rapid-antibody-glycoengineering-in-chinese-hamster-ovary-cells

Rapid Antibody Glycoengineering in Chinese Hamster Ovary Cells

Recombinant monoclonal antibodies bind specific molecular targets and, subsequently, induce an immune response or inhibit the binding of other ligands. ...

Imperial College London

GlaxoSmithKline Research and Development

Exerting control over the glycan moieties of antibody therapeutics is highly desirable from a product safety and batch-to-batch consistency perspective. Strategies to improve antibody productivity may compromise quality, while interventions for improving glycoform distribution can adversely affect cell growth and productivity. Process design therefore needs to consider the trade-off between preserving cellular health and productivity while enhancing antibody quality. In this work, we present a modeling platform that quantifies the impact of glycosylation precursor feeding - specifically that of galactose and uridine - on cellular growth, metabolism as well as antibody productivity and glycoform distribution. The platform has been parameterized using an initial training data set yielding an accuracy of ±5% with respect to glycoform distribution. It was then used to design an optimized feeding strategy that enhances the final concentration of galactosylated antibody in the supernatant by over 90% compared with the control without compromising the integral of viable cell density or final antibody titer. This work supports the implementation of Quality by Design towards higher-performing bioprocesses.

Model-based optimization of antibody galactosylation in CHO cell culture.

Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.

Harnessing the potential of artificial neural networks for predicting protein glycosylation.

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.

The era of big data: Genome-scale modelling meets machine learning.

The addition of nutrients and accumulation of metabolites in a fed-batch culture of Chinese hamster ovary (CHO) cells leads to an increase in extracellular osmolality in late stage culture. Herein, we explore the effect of osmolality on CHO cell growth, specific monoclonal antibody (mAb) productivity and glycosylation achieved with the addition of NaCl or the supplementation of a commercial feed. Although both methods lead to an increase in specific antibody productivity, they have different effects on cell growth and antibody production. Osmolality modulation using NaCl up to 470 mOsm kg had a consistently positive effect on specific antibody productivity and titre. The addition of the commercial feed achieved variable results: specific mAb productivity was increased, yet cell growth rate was significantly compromised at high osmolality values. As a result, Feed C addition to 410 mOsm kg was the only condition that achieved a significantly higher mAb titre compared to the control. Additionally, Feed C supplementation resulted in a significant reduction in galactosylated antibody structures. Cell volume was found to be positively correlated to osmolality; however, osmolality alone could not account for observed changes in average cell diameter without considering cell cycle variations. These results help delineate the overall effect of osmolality on titre and highlight the potentially negative effect of overfeeding on cell growth.

Osmolality Effects on CHO Cell Growth, Cell Volume, Antibody Productivity and Glycosylation.

Understanding protein folding in different environmental conditions is fundamentally important for predicting protein structures and developing innovative antibody formulations. While the thermodynamics and kinetics of folding and unfolding have been extensively studied by computational methods, experimental methods for determining antibody conformational transition pathways are lacking. Motivated to fill this gap, we prepared a series of unique formulations containing a high concentration of a chimeric immunoglobin G4 (IgG4) antibody with different excipients in the presence and absence of the ionic liquid (IL) choline dihydrogen phosphate. We determined the effects of different excipients and IL on protein thermal and structural stability by performing variable temperature circular dichroism and bio-layer interferometry analyses. To further rationalise the observations of conformational changes with temperature, we carried out molecular dynamics simulations on a single antibody binding fragment from IgG4 in the different formulations, at low and high temperatures. We developed a methodology to study the conformational transitions and associated thermodynamics of biomolecules, and we showed IL-induced conformational transitions. We showed that the increased propensity for conformational change was driven by preferential binding of the dihydrogen phosphate anion to the antibody fragment. Finally, we found that a formulation containing IL with sugar, amino acids and surfactant is a promising candidate for stabilising proteins against conformational destabilisation and aggregation. We hope that ultimately, we can help in the quest to understand the molecular basis of the stability of antibodies and protein misfolding phenomena and offer new candidate formulations with the potential to revive lost therapeutic candidates.

