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The ability to certify feasibility in process design and process operations is crucial in many applications. This includes quality-by-design in pharmaceutical manufacturing, where a key element is the characterization of the design space to better understand the links between materials, processes and products. Sampling-based approaches are versatile but they are cursed by dimensionality, which currently limits their application to problems in a few process variables only. We propose a decomposition approach that enables feasibility characterization for nominal settings of uncertain parameters in acyclic multi-unit processes by sampling. Our methodology leverages problem structure to decompose unit-wise, and deploys surrogate models to couple the resultant subproblems. We demonstrate it on a serial, batch chemical reactor network. In future research, we will extend this framework to cyclic multi-unit processes and the presence of parameter uncertainty.

Glycosylation is a critical post-translational protein modification that affects folding, half-life and functionality. Glycosylation is a non-templated and heterogeneous process because of the promiscuity of the enzymes involved. We describe a platform for sequential glycosylation reactions for tailored sugar structures (SUGAR-TARGET) that allows bespoke, controlled N-linked glycosylation in vitro enabled by immobilized enzymes produced with a one-step immobilization/purification method. We reconstruct a reaction cascade mimicking a glycosylation pathway where promiscuity naturally exists to humanize a range of proteins derived from different cellular systems, yielding near-homogeneous glycoforms. Immobilized β-1,4-galactosyltransferase is used to enhance the galactosylation profile of three IgGs, yielding 80.2–96.3% terminal galactosylation. Enzyme recycling is demonstrated for a reaction time greater than 80 h. The platform is easy to implement, modular and reusable and can therefore produce homogeneous glycan structures derived from various hosts for functional and clinical evaluation.

ABSTRACTHost cell proteins (HCPs) are endogenous impurities, and their proteolytic and binding properties can compromise the integrity, and, hence, the stability and efficacy of recombinant therapeutic proteins such as monoclonal antibodies (mAbs). Nonetheless, purification of mAbs currently presents a challenge because they often co-elute with certain HCP species during the capture step of protein A affinity chromatography. A Quality-by-Design (QbD) strategy to overcome this challenge involves identifying residual HCPs and tracing their source to the harvested cell culture fluid (HCCF) and the corresponding cell culture operating parameters. Then, problematic HCPs in HCCF may be reduced by cell engineering or culture process optimization. Here, we present experimental results linking cell culture temperature and post-protein A residual HCP profile. We had previously reported that Chinese hamster ovary cell cultures conducted at standard physiological temperature and with a shift to mild hypothermia on day 5 produced HCCF of comparable product titer and HCP concentration, but with considerably different HCP composition. In this study, we show that differences in HCP variety at harvest cascaded to downstream purification where different residual HCPs were present in the two sets of samples post-protein A purification. To detect low-abundant residual HCPs, we designed a looping liquid chromatography-mass spectrometry method with continuous expansion of a preferred, exclude, and targeted peptide list. Mild hypothermic cultures produced 20% more residual HCP species, especially cell membrane proteins, distinct from the control. Critically, we identified that half of the potentially immunogenic residual HCP species were different between the two sets of samples.

As efforts for selecting high-producing cells during cell line development are moving toward the use of high-throughput, automated methods, there is considerable promise for identifying suitable markers arising from transcriptomic, proteomic and metabolomic studies. Herein we discuss the opportunities presented from recent characterization studies of high producers as well as comparisons with non- and lower-producing clones. We further put forward unique characteristics of cell lines with high productivity, which can be used for cell line screening and genetic engineering.

Therapeutic glycoproteins represent one of the most important classes of products in the pharmaceutical industry, accounting for 77 high-value drugs out of 642 pharmaceuticals approved by the European Medicines Agency. Their therapeutic efficacy, serum half-life and immunogenicity depend on glycosylation, a complex and prominent post-translational event, which in turn is influenced by manufacturing process conditions. For this reason, protein glycosylation is a critical quality attribute for these drugs. Herein, we review the impact of glycosylation on product function, and the role of manufacturing conditions on the resulting glycoform distribution. We further present promising developments in terms of alternative, genetically engineered hosts, as well as advances in process operation that influence the glycan profile of the recombinant product. Finally, we review work on dynamic mathematical modeling for protein glycosylation that allows researchers to evaluate genetic engineering and process operation strategies in silico, with the aim of guiding experimentation. We demonstrate that such model-based approaches, when substantiated by experimental evidence, can support the quality by design initiative and expedite process development.

Post-mortem analyses of human brain tissue samples from patients suffering from neurodegenerative disorders have demonstrated dysfunction of the endoplasmic reticulum (ER). A common characteristic of the aforementioned disorders is the intracellular accumulation and aggregation of proteins due to genetic mutations or exogenous factors, leading to the activation of a stress mechanism known as the unfolded protein response (UPR). This mechanism aims to restore cellular homeostasis, however, if prolonged, can trigger pro-apoptotic signals, which are thought to contribute to neuronal cell death. The authors present evidence to support the role of ER stress-induced apoptosis in Alzheimer’s, Parkinson’s and Huntington’s diseases, and further examine the interplay between ER dyshomeostasis and mitochondrial dysfunction, and the function of reactive oxygen species (ROS) and calcium ions (Ca2) in the intricate relationship between the two organelles. Possible treatments for neurodegenerative diseases that