each clone was evaluated for productivity, mRNA expression of genes of interest, and glycans. Seven clones (from either CL1-Figure 2-8. Impact of generational age cell line stability attributes 54 A or CL1-D) highlighted in the green boxes exhibited acceptable <20% CV for titer and <25% CV for other attributes indicating the stability of various PQAs through age. Similarly, two clones (from CL1-E) highlighted in the red box indicate stability. 2.3.6 Evaluation of Two Selected Clones in Bench-Scale Bioreactors with Acceptable Productivity and Desired Product Attributes Of the 7 experimental clones that demonstrated maximum stability, clones A-29 and A-52 had the closest glycan profile to that of the RB. Therefore A-29 and A-52 were further characterized to understand their production potential. Clones A-29 and A-52 were scaled up in bench-scale 3 L bioreactors under culture conditions that more closely mimic commercial manufacturing. Similar growth profiles and productivity were observed between small spin tube and bioreactor scales (Figures 2-9A and B). The viable cell density for both clones reached a maximum of ∼15-18 × 106 cells/mL by Day 8 and slowly declined for the remainder of the culture. The viability ranged from 99% on Day 3 after inoculation of the fed-batch for both the clones to 50% at the end of the culture by Day 10 for both small (spin tube) and bench scales. Glycan profiling confirmed that clones A-29 and A-52 retained the desired glycan profile under these manufacturing conditions (Table 2-2A). The overall glycan distribution of Fab and Fc glycan forms for both clones A-29 and A-52 was similar to that of the RB. Nonetheless, A-52 most closely resembled the glycan profile of the RB at both the Fab and Fc region. More than 20 glycan species were identified at each Fc (N88) and Fab (N299) glycan site. Four glycan groups were evaluated: galactosylated glycans, sialylated glycans, high mannose glycans, and afucosylated glycans. The levels of α-Gal and β-Gal were calculated as the sum of complex type and hybrid 55 type glycan structures which contain at least one terminal α-Gal and β-Gal, respectively (Table 2-2A). Clones A-29 and A-52 had similar Fab and Fc β-Gal compared to the RB except A-52 in the small-scale run. This difference in glycan at small-scale might be due to the lack of controlled culture conditions at this scale. Clone A-29 had slightly higher α-Gal for both Fab and Fc, whereas A-52 had similar α-Gal levels to the RB. In addition, insignificant sialylation was observed at the Fc region of the RB, and in the case of A-29 and A-52 Fc sialylation (NGNA and NANA) was ≤1.1%. On the other hand, the RB NGNA levels at the Fab region were between 28%-36%, and clone A-52 matched this range. There was negligible NANA at the Fab region. High mannose was calculated as the sum of M8, M7, M6, and M5. Results show RB had ≤ 10% HM at Fab whereas Fc HM% was ≤2%, and A-29 and A-52 closely matched these levels of HM%. Afucosylated glycans are comprised of afucosylated complex and afucosylated hybrid types of glycans. A-52 showed afucosylation glycan levels similar to those of the RB, but compared to A-29, A-29 had slightly higher levels of afucosylated glycans. This dataset suggests that overexpression of GGTA1 and CMAH resulted in similar α-Gal and NGNA, respectively to the RB without affecting other glycans such as β-Gal, HM, NANA, and afucose. Together, similar glycan profiles and optimal productivity would qualify these two clones as suitable for biosimilar production. As a part of the analytical similarity assessment, chemical modifications of amino acid residues were evaluated. Any observed modifications can alter antibody functionality and are considered critical molecular attributes. Therefore, all mAb samples and the RB were subjected to digestion and peptide mapping to determine any chemical modifications. As a result, minimal changes in fragmentation and chemical modification were observed in methionine (Met) and 56 tryptophan oxidation, glycation of lysine (Lys) amino acid residues in the RB and samples (Table 2-2B). Overall, the minor differences observed werenot expected to impact safety and efficacy. Next, SE-UHPLC was used to quantitate the percentage of high molecular weight (HMW) species in A29 and A-52. Similar to the RB, A-29 and A-52 were predominately monomers with 1.5% and 1,1% HMW species, respectively, while RB had 0.3% HMW species. 57 Figure 2-9. Comparison of process performance across scales Figure 2-9. Comparison of process performance across scales. Clone A29 and A52 were compared in 10 day fed-batch productions small scale (spin tubes) and benchtop Applikon bioreactors. (A, B) VCD, Viability and titer plots for A-29 and A-52 respectively. Error bars represent the standard deviation calculated from data obtained in three independent experiments (n = 4) for small scale spin tube and biological replicates (n=3) in bioreactor production. A B 58 A B (A) The Fc and Fab glycan profile range of antibodies produced at small and bioreactor scale was compared with reference biologic (RB). (B) Site specific amino acid modification were confirmed based on fragmentation of the RB and clones A-52 and A-29. n=number of replicates tested. Table 2-2. LC- MS/MS analysis performed on pepsin digest of purified protein on day 10 fed-batch harvest samples for clones A29 and A-52 for glycan analysis and molecular attributes (relative %). 59 2.3.7 Clones A-29 and A-52 Retain Similar Biological Activity to the RB The mechanism of action of the RB is mediated by antigen targeting and subsequent ADCC through the interaction of the Fc domain with Fc receptors on the surface of immune effector cells. Hence, FcgγRIIIa (I58V), target antigen binding, and ADCC function of clones A-29 and A-52 were assessed to ensure that biological activity was similar to the RB. Antibodies from both small (spin tube) and bioreactor scales were tested. The antibodies showed similar binding to FcγRIIIa (158V) (blue bars) and cell surface target (grey bars) as the RB (Figure 2-10). The ADCC activity of the antibodies in the NK92 cells (white bars) was also similar to that of the RB (Figure 2-10). These data suggest that the antibody retains the expected functionality when expressed in the engineered production cells. 60 Figure 2-10. Summary of binding and biological activity of antibodies produced from clones A-29, and A-52 as compared to the reference biologic (RB) ADCC (antibody-dependent cell-mediated cytotoxicity), FcγRIIIa binding activity, and cell surface target binding. Relative activity (%) was calculated against RB reference standard lot. All samples were tested in triplicate for each assay, with the mean values reported as relative activity. The error bar represents the standard deviation calculated from data obtained in independent activity runs (n = 3). 