Multivariate data analysis of growth medium trends affecting antibody glycosylation

Antibody glycan quality predicted from CHO cell culture media markers and machine learning

N-glycosylation can have a profound effect on the quality of mAb therapeutics. In biomanufacturing, one of the ways to influence N-glycosylation patterns is by altering the media used to grow mAb cell expression systems. Here, we explore the potential of machine learning (ML) to forecast the abundances of N-glycan types based on variables related to the growth media. The ML models exploit a dataset consisting of detailed glycomic characterisation of Anti-HER fed-batch bioreactor cell cultures measured daily under 12 different culture conditions, such as changes in levels of dissolved oxygen, pH, temperature, and the use of two different commercially available media. By performing spent media quantitation and subsequent calculation of pseudo cell consumption rates (termed media markers) as inputs to the ML model, we were able to demonstrate a small subset of media markers (18 selected out of 167 mass spectrometry peaks) in a Chinese Hamster Ovary (CHO) cell cultures are important to model N-glycan relative abundances (Regression - correlations between 0.80-0.92; Classification - AUC between 75.0-97.2). The performances suggest the ML models can infer N-glycan critical quality attributes from extracellular media as a proxy. Given its accuracy, we envisage its potential applications in biomaufactucuring, especially in areas of process development, downstream and upstream bioprocessing.

Multivariate data analysis of process parameters affecting the growth and productivity of stable Chinese hamster ovary cell pools expressing SARS-CoV-2 spike protein as vaccine antigen in early process development

The recent COVID-19 pandemic revealed an urgent need to develop robust cell culture platforms which can react rapidly to respond to this kind of global health issue. Chinese hamster ovary (CHO) stable pools can be a vital alternative to quickly provide gram amounts of recombinant proteins required for early-phase clinical assays. In this study, we analyze early process development data of recombinant trimeric spike protein Cumate-inducible manufacturing platform utilizing CHO stable pool as a preferred production host across three different stirred-tank bioreactor scales (0.75, 1, and 10 L). The impact of cell passage number as an indicator of cell age, methionine sulfoximine (MSX) concentration as a selection pressure, and cell seeding density was investigated using stable pools expressing three variants of concern. Multivariate data analysis with principal component analysis and batch-wise unfolding technique was applied to evaluate the effect of critical process parameters on production variability and a random forest (RF) model was developed to forecast protein production. In order to further improve process understanding, the RF model was analyzed with Shapley value dependency plots so as to determine what ranges of variables were most associated with increased protein production. Increasing longevity, controlling lactate build-up, and altering pH deadband are considered promising approaches to improve overall culture outcomes. The results also demonstrated that these pools are in general stable expressing similar level of spike proteins up to cell passage 11 (~31 cell generations). This enables to expand enough cells required to seed large volume of 200-2000 L bioreactor.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Modulating antibody effector functions by Fc glycoengineering

Antibody based drugs, including IgG monoclonal antibodies, are an expanding class of therapeutics widely employed to treat cancer, autoimmune and infectious diseases. IgG antibodies have a conserved N-glycosylation site at Asn297 that bears complex type N-glycans which, along with other less conserved N- and O-glycosylation sites, fine-tune effector functions, complement activation, and half-life of antibodies. Fucosylation, galactosylation, sialylation, bisection and mannosylation all generate glycoforms that interact in a specific manner with different cellular antibody receptors and are linked to a distinct functional profile. Antibodies, including those employed in clinical settings, are generated with a mixture of glycoforms attached to them, which has an impact on their efficacy, stability and effector functions. It is therefore of great interest to produce antibodies containing only tailored glycoforms with specific effects associated with them. To this end, several antibody engineering strategies have been developed, including the usage of engineered mammalian cell lines, in vitro and in vivo glycoengineering.