An experimental approach probing the conformational transitions and energy landscape of antibodies: a glimmer of hope for reviving lost therapeutic candidates using ionic liquid.

N-glycosylation is of paramount importance for understanding the mechanisms of various human diseases and ensuring the safety and efficacy of biotherapeutics. Traditional glycan analysis techniques include LC-based separations and MALDI-TOF-MS identification. However, the current state-of-the-art methods lack throughput and structural information, include laborious sample preparation procedures and require large sample volumes. Capillary electrophoresis (CE) has long been used for the screening and reliable quantitation of glycans, but its applications have been limited. Because of its speed, sensitivity and complementarity with standard glycan analysis techniques, CE is currently emerging as one of the most versatile and adaptable methods for glycan analysis in both academia and industry. Herein, we review the latest advancements in CE-based applications to glycomics and glycoproteomics within both the biopharmaceutical and clinical sectors.

Hitting the sweet spot with capillary electrophoresis: advances in N-glycomics and glycoproteomics.

The impact of the glycan distribution on the in vivo function and half-life of monoclonal antibodies has long motivated the genetic engineering of producer cells to achieve structures that enhance efficacy, safety and stability. To facilitate glycoengineering of IgG-producing Chinese hamster ovary cells, we present a rapid protocol that involves the use of RNA interference for the knockdown of genes of interest coupled with capillary gel electrophoresis and laser-induced fluorescence detection (CGE-LIF) for fast, high-throughput glycan analysis. We apply this methodology to the Fut8 gene, responsible for the addition of core fucose, which is a typical target for increasing antibody-dependent cellular cytotoxicity.

Rapid Antibody Glycoengineering in CHO Cells Via RNA Interference and CGE-LIF N-Glycomics.

Coexistence of different populations of cells and isolation of tasks can provide enhanced robustness and adaptability or impart new functionalities to a culture. However, generating stable cocultures involving cells with vastly different growth rates can be challenging. To address this, we developed living analytics in a multilayer polymer shell (LAMPS), an encapsulation method that facilitates the coculture of mammalian and bacterial cells. We leverage LAMPS to preprogram a separation of tasks within the coculture: growth and therapeutic protein production by the mammalian cells and l-lactate biosensing by  encapsulated within LAMPS. LAMPS enable the formation of a synthetic bacterial-mammalian cell interaction that enables a living biosensor to be integrated into a biomanufacturing process. Our work serves as a proof-of-concept for further applications in bioprocessing since LAMPS combine the simplicity and flexibility of a bacterial biosensor with a viable method to prevent runaway growth that would disturb mammalian cell physiology.

Article	Year
Model-based optimization of antibody galactosylation in CHO cell culture. Biotechnology and bioengineering\| PubMed ID: 30802295	2019
Harnessing the potential of artificial neural networks for predicting protein glycosylation. Metabolic engineering communications\| PubMed ID: 32489858	2020
The era of big data: Genome-scale modelling meets machine learning. Computational and structural biotechnology journal\| PubMed ID: 33240470	2020
Osmolality Effects on CHO Cell Growth, Cell Volume, Antibody Productivity and Glycosylation. International journal of molecular sciences\| PubMed ID: 33804825	2021
An experimental approach probing the conformational transitions and energy landscape of antibodies: a glimmer of hope for reviving lost therapeutic candidates using ionic liquid. Chemical science\| PubMed ID: 34349928	2021
Hitting the sweet spot with capillary electrophoresis: advances in N-glycomics and glycoproteomics. Current opinion in biotechnology\| PubMed ID: 34438131	2021
Rapid Antibody Glycoengineering in CHO Cells Via RNA Interference and CGE-LIF N-Glycomics. Methods in molecular biology (Clifton, N.J.)\| PubMed ID: 34611868	2022
Polymer Encapsulation of Bacterial Biosensors Enables Coculture with Mammalian Cells. ACS synthetic biology\| PubMed ID: 35245022	2022

Pavlos Kotidis

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