2.4 Discussion This study demonstrated that cell line engineering can address the challenge of achieving similar glycan profiles between biosimilars produced in CHO cells and a RB produced in murine host cells. Our initial cloning strategy featured the co-transfection of individual CMAH and GGTA1 vectors in the CL1 cell line. There were two unexpected observations from the use of this strategy. First, the co-transfection resulted in the expression of only one or the other enzyme rather than the desired dual expression of both enzymes. Second, although mRNA expression of either CMAH or GGTA1 was observed in different clones, there was no detectable α-Gal, and NGNA found by LC-MS/MS analysis. This indicated that the enzymes were not translated, were nonfunctional, or were mislocalized in this system. Interestingly, a recent study also reported similar observations when attempting to overexpress two different glycosyltransferases. It was found that overexpressing two enzymes simultaneously resulted in the truncation and mislocalization of the enzymes [115]. Our initial results forced us to reconsider our cloning strategy and reinforced the need demonstrate appropriate subcellular localization of the enzymes. Therefore, in the present study, a single vector was designed with CMAH and GGTA1 joined by furin-2A, a self-cleaving peptide to maintain the 1:1 stoichiometry during 61 the expression of the two genes. In addition, to determine the subcellular localization of CMAH and GGTA1, a construct with tags added to the C- terminus, and N-terminus of the genes, respectively, was designed. With the help of tags in immunofluorescence, the appropriate subcellular localization of the two enzymes was confirmed. This relatively strict compartmentalization of the exogenous enzymes is thought to ensure the efficient biosynthesis of desired glycan structures by providing optimal contact between enzyme, mAb, and sugar nucleotide donor. Typically, the development of biological products, including both reference biologics and biosimilars, starts with cell line development and the selection and analysis of clones that could potentially result in desired CQAs, followed by media optimization for precise control of the desired CQAs. Instead of adopting this sequential approach, we relied on our understanding of the glycosylation pathway in the development cell line and focused on producing the desired RB glycan profile. Therefore, to ensure adequate levels of galactose needed to make both β-Gal and α-Gal, we added a UMG bolus feed on Day 3 of fed-batch cultivation. The addition of UMG has consistently been shown to increase galactosylation [99, 116]. In addition, this approach would save us time for later media optimization. We screened a large number of clones to increase the probability of finding a clone that could have a highly similar glycan profile to that of the RB glycan profile without compromising productivity. Indeed, within our panel of 72 clones, four distinct of glycan profile patterns emerged from small-scale production. Interestingly, the data revealed an inverse relationship between %α-Gal, and %NGNA 62 (Figure 2-4C). This inverse relationship can have two possible explanations. First, in engineered CHO cell lines, β-1,4Galactosyltransferase 1 (B4GALT1) transfers galactose from UDP-galactose in β-1,4-linkage to the glycan chain on GlcNAc sugar moiety, then GGTA1 or α-2,3-sialyltransferase 1 (ST3GAL1) sequentially adds α1,3-galactose and/ α-2,- linked sialic acid to β-1,4-linked galactose. Therefore, GGAT1 and ST3GAL1 compete for their common substrate β-1,4-linked galactose [117]. Thus, overexpression of GGTA1 in engineered CHO cell lines can usurp the terminal galactose-containing substrate and convert it to α-Gal. Secondly, it has been shown that glycosyltransferase homomers are assembled in the endoplasmic reticulum. These homomers become disassembled in Golgi, allowing the formation of specific active glycosyltransferases heteromers. There is no structural information on GGTA1, however, we hypothesize GGTA1 could form heteromers as reported for other glycosyltransferases [118]. Therefore, in the case of excess GGTA1, which may have reduced the association with ST3GAL1, the overall sialylation level in most clones would result. The diverse panel of 72 clones captured the complexity of the glycosylation pathway and allowed us to identify two clones, particularly clone A-52, with similar glycan profiles to the RB. These clones were found to have similar growth profiles, productivity, analytical similarity assessment, and biological functionality to the RB. Overall, our CHO cell engineering strategy can be applied to the development of future biosimilars to achieve the desired glycan profiles of RBs derived from different host cell species. 2.5 Conclusions Cell line engineering has increasingly been utilized to improve the production of therapeutic biologics. This study presents a novel engineering application for developing a CHO-derived biosimilar to a murine-derived RB. It demonstrates that the stable 63 expression of key glycosylation enzymes in engineered CHO cells enables the production of a biosimilar mAb with glycosylation patterns similar to that of the RB while maintaining productivity and other critical quality attributes. Our approach relied on a rigorous understanding of the murine and CHO glycosylation pathways and cell line engineering techniques. The positive results presented in this study suggest a method to achieve the desired glycan profile for future biosimilar development. Additionally, the use of a single engineered CHO cell line across the development process was highly efficient. The vector strategy to track the subcellular localization of the exogenous enzymes utilized in this study ensured the clones express the gene of interest from Day 1 using FACS sorting of the clones and minimizing the need for extensive process optimization to achieve similar RB quality attributes. The functional and analytical similarity assessment results provided confidence for the similarity of this mAb compared to the RB. This strategy has potentially broad application in the development of biosimilars. To further enhance our mechanistic understanding of the N-glycosylation pathway and design strategies for making future biosimilars, in the next Chapter, chapter3, we established a CHO- specific N-glycosylation assay. The assay is based on branched DNA technology that simultaneously measures up to 80 genes of interest from a single sample in a 96- or 384-well plate format. The assay identifies differentially expressed genes and enhances our mechanistic understanding of the glycosylation pathway. In addition, the assay can be leveraged for genetic engineering and cell culture medium development. 64 Chapter 3: High-Throughput CHO Cell Specific Glycosylation Enzyme mRNA Expression Profiler 3.1 Introduction Glycosylation is an essential cellular process where individual sugar molecules (monosaccharides) are sequentially added to proteins or lipids by the action of functionally distinct glycosidases and glycosyltransferases. N-linked glycosylation, also known as N-glycosylation, refers to the attachment of sugar molecules to the amide nitrogen of asparagine (Asn) residue of a protein. N-glycosylation of immunoglobulin G (IgG) antibodies, and IgG Fc-containing biologics (such as bispecifics, antibody-drug conjugates, Fc-fusion proteins), affect the activity, efficacy, serum half-life, and immunogenicity of the [12, 13]. Therefore, glycosylation is considered a critical quality attribute (CQA). N-glycosylation starts in the endoplasmic reticulum (ER). Oligosaccharides are first assembled on a lipid carrier, dolichylpyrophosphate and then transfered in the lumen of the ER to the asparagine residue at Asn-X-Ser/Thr consensus glycosylation sites (X can be any amino acid except proline). After the addition of GlcNAc and mannose residues and trimming by specific glucosidases and mannosidases, the core glycan chain is transported to the Golgi apparatus, where further sequential addition and trimming of sugar residues occurs by specific glycosyltransferases and glycosidases, resulting in different levels of complex, hybrid, and oligomannose type N-glycans [119] (Figure 3-1). 