Real time on-line amino acid analysis to explore amino acid trends and link to glycosylation outcomes of a model monoclonal antibody

Bioreactor parameters can have significant effects on the quantity and quality of biotherapeutics. Monoclonal antibody products have one particularly important critical quality attribute being the distribution of product glycoforms. N-linked glycosylation affects the therapeutic properties of the antibody including effector function, immunogenicity, stability, and clearance rate. Our past work revealed that feeding different amino acids to bioreactors altered the productivity and glycan profiles. To facilitate real-time analysis of bioreactor parameters and the glycosylation of antibody products, we developed an on-line system to pull cell-free samples directly from the bioreactors, chemically process them, and deliver them to a chromatography-mass spectroscopy system for rapid identification and quantification. We were able to successfully monitor amino acid concentration on-line within multiple reactors, evaluate glycans off-line, and extract four principal components to assess the amino acid concentration and glycosylation profile relationship. We found that about a third of the variability in the glycosylation data can be predicted from the amino acid concentration. Additionally, we determined that the third and fourth principal component accounts for 72% of our model's predictive power, with the third component indicated to be positively correlated with latent metabolic processes related to galactosylation. Here we present our work on rapid online spent media amino acid analysis and use the determined trends to collate with glycan time progression, further elucidating the correlation between bioreactor parameters such as amino acid nutrient profiles, and product quality. We believe such approaches may be useful for maximizing efficiency and reducing production costs for biotherapeutics.

Recent Advances in Biologic Therapeutic N-Glycan Preparation Techniques and Analytical Methods for Facilitating Biomanufacturing Automation

N-glycosylation is a post-translational modification that occurs during the production of monoclonal antibody (mAb) therapeutics. During production of mAb based therapeutics the use of various hosts and cell culture additives attribute to glycan heterogeneity. The safety and efficacy of monoclonal antibodies with mechanism of actions that utilize Fc effector functions can be negatively impacted by glycan heterogeneity and thus is often considered a critical quality attribute (CQA). In this mini review, we discuss recent advances in mAb sample preparation specifically focused on denaturation, enzymatic processing, and released glycans derivatization methods. Additionally, we review the recent advances in characterization of released and intact N-glycans using chromatography, capillary electrophoresis, and mass spectrometry techniques with a focus on rapid, automated approaches that support analysis of glycosylation profiles of biopharmaceuticals. We delve into advances within sample preparation techniques that allow for rapid and robust sample preparation as well as how these techniques are being used for innovative at-line high-throughput screening and process analytical technology (PAT). The future of biomanufacturing is focused on decreasing process costs while increasing process understanding and quality for novel biologic candidates and biosimilars. Therefore, advances in PAT for biotherapeutics will positively influence current manufacturing practices and enable further bioprocess automation.

The Mannose in the Mirror: A Reflection on the Pharmacokinetic Impact of High Mannose Glycans of Monoclonal Antibodies in Biosimilar Development

Biosimilar development has a well-documented foundation of product quality and extensive comparative analytics providing the bulk of the "totality of the evidence" that a proposed product is biosimilar to its reference product. This work provides a retrospective evaluation of a single critical quality attribute-high mannose glycans for monoclonal antibody biosimilars. Given the well-established conclusion that high mannose glycans can impact pharmacokinetic (PK) profile, we performed a retrospective evaluation of 21 monoclonal antibody biosimilar programs (those licensed before April 2022), their levels of glycans, and the methods used to study them. We provide herein a summary of the methods used and their relative performance. We also present a subset analysis for seven biosimilar products with levels of high mannose that differ from the corresponding reference product (and where other differences in quality attributes between the two that may influence PK profile were not observed or considered minor) and compared the PK profiles. Critically, this analysis has demonstrated that the measurement of glycan profiles is highly precise, reproducible within and across programs, and can detect differences in mannose levels, even those that do not impact PK. These results provide support that analytics rather than pharmacokinetic data may be sufficient to predict whether differences within a certain magnitude of this attribute are likely to impact PK. This work enhances the Agency's understanding of this issue allowing for better understanding of challenges faced by the biotechnology industry developing biosimilars.

Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

Using MVDA with stoichiometric balances to optimize amino acid concentrations in chemically defined CHO cell culture medium for improved culture performance

Chemically defined (CD) media are routinely used in the production of biologics in Chinese hamster ovary (CHO) cell culture and provide enhanced raw material control. Nutrient optimized CD media is an important path to increase cell growth and monoclonal antibody (mAb) productivity in recombinant CHO cell lines. However, nutrient optimization efforts for CD media typically rely on multifactorial and experimental design of experiment approaches or complex mathematical models of cellular metabolism or gene expression systems. Moreover, the majority of these efforts are aimed at amino acids since they constitute essential nutrients in CD media as they directly contribute to biomass and protein production. In this study, we demonstrate the utilization of multivariate data analytics (MVDA) coupled with amino acid stoichiometric balances (SBs) to increased cell growth and mAb productivity in efforts to support CD media development efforts. SBs measure the difference between theoretical demand of amino acids and the empirically measured fluxes to identify various catabolic or anabolic states of the cell. When coupled with MVDA, the statistical models were not only able to highlight key amino acids toward cell growth or productivity, but also provided direction on metabolic favorability of the amino acid. Experimental validation of our approach resulted in a 55% increase in total cell growth and about an 80% increase in total mAb productivity. Increased specific consumption of stoichiometrically balanced amino acids and decreased specific consumption of glucose was also observed in optimized CD media suggesting favorable consumption of desired nutrients and a potential for energy redistribution toward increased cellular growth and mAb productivity.

Impacts on product quality attributes of monoclonal antibodies produced in CHO cell bioreactor cultures during intentional mycoplasma contamination events

A mycoplasma contamination event in a biomanufacturing facility can result in costly cleanups and potential drug shortages. Mycoplasma may survive in mammalian cell cultures with only subtle changes to the culture and penetrate the standard 0.2‐µm filters used in the clarification of harvested cell culture fluid. Previously, we reported a study regarding the ability of Mycoplasma arginini to persist in a single‐use, perfusion rocking bioreactor system containing a Chinese hamster ovary (CHO) DG44 cell line expressing a model monoclonal immunoglobulin G 1 (IgG1) antibody. Our previous work showed that M. arginini affects CHO cell growth profile, viability, nutrient consumption, oxygen use, and waste production at varying timepoints after M. arginini introduction to the culture. Careful evaluation of certain identified process parameters over time may be used to indicate mycoplasma contamination in CHO cell cultures in a bioreactor before detection from a traditional method. In this report, we studied the changes in the IgG1 product quality produced by CHO cells considered to be induced by the M. arginini contamination events. We observed changes in critical quality attributes correlated with the duration of contamination, including increased acidic charge variants and high mannose species, which were further modeled using principal component analysis to explore the relationships among M. arginini contamination, CHO cell growth and metabolites, and IgG1 product quality attributes. Finally, partial least square models using NIR spectral data were used to establish predictions of high levels (≥10⁴ colony‐forming unit [CFU/ml]) of M. arginini contamination, but prediction of levels below 10⁴ CFU/ml were not reliable. Contamination of CHO cells with M. arginini resulted in significant reduction of antibody product quality, highlighting the importance of rapid microbiological testing and mycoplasma testing during particularly long upstream bioprocesses to ensure product safety and quality.

Multivariate data analysis of growth medium trends affecting antibody glycosylation

Use of multivariate data analysis for the manufacturing of biologics has been increasing due to more widespread use of data-generating process analytical technologies (PAT) promoted by the US FDA. To generate a large dataset on which to apply these principles, we used an in-house model CHO DG44 cell line cultured in automated micro bioreactors alongside PAT with four commercial growth media focusing on antibody quality through N-glycosylation profiles. Using univariate analyses, we determined that different media resulted in diverse amounts of terminal galactosylation, high mannose glycoforms, and aglycosylation. Due to the amount of in-process data generated by PAT instrumentation, multivariate data analysis was necessary to ascertain which variables best modeled our glycan profile findings. Our principal component analysis revealed components that represent the development of glycoforms into terminally galacotosylated forms (G1F and G2F), and another that encompasses maturation out of high mannose glycoforms. The partial least squares model additionally incorporated metabolic values to link these processes to glycan outcomes, especially involving the consumption of glutamine. Overall, these approaches indicated a tradeoff between cellular productivity and product quality in terms of the glycosylation. This work illustrates the use of multivariate analytical approaches that can be applied to complex bioprocessing problems for identifying potential solutions.