65 66 Figure 3-1. Schematic of the N-glycosylation pathway. The glycans enclosed in blue boxes were detected in the present study. The biosynthesis of N-glycans starts in the ER where GlcNAc-P combines with dolichol phosphate to generate dolichol pyrophosphate N-acetylglucosamine (GlcNAc-PP-Dol)—followed by the addition of a second GlcNAC. Then Alg mannosyltransferases catalyze the formation of precursor complex M9GlcNAc2-PP-Dol by sequentially addition of mannose to GlcNAc2-PP-Dol. Then M9GlcNAc2-PP-Dol is co-translationally transferred to an asparagine residue. Subsequent trimming of three-terminal glucose residues by Ganab and one mannose by Man1B1 leads to the formation of high mannose structures, which proceed to the cis-Golgi. Then, Golgi 1,2-α-mannosidases (Man1a1, Man1A2, and Man1c1) further cleave mannose resulting in M5. This M5 is the substrate for various glycosyltransferases and mannosidases, to generate hybrid and complex N-glycans. Overall this pathway results in glycans with terminal mannose, galactose, neu5Ac and fucose. The glycan profile of a biologic is a consequence of the interplay between the molecular sequence, the host cell line, and bioprocessing conditions [35, 120]. As a result, to develop methods to appropriately control the levels of N-glycans during the manufacturing of biologics, there is a need to enhance our mechanistic understanding of the glycosylation pathway in relation to factors that can influence glycans, such as gene expression levels of enzymes that produce N-glycosylation. The Chinese hamster Ovary (CHO) cell line is a well-established host cell line for biologics manufacturing. CHO cells enable robust and stable biologics production at manufacturing scales with a long history of regulatory approvals [121]. There are several different CHO sublines derived from the parental CHO line, such as CHO K1 or CHO/dhfr-, cell lines. There is considerable variability in the genetic makeup of the different CHO sublines [122]. As a result, there are differences in the relative expression and activity of glycosyltransferases, sugar transporters, and other enzymes that can cause variations in glycosylation profiles. Understanding the genetic heterogeneity in the glycosylation pathway and the associated functional outcomes can help rationally select a suitable parental host cell line. This knowledge can also prove decisive during 67 clone selection for production cell line development, such as to exclude undesirable genetic bottlenecks. Furthermore, glycosylation-specific cell line engineering is a powerful tool during the construction of a host for manufacturing novel biologics and biosimilars with the desired glycans. However, a comprehensive understanding of the differential expression of the genes that impact structure has not been available. This lack of fundamental understanding of the differential transcription, for the many glycosylation related enzyme mRNA levels and in hundred plus clones, has hindered the development of cell line engineering for commercial biologic manufacturing processes. The most common method to quantify gene mRNA expression is quantitative real-time polymerase chain reaction (qRT-PCR), which is a specific and highly sensitive manner. However, there are limitations to this technique due to the need for RNA extraction and the enzymatic reverse transcription and mRNA amplification steps [123]. As a result, the technique is labor intensive for many transcripts and prone to handling error, contamination, and variation. Similarly, RNA sequencing (RNA-seq) measurements of mRNA expression can be influenced by enzyme and gene-specific biases in the reverse transcription of RNA [124, 125]. Additionally, RNA-seq or microarrays can provide a wealth of data but could be costly for routine screening [126]. Cell line development for novel biologics or biosimilars typically requires screening ≥100+ clones to identify the ones with the desired growth and product quality attributes. Therefore, it is necessary to have a robust and cost-effective high-molecular assay that can accurately characterize the mRNA expression of many genes from a small sample, ideally automated and available as a kit. For example, Qiagen has developed an RT² Profiler PCR Array kit for the analysis of CHO cell glycosylation (Cat. no. 330231 PAJJ-046ZR) that can screen up to 84 genes. However, the assay requires RNA isolation, and one can only analyze one gene per sample; there is no option of 68 multiplexing. Therefore, the cost, time, and labor to analyze far more transcripts per sample are high. Alternatively, one can use the Luminex® xMAP® system (Luminex Corporation) beads for multiplexing of 3-80 RNA targets followed by branched DNA-based (bDNA) signal amplification [127]. The beads are magnetic microspheres functionalized with surface carboxyl groups for the covalent attachment of ligands (MagPlex®) (Figure 3-2A). These beads are internally labeled with different ratios of red and infrared fluorochromes, resulting in an array of different spectral regions, each of which can be coupled to a green fluorochrome-labeled oligonucleotide (probe) complementary to a specific target analyte (DNA/RNA) of interest [128]. Bead identification is by charged coupled device image capture of the magnetically arrayed beads. bDNA technology provides a unique sequential nucleic acid hybridization method for DNA or RNA quantification by amplifying the reporter signal rather than replicating the target sequence (Figure 3-2B). First, the cells are lysed to release the target RNA. Second, an oligonucleotide probe set is incubated with the target RNA, where the probes cooperatively hybridize the target. Third, signal amplification is performed by sequential hybridizing the bDNA pre-amplifier, amplifier, and labeled probe molecules to the target. Later, adding a fluorescent reporter generates a signal directly proportional to the amount of RNA in the sample[127]. QuantiGene® RNA MultiPlex (Thermo Fisher Scientific) gene expression assay combines Luminex xMAP bead technology MagPlex® bead and bDNA technology to quantify mRNA expression. They offer a customizable panel, therefore, we adapted QuantiGene® RNA assay to develop and validate a CHO-specific glycosylation assay referred to as the N-glycan enzyme mRNA expression profiler. a CHO-specific N-glycosylation pathway-related 133 gene panel was designed. It is well known that mRNA levels and their expressed protein levels can be poorly correlated [109]. Furthermore, the 69 complexity of the glycosylation pathway, where multiple enzymes, transporters, co-factors and nucleotide sugars are involved, coupled with competition between enzyme reactions, makes it challenging to relate the enzyme mRNA expression data with the detailed final glycan profile of the mAb [129, 130].Therefore, the hypothesis is that by performing mRNA profiling of N-glycan enzyme we will be able to explain CHO cell mAb glycan differences. Once we established the assay, to evaluate the hypothesis the glycan profiles and N-glycan enzyme mRNA expression in two Amgen CHO cell hosts, each producing two different mAbs was evaluated. Figure 3-2. Luminex xMAP bead technology MagPlex® and bDNA technology to quantify mRNA expression. The 6.5 µm polystyrene bead magnetic core is formed by polystyrene methacrylic acid (infusion of dyes). The surface of each microsphere is irregular, porous and carboxylated. Different concentrations of red and B A 70 infrared fluorophores were used to create 100 distinct microsphere sets. (B) Branched DNA technology workflow 3.2 Methods and Materials 3.2.1 Media and Reagents Company-proprietary cell line and cell culture media (Amgen Inc., Thousand Oaks, CA) were used throughout the study unless otherwise specified. Reagents for N-glycan enzyme mRNA expression profiler were supplied by Thermo Fisher Scientific. 3.2.2 Selection of Genes and Probe Design Previously, 297 glycan pathway-associated human genes were found to have homologs in the CHO-K1 cell line. However, only 159 of these homologs were expressed under exponential growth conditions in the CHO-K1 cell line [131]. Therefore, we started with the 159 genes and further customized the panel of genes based on the interest of the study. The focus of this study was to develop and validate the assay for the N-glycosylation pathway selectively. Therefore, the genes involved in O-glycosylation were excluded from the panel. Further, as there was no interest in looking at the highly sulfated polysaccharides, known as glycosaminoglycan (GAGs), like heparan sulfate, chondroitin sulfate (CS), and dermatan sulfate (DS), the genes for GAG modification enzymes were eliminated. Furthermore, RNA seq data mentioned earlier for the 297 genes was compared to microarray data from untreated CHO-K1 (wildtype) and untreated CHO-K6 (stably overexpressing human HER2) (Affymetrix Inc., Santa Clara, CA, USA) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110189 [132]. The data was compiled and curated from the two studies and used for gene selection. Genes of interest with expression in the microarray data but no expression in the RNAseq were added to the panel, such 71 as SLC35A2, SLC35E1, and Man1C1. The difference in observed gene expression levels can be due to CHO sublines and culture conditions used for individual studies. Moreover, the process of clonal selection can lead to subpopulations with different gene patterns. In this study, a panel of 133 genes, including two constitutively expressed genes as controls, was selected for further evaluation. The panel included N-glycosylation enzymes: glycosyltransferases, glycan degradation, nucleotide synthesis, and nucleotide sugar transporters. A maximum of 84 genes can be incorporated in a single N-glycan enzyme mRNA expression profiler probe set. Therefore, the probe set was divided into two subsets. Two gene-specific probe sets and a capture bead sets panel (75 plex) and a second panel (60 plex) were custom synthesized (Affymetrix, ThermoFisher). Each panel had TATA-binding protein (TBP) and glucuronidase beta (GUSB) as control genes that were constitutively expressed. The probes were designed. to capture all the potential transcript variants in the CHO sublines (Ch and CHO-K1) www.chogenome.org/files/CH_CHO_transcript_homolog_2014.xlsx. 3.2.3 Cell Culture Four production cell lines (previously developed by Amgen) were studied that were derived from parental host cell lines CHO-A and CHO-B that expressed two different molecules, AMG 1 and AMG 2 (AMG1_CHO-A, AMG1_CHO-B, AMG2_CHO-A, and AMG2_CHO-B). Cells were cultured in 50 mL TubeSpin® tubes (TPP, Trasadingen, Switzerland) at 370C, 5% CO2, and 85% humidity. Cells were maintained by passaging twice a week at 4x106-5x106 cells/mL concentrations. The doubling times for the cell lines were calculated by estimating the specific growth rate (µ) of each cell line during the regular passage. The doubling time (td) was calculated based on the formula below: 72 td = 𝑙𝑙𝑙𝑙2µ Where µ = (𝑉𝑉𝑉𝑉𝑉𝑉1/𝑉𝑉𝑉𝑉𝑉𝑉0)/(𝑡𝑡1 − 𝑡𝑡0) Where: VCD0: (cells/mL) is the viable cell density at the time of inoculation. VCD1: (cells/mL) is the viable cell density in a culture sample. t0: (h) is the time of inoculation. t1: (h) is the time of sampling. All 4 production cell lines were evaluated in a fed-batch production assay to evaluate product quality attributes (PQAs). 3.2.4 Small-Scale Production Small-scale fed-batch production was performed in 50 mL TubeSpin for cultures to determine the PQAs exhibited by the different production cell lines expressing AMG 1 and AMG 2 using the same culture condition at the same time (TPP, Trasadingen, Switzerland). Briefly, individual tubes were set up with a working volume of 15 mL of production medium, incubated at 360C, 5% CO2, 85% relative humidity, and shaken at 225 rpm with a 50 mm orbital diameter in an ISF4-X incubator (Kuhner AG, Basel, Switzerland). Cultures were inoculated at 1x106 cells/mL and fed a bolus on Days 3, 6, and 8. During the production, samples were taken on Days 0, 3, 6, 8, and 10 to analyze growth and viability using a Vi-Cell XR counter (Beckman Coulter, Brea, CA). In addition, samples for mAb concentration were taken on Days 8 and 10. The protein A affinity high-performance liquid chromatography (HPLC) method determined antibody concentrations with an affinity column (Protein A; Waters, Milford, MA). For glycan analysis, cell 73 culture supernatants were purified via affinity chromatography (Atoll GmbH, Weingarten, Germany). 3.2.5 mRNA Expression Analysis, Normalization, and Statistics mRNA expression analysis was performed using QuantiGene® (Thermo Fisher) assay manufacturer's protocol [123]. Briefly, cell pellets from 5x105 viable cells were lysed using 1X lysis buffer, supplemented with proteinase K (stock concentration 50 mg/mL), and incubated at 50°C for 1 hour. Cell lysates were stored at -800C. Later, frozen lysates were thawed. 80 µL of cell lysate per well was analyzed and processed for mRNA quantification for mRNA as described previously [104]. The plate was read using the Luminex FLEXMAP 3D instrument and xPONENT® 4.2 software. Signals recorded as the mean fluorescence intensity (MFI) generated from each bead are proportional to the amount of each mRNA captured on the surface of each generated specific probe set. Next, Gene expression was normalized using the geometric mean expression of the constitutively expressed control genes TBP and GUSB. The normalized expression was log2 transformed for analysis. Statistical analysis was performed in the R statistical language. Multidimensional data dimension reduction and exploration was performed using principal component analysis (PCA). Differential expression analysis was performed via the non-parametric Mann_Whitney test run separately for each parent-host cell line pair, corrected by False Discovery Rate (FDR) and adjusted p-values referred as Q-values (Benjamini-Hochberg procedure). A log2 Fold change >1 and FDR0.05 was considered significant differential gene expression. 3.2.6 RNA-Seq and gene expression analysis For the RNA-Seq analysis 36 clones with varying glycan profiles were selected from the 72 Chapter 2 clones. Cell pellets were collected by centrifuging ten million cells at 1,000 rpm for 74 3 min at 4C. Total RNA was then extracted from the cell pellets using the RNeasy Mini Kit (QIAGEN, Valencia, CA) following the manufacturer’s protocol. RNA concentration and quality were measured using the NanoDrop ND-1000 spectrophotometer (Thermo Scientific, Wilmington, DE) and the 2100 Bioanalyzer (Agilent, Santa Clara, CA), respectively. Total RNA was provided to Genewiz Inc. (South Plainfield, NJ) for quality control, RNA library preparation with polyA selection and subsequent sequencing analysis. Sequencing was performed on the Illumina HiSeq platform (Illumina Inc., San Diego, CA) and >350 million 2x150 bp paired-end reads were generated for each sequencing sample. RNA-Seq reads in the FASTQ format were trimmed and mapped to a CHO-K1 genome reference (https://blast.chogenome.org/archive/Genomes/CHO-K1/CHO-K1/CHO-K1-v1/) for gene read count analysis. 3.2.7 Glycan Analysis Day 10 purified samples for AMG 1 and AMG 2 were evaluated for glycan analysis. N-linked oligosaccharides were enzymatically released from AMG 1 and AMG 2 using peptide-N-glycosidase F (Prozyme, Hayward, CA). The free oligosaccharides were then derivatized with the fluorophore 2-aminobenzoic acid (Sigma-Aldrich, St. Louis, MO) on the reducing terminal N-acetylglucosamine (GlcNAc). The fluorescently labeled oligosaccharides were separated by hydrophilic interaction chromatography (HILIC) (Waters, Milford, MA). Bound oligosaccharides were eluted from the column with an increasing gradient of ammonium formate and detected by fluorescence. The relative % peak areas of the oligosaccharides were calculated. 3.3 Results 3.3.1 N-glycan enzyme mRNA expression profiler assay performance The stable production cell lines (AMG1_CHO-A, AMG1_CHO-B, AMG2_CHO-A, and AMG2_CHO-B) had been previously generated using transfection. The clones were selected based 75 on monoclonality, productivity, and product quality attributes at small scale, followed by bioreactor production runs. For this study, these mAb expressing producing cell lines and the two untransfected CHO cell hosts (CHO-A and CHO-B) were propagated in a selective culture medium. The fed-batch study was conducted using the same production media to compare all four mAb expressing clones (Figure 3-3). A total of 6 samples in triplicate were analyzed using the N-glycan enzyme mRNA expression profiler assay. For the assay the detection limit was defined as a signal three standard deviations above the background. By this standard, of 133 genes 127 genes were detected in all the samples. The precision of the assay was measured by the coefficient of variance (CV) between the triplicates. The %CV ranged from 2%-19% for genes above the detection limit, whereas for genes below the detection limit the %CV was ≥ 25%. TATA-binding protein (TBP) and glucuronidase beta (GUSB) were used as control genes that were constitutively and stably expressed in the cell lines (Figure 3-4A). Tbp was selected as it has a low expression range whereas Gusb has a higher expression range. The geometric mean of their expression was used instead of the arithmetic mean for more accurate averaging of the control genes, in the case of outlying values and abundance differences between the different genes [133]. To confirm that the mRNA expression profiler assay developed in this study was able to quantify the expression levels of key N-glycan enzymes across a wide dynamic range, cell culture samples from the panel of 36 different clones were analyzed by both this assay and by RNA-Seq. Strong correlations between gene expression results from the mRNA profiler assay and RNA-Seq were seen for key N-glycan enzymes (for example, CMAH, GGTA1). For CMAH, a Pearson correlation coefficient r = 0.80 was seen with a p-value of p<0.0001 confirming a statistically significant correlation between the mRNA profiler assay and the RNA-Seq gene expression method (Figure 3-4B). Similarly, a statistically strong correlation was seen for GGTA1, with a 76 Pearson correlation coefficient r = 0.80 and p-value of p<0.0001 (Figure 3-4C). Moreover, for both CMAH and GGTA1, the varied clones provided for the analysis of a broad range of expression levels (>1000-fold difference between the highest and the lowest expression, (Figure 3-4B and 3-4C). Figure 3-3. Schematic showing the study plan for comparing the two hosts, CHO-A and CHO-B, for two different mAb-producing cell lines. Samples were collected for mRNA expression analysis as shown in the green boxes. Growth was estimated during the fed-batch production assay, whereas titer and glycan levels were estimated on Day 10. 77 Figure 3-4. Performance of N-glycan enzyme mRNA expression profiler assay (A) mRNA expression of constitutive genes Gusb and Tbp in CHO-A, CHO-B host cells and in CHO-A and CHO-B cells expressing AMG 1 and AMG 2 (B, C) Day 8 mRNA transcript expression of overexpressed genes in CHO CL1 derived 36 clones. (B) CMAH and (C) GGTA1. The x-axis is normalized to constitutive genes Gusb and Tbp, and the y-axis is the log10 (RNA-Seq +100) of raw read counts mapped to the gene. 3.3.2 Growth and Glycan Differences are Observed in CHO-A and CHO-B cell lines Production assays show that irrespective of the mAbs being produced, CHO-B cells grew faster than CHO-A cells and maintained higher viability for a longer duration, resulting in higher product titers for both AMG 1 and AMG 2 (Figure 3-5 A-C). The doubling time for the CHO-B cell lines was 19±1 h, whereas for CHO-A, it was 25±3 h. The higher mass per volume of cells on Day 10 for CHO-B cell lines likely contributed to the higher yield of mAb AMG 1 and AMG 2 produced in this cell line. (Figure 3-5C). 78 0 2 4 6 8 10051015202530Viable cell densityDaysViable cell density (10e6/ml)AMG1-CHO-AAMG1-CHO-BAMG2-CHO-AAMG2-CHO-B0 2 4 6 8 10020406080100ViabilityDays%ViabilityAMG1-CHO-AAMG1-CHO-BAMG2-CHO-AAMG2-CHO-BAMG1 AMG2Titer (g/L)Titer (g/L)CHO-ACHO-B Figure 3-5. Comparison of two host cell lines expressing mAb AMG 1 and AMG 2 in 10 day fed-batch. (A) Viable cell density, (B) viability and (C) Titer plots for AMG 1 and AMG 2 expressed in CHO-A and CHOB. Error bars represent the standard deviation calculated from biological replicates (n=4) data from spin tube production cultures. A B C 79 Next, from Day 10 of the purified production samples, N-glycans of the mAbs were profiled for production cell lines AMG 1 and AMG 2 expressed in both CHO-A and CHO-B host cell lines. Both AMG 1 and AMG 2 have one glycosylation site at position N297 of the Fc region. Upon comprehensive analysis, most of the resulting N-glycan species processed in Golgi were significantly different for AMG1 and AMG 2 when produced in each host (Figure 3-6). Interestingly, both mAbs, AMG 1 and AMG 2, had a similar glycan profile when produced in different hosts. For example, the terminal GlcNAc hybrid glycans processed in cis-Golgi, A1G0, and A1G0F were significantly higher in CHO-A produced mAbs (Figure 3-6A-B). In contrast, the complex bi-antennary fucosylated glycan, A2G0F, processed in medial- and trans-Golgi, were significantly higher in CHO-B produced mAbs. A2G0F is the predominant fucosylated glycan species found on the two mAbs (Figure 3-6C). Both CHO hosts expressed mainly core fucosylated complex biantennary Fc glycans-A2G0F and A2G1F (Figure 3-6-C-E). In comparison, afucosylated glycans are ≤5% (Figure 3-6F). Notably, for AMG 1 and AMG 2, CHO-A yielded significantly higher % high mannose than CHO-B (Figure 3-6G). The majority of % high mannose comes from the M5 glycan glycoform (Figure 3-6H). Also, AMG 1 had more HM species (>15%) versus AMG 2 (>5%) when produced in CHO-A. AMG 2 for both CHO-A and CHO-B had more terminal galactose residues compared to AMG 1. CHO-A consistently produced significantly more terminal galactosylation than CHO-B (Figure 3-6I). Although AMG 2 had overall higher galactosylation than AMG 1, terminal sialylation was not detected on AMG 1. Similarly, on AMG 2, there was negligible < 0.5% total sialylation. 80 AMG1 AMG20.00.51.01.52.0% A1G0% A1G0CHO-ACHO-B✱✱ nsAMG1 AMG2051015% A1G0F% A1G0FCHO-ACHO-B✱✱✱✱ ✱✱✱AMG1 AMG2020406080100% A2G0F% A2G0FCHO-ACHO-B✱✱ ✱✱AMG1 AMG2051015% A2G1F% A2G1FCHO-ACHO-B✱✱ ✱✱✱✱AMG1 AMG2020406080100% Total Fucosylation% Total Fucosylation CHO-ACHO-B✱ ✱AMG1 AMG2012345% aFucosylation% AfucosylationCHO-ACHO-Bns ✱AMG1 AMG205101520% High Mannose% High MannoseCHO-ACHO-B✱✱✱✱ ✱✱✱✱AMG1 AMG205101520% M5% M5CHO-ACHO-B✱✱✱✱ ✱✱✱✱AMG1 AMG20102030% Total Galactosylation% Total GalactosylationCHO-ACHO-B✱✱✱✱✱✱✱ Figure 3-6. Glycan distribution profile of AMG 1 and AMG 2 expressed in CHO-A and CHO-B in 10-day fed-batch production. Antibody quality of (A) A1G0 (B) A1G0F (C) A2G0F (D) A2G1F (E) Total percentage of fucosylation, (F) Total percentage of afucosylation (G) Total percentage high mannose, (H) M5, and (I) Total percentage of galactosylation were analyzed from the Day10 harvest material. All values represent the mean of four replicates, and the error bars show one standard deviation. Statistical analysis was performed by Welch T-test (n.s., non-significant p> 0.05; * p ≤0.05; **p≤0.01; ***p≤0.001; ****≤0.0001) A B C D E F G H I 81 3.3.3 CHO-A and CHO-B cell lines show genetic divergence The normalized transcript profile of 133 N-glycosylation genes expressed as log2 values were determined to understand what mRNA expression differences distinguished the CHO-A and CHO-B hosts. The Principal component analysis (PCA) of the untransfected host cell lines and all mAb-producing cell lines segregated the cell lines into three clear, distinct groups (Figure 3-7A). The component 1 separated the CHO-A host from CHO-A transfected and CHO-B, (host and untransfected) with a variance score of 43%. These results suggest that CHO-A undergoes a stronger change in mRNA expression of the glycosylation genes measured upon transfection than does CHO-B after transfection (Figure 3-7A). The second component separated the CHO-A and CHO-B cell lines with the variance score To understand the parental host cell differences, the mRNA expression levels of genes involved in N-glycosylation were compared in the untransfected CHO-A to CHO-B samples. The 133 genes were divided into three functional categories: glycan degradation, glycan transferase, and nucleotide sugar metabolism. The individual heat maps with hierarchical clustering for the three categories reveal differences in the CHO-A and CHO-B host cell lines, evident by relative upregulated (red) and relative downregulated (blue) patterns by cell lines (Figure 3-7B-D). Out of the 133 analyzed genes, 115 genes were significantly different with false discovery rate, FDR <0.5, and adjusted p-value <0.05, between the two hosts, 47 of these genes had a ≥ 2 fold difference and were referred as differentially expressed genes. This analysis suggests inherent genetic differences in the N-glycosylation pathway genes of the two host cell lines. 82 Figure 3-7. Genetic divergence in the CHO-A and CHO-B host. (A) Principal Component Analysis (PCA) of all the data for untransfected and mAb producing clones for three different time points shows distinct clustering as shown by region 1- untransfected CHO-A host only, region 2- transfected CHO-A cell lines and region 3- untransfected and transfected CHO-B cell lines. Heat maps with hierarchical clustering comparing untransfected CHO-A and CHO-B host for 133 N-glycosylation pathway-related genes categorized as (B) Glycan degradation, (C) Glycan transferase, and (D) Nucleotide sugar metabolism. Red (upregulated) and Blue (downregulated) 83 Next, we compared the differential gene expression patterns of AMG1 and AMG2 between mAb producing clones and the CHO-A and CHO-B cell lines at three individual time points (passaging, Day 8, and Day 10 of production). First, the shared differentially expressed genes were determined between the three-time points for each mAb AMG 1 and AMG 2. As can be noted from the Venn diagram (Figure 3-8A-B). AMG1 and AMG2 had different profiles, suggesting the influence of the process of transfection and/or possibly due to molecule production (Figure 3-7A-B). In the case of AMG 1, only 4 differentially expressed genes were shared between the three time points. Whereas, in the case of AMG 2, there were 21 shared differentially expressed genes between the different time points (Figure 3-8B). The expression of genes across different time points could also be influenced by the different growth passaging media that was leaner than the fed-batch media. Therefore, it is reasonable to expect different gene expression profiles due to media and metabolic state differences for different time points. This analysis suggests that for cell line development screening of clones, one might have to investigate the gene expression, especially at the production stages, to understand the gene profile that will most influence the product quality. B A A B 84 Figure 3-8. Transcriptional similarity of cell lines from three individual time points -passaging, Day 8, and Day 10 of production. Number of genes found to be differentially expressed (fold change ≥2) between CHO-A and CHO-B (A) AMG 1 and (B) AMG 2 Next, the variations in the N-glycan enzyme profile were compared with the transcriptomic data of 133 genes. One glycan, total % high mannose (% HM), was significantly higher in mAbs produced in the CHO-A cell line across both mAbs. High mannose glycans contain unsubstituted terminal mannose (M5-M9) sugars. Among M5-M9, M5 contributed to the majority of the % HM for AMG 1 and AMG 2 (Figure 3-6H). To further examine the reason for the accumulation of M5 glycan species, a schematic of the pathway illustrating the biosynthesis of M5 glycan was generated (Figure 3-9). In the first step, Dol-P-Man synthase (Dmps) family member enzymes, Dpm1, Dmp2, and Dmp3 synthesize Man-P-Dol by catalyzing the transfer of mannose from guanosine diphosphate α-D-mannose (GDP-Man) to dolichylphosphate (Dol-P). Further, Man-P-Dol is used by the ER luminal enzymes Alg mannosyltransferase genes, Alg1, Alg2, Alg3, Alg9, and Alg12, catalyze the formation of nine mannose glycan intermediate by sequential addition of mannose. Next, ER mannosidase Man1b1 is involved in single mannose trimming from M9 to produce M8. Further, Golgi residing 1,2-α-mannosidases Man1a1, Man1a2, and Man1c1 cleave the 1,2-α-bound mannose moiety from high mannose glycan M8, M7, resulting in M5 (5-mannose glycan) [134]. M5 is the substrate for glycosyltransferase Mgat1, which adds GlcNAc to the high-mannose site, an essential early step in producing hybrid and complex structures. Later, additional enzymes help make more hybrid and complex structures. 85 Interestingly, the mRNA expression data showed that Dpm1, Dpm2 and all mannosyltransferase family enzymes Alg2, Alg3, Alg9, Alg11, Alg12, are expressed at a higher expression level in the CHO-A cell lines with significant higher expression of Alg12 for both AMG 1 and AMG 2 mAbs compared to CHO-B produced AMG 1 and AMG 2. At the same time, relative lower expression levels of the ER and Golgi mannosidases (Man1a1, Man1a2, Man1c) and Mgat1 were found in CHO-A compared to the CHO-B cell line for both mAbs (Figure 3-9). These observations suggest that the CHO-A cell line can efficiently cap mannose sugar resulting in high mannose glycans. However, due to the low mRNA expression levels of mannosidases and Mgat1, the high-mannose glycan, M5, accumulates, creating a bottleneck for further efficient processing. 86 Figure 3-9. Expression of genes involved in the biosynthesis of M5 glycan. The expression of various transcript of genes involved in the biosynthesis of high mannose glycoform, specifically M5 was analyzed in CHO-A and CHO-B cells expressing AMG 1 and AMG 2 Next, the reason for significantly higher % galactosylation in both mAbs produced by the CHO-A cell line compared to mAb produced by the CHO-B cell line was examined (Figure 3-6I). Therefore, the transcript level of all seven different beta-1,4-galactosyltransferases (B4GALTs) that specifically transfer galactose in a beta1,4 linkage to the acceptor sugar GlcNAc was investigated (Figure 3-10A). Although for both AMG 1 and AMG 2 mAbs produced in CHO-A and CHO-B, there were differences in the expression of seven enzymes, none of which were differentially expressed with > 2 fold difference. Therefore, the mRNA expression data do not explain the high galactosylation levels of mAbs produced in the CHO-A cell line. In addition to the difference in galactosylation between CHO-A and CHO-B produces mAb cell lines, within CHO-A produced mAbs, AMG 1 had 5%, whereas AMG 2 had 27% of total galactosylation. This galactosylation difference could result from the molecule and clonal selection difference. Of the seven galactosylation enzymes, B4GALTs, four enzymes, B4Galt1, B4Gal2, B4Gal3, and B4Galt4 are previously reported to contribute to galactosylation of N-glycans, with B4Galt1 being the key isozyme [95, 135, 136]. In the case of AMG 2, even though B4Galt1 is significantly differentially expressed with a < 2 fold difference, it is relatively highly expressed in AMG 2 produced in CHO-A compared to AMG 1. Therefore, it might be possible that the expression of B4Galt1 in AMG 2_CHO-A is sufficient to contribute to high galactosylation levels compared to AMG 1_CHO-A. Moreover, sometimes it is not the enzyme but cofactors that play a significant role in the activity of the enzyme. For example, manganese, a co-factor for galactosyltransferases, is well described as a modulator of antibody galactosylation [99, 137]. 87 Figure 3-10. Expression pattern of genes on days 8 and 10 of production of AMG 1 and AMG 2 from CHO-A and CHO-B cell lines. Expression of genes involved in (A) N-galactosylation and (B) Fucosylation genes expression is shown Both AMG 1 and AMG 2 have predominantly fucosylated glycans (Figure 3-6E). A significantly higher % of total fucosylation was measured in mAbs produced by the CHO-A cell line compared to the CHO-B cell line. Higher fucosylation in CHO-A could be due to the higher expression of fucosyltransferase genes Fut8 and Fut11 in CHO-A (Figure 3-10B). Although AMG 1-CHO-A has a significantly higher expression of α-L-fucosidase, Fuca1, it still has higher fucosylated species. Fuca1 is responsible for the defucosylation of fucosylated substrates in lysosomes. However, the substrate IgG structure-activity association of the Fuca1 gene is poorly understood. A B 88 3.4 Discussion The CHO-specific N-glycan enzyme mRNA expression profiler assay designed in this study yielded a high-throughput, easy-to-use, and automated assay. Furthermore, it directly detected gene expression from lysates of as few as 30,000 cells per well in 96 well plate to quantify up to 80 genes per panel. We evaluated a total of 133 genes across 2 panels. Compared with other multiplex mRNA expression assays such as Nano string (NS), the method involves approximately 45 mins more technician time than NS but is one-tenth the cost of NS assay [138, 139]. In addition, the multiplex feature of the assay has several advantages: 1) conservation of sample, reagents, and labor, especially when screening 100+ clones; 2) extensive data set is collected from one assay; 3) all analytes are measured at the same time under identical conditions. The assay can be run in a 96 or 384 well format, thus providing flexibility of screening a large number of samples at a time. Many comparative studies have compared b-DNA based assays like QuantiGene® with microarray, qPCR, and NS. QuantiGene yielded similar or better results with improved detection sensitivity, precision, and reproducibility [123, 126, 140, 141]. Furthermore, QuantiGene® was successfully utilized as a clone screening method to identify CHO-produced clones with maximum specific product quality and exclude clones with aggregation propensity. The study demonstrated that clones with heavy chain transcript levels correlated with specific productivity, and low ratios (<1.5) of light to heavy chain transcript levels are indicative of high antibody aggregation [142]. The assay has also been approved by US Food and Drug Administration (FDA) as a diagnostic product for quantitative viral load determination for human immunodeficiency virus (HIV), hepatitis C, and hepatitis B virus with a detection sensitivity of <50 transcripts molecules [143]. In the present study, the N-glycan enzyme mRNA expression profiler was utilized to compare two different CHO cell lines, CHO-A and CHO-B, producing mAbs AMG 1 and AMG 89 2. CHO-B consistently grew faster compared to CHO-A with or without transfection. Similar growth differences have been observed when two different CHO hosts are compared elsewhere [91, 122]. In addition, productivity and glycosylation differences were observed even when different CHO hosts were transfected with the same mAb gene vector and grown in fed-batch production media. In case of higher mannosylation and lower fucosylation in mAbs produced by the CHO-A cell line was reasoned by the increase in mRNA expression of most mannoslytransferases and a decrease in mannosidases. Mannosidases play a critical role in the maturation of N-glycans in the ER and Golgi apparatus. Previous reports have shown similar observations where low expression of mannosidases such as Man1B1, Man1A2, Man1C1, and Man1A1 resulted in the accumulation of high mannose glycans [134]. Another study shows that the knockout of Man1A1 and Man1A2 primarily decreased complex-type N-glycan by 15% in HEK293 cells [144]. Also, it has been reported that mannosidase inhibitors such as kifunensine, deoxymannojirimycin are used to increase high mannose glycans for mAb production in CHO cells [145]. In one study, the use of kifunensine resulted in increased high mannose glycans, thereby resulting in a 14-fold increase in ADCC activity of Rituximab [146]. Similarly, Mgat1 was found to have an essential role in forming hybrid and complex glycoforms. Notably, Mgat1 was significantly less expressed in the CHO-A cell line than CHO-B. Studies have shown that Mgat1 deficient CHO cell lines produce recombinant mAbs and vaccines with high-high mannose [63, 147]. Therefore, our results suggest that a cell line with the downregulation of mannosidases and Mgat1 might result in higher high mannose glycans. Our results provide further evidence that the higher expression of B4Galt1 might correlate to higher galactosylation observed in CHO-A-produced mAbs. Studies indicate that B4Gal-T1 can 90 transfer galactose to all four branches and suggest that B4Gal-T1 is the most active N-glycan processing B4Gal-T within the family of β-1,4-galactosyltransferases The CRISPR KO of B4Galt1 has resulted in EPO with only 1% galactosylation [95], suggesting the B4Gal-T1 is the key enzyme for galactosylation. Similarly, in CHO cells, the overexpression of the B4Galt1 gene alone produced mAbs with more than 80% galactosylated bi-antennary N-glycans [148]. When we compare the mRNA expression of B4Gal-T1 for AMG 1 produced in CHO-A and CHO-B, although the mRNA expression is higher in CHO-B, the galactosylation is lower than observed in AMG 1 produced in CHO-A. Similarly, there was no clear correlation between enzyme mRNA expression and the glycan levels for hybrid glycans A1G0, A1G0F, and complex bi-antennary fucosylated glycan A2G0F established. This is an example of a poor correlation between the glycosylation enzyme mRNA expression and glycan profiles. This could be due to the glycosylation pathway complexity, where numerous enzymes and their isoform work together, overlapping and competing, within intertwined reactions. This complexity is augmented by impacts from what can be varied enzyme localization and activity, as well as the levels of nucleotide sugars and cofactors. In addition, the expression and activity of these enzymes is controlled at the transcription, translation, and post-translation levels [149]. Sometimes, the prediction of protein from mRNA expression levels can be poorly correlated with unreliability and a lack of precision due to the complex regulation of the translation process [109]. All these factors occasionally make it challenging to extrapolate the enzyme mRNA expression data to explain the glycan profile. Therefore, the gene expression data can give us signature genes that could correlate to the glycan profile, but this might sometimes not be the case. In such cases, it is important to have a qualitative understanding of the glycosylation reaction networks and subsequently integrate quantitative mRNA expression, combined with protein and substrate concentration knowledge. 91 In summary, our study used an N-glycan enzyme mRNA expression profiler assay to dissect the complexity of the glycosylation pathway and host cell line diversity. The study pinpointed that N- glycan patterns of mAb could acquire cell line-specific variation due to the inherent genetic characteristics of specific CHO hosts. Our gene expression analysis demonstrates that the N-glycan enzyme mRNA expression profiler assay as a tool that can enable rapid, sensitive, and routine quantitation, detection, and identification of critical cellular signatures that impact the glycan profile of a biologic. Additionally, the technique can be used to analyze any pathway-related transcript levels and enhance our holistic understanding of production cell lines. 92 Chapter 4: Conclusion and Future Directions Glycan differences between proteins produced by CHO or murine cell lines can cause Fc domain changes that can affect protein half-life, binding activity, stability, and efficacy. To leverage the manufacturing advantages of the CHO expression system, a CHO cell expressing antibody that was originally produced in a murine cell line to produce murine-like glycans was engineered. Specifically, cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) and N-acetyllactosaminide alpha-1,3-galactosyltransferase (GGTA1) to obtain glycans with N-glycolylneuraminic acid (Neu5Gc; NGNA) and galactose-α-1,3-galactose (alpha gal; α-Gal) were overexpressed. The relevance of this work to the industry was far greater than usual since a rigorous cloning process, extensive characterization including fed-batch cultures, and comparing detailed analytical and functional profiles to demonstrate similarity to the reference biologic (RB) was performed. Thereby, a cell engineering strategy was established that can enable the use of CHO cell manufacturing platforms for biosimilar production of murine-produced reference biologics Our approach relied on a rigorous understanding of the murine and CHO glycosylation pathways and cell line engineering techniques. The positive results presented in the study open opportunities to model and predict options for cell line engineering to achieve the desired glycan profile for future biosimilar development. Additionally, the use of a single engineered CHO cell line across the development process was highly efficient. The vector strategy to track the subcellular localization of the exogenous enzymes utilized in this study ensured the clones express the gene of interest from day 1 of FACS sorting for cloning, minimizing the need for extensive process optimization to match RB quality attributes. Since biosimilars require matching the post-93 translation modification profile of the RB, this strategy has potential applications in the development of biosimilars. To further engineer host cell lines to achieve the desired glycan profile, it is vital to understand the relation between the expression profile of N-glycosylation pathway-related genes and the resulting glycan profile. Differences in the relative activity of glycosyltransferase, sugar transporters, and other enzymes can cause variations in the glycosylation profile. Developing novel biologics and biosimilars requires the generation of a cell line expressing a protein with the desired glycosylation profiles. Consequently, there was a need to design a CHO-specific high-throughput N-glycosylation enzyme mRNA expressiion assay. To address this gap, we developed a simple, sensitive, and high throughput 96-well plate assay to probe gene expression profiles in the CHO cell N-glycosylation pathway. This assay is based on branched DNA technology and uses signal amplification to measure mRNA transcripts. Up to 84 genes from the CHO glycosylation pathway can be analyzed from a single sample lysate. The assay can be used routinely during the cell line development process to evaluate the expression of N-glycosylation genes. We deployed this approach for the comparison of two CHO cell hosts expressing the same glycosylated protein. Our results indicated that the host expressing a highly mannosylated form of the protein had elevated mRNA expression of the mannosylation genes (Alg2, Alg3, Alg9, Alg11, Alg12) and downregulation of mannosidases glycosyltransferases such as Mgat1. This indicates that sub-optimal expression of these specific glycosylation genes results in glycosylation pathway bottlenecks that result in incomplete glycan processing. Another application of the high throughput assay developed in this study is the identification of significantly differentially expressed glycosylation genes across bioprocessing conditions to guide process development and ultimately lead to a commercial manufacturing process that delivers the desired glycosylation profile. 94 Overall, the N-glycan enzyme mRNA expression profiler, a high throughput assay developed in this study has broad applicability to study the glycosylation of CHO cells and can enable glycan-focused cell line and bioprocess development to optimize commercial manufacturing processes for biotherapeutic production. Glycosylation is a complex pathway involving multiple enzymes with the activity that is coordinated in a remarkable orchestral fashion to maintain a delicate balance of nucleotide sugar substrates. In this study, we did not look at the metabolic profile of the CHO cells. Therefore the information about the availability of nucleotide sugars was missing from our study. Likewise, future work could evaluate the protein levels of selective enzymes to identify information missed by the transcript level information. Furthermore, simultaneous quantification of multiple proteins and corresponding transcript levels from the same sample can be performed using xMAP technology on the Luminex instrument. Therefore, the N-glycan enzyme mRNA expression profiler panel information could be augmented a multiplex protein panel, for example, Procartaplex® for protein profiling [150]. Finally, multi-omics high-throughput approaches combined with machine learning and predictive modeling can enable more sophisticated, mechanism-based decision-making during host and clone selection. As we develop a greater understanding of the underlying biological mechanism of CHO cells leading to different glycosylation patterns, we should be able to design optimized engineering strategies to improve the host cell line, process, and media/feed development by optimization of cellular responses and ultimately achieve the desired glycan profiles. Further, gathering information on intracellular processes is critical to understanding how to improve protein production and achieve desired and stable PQAs. The future of cell line development for biosimilars should greatly benefit from the integration of multi-omics and 95 machine learning predictability approaches. 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Thesis/Dissertation
eng
Chemical and Biological Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Piret, James
Engineering Chinese Hamster Ovary cell recombinant protein glycosylation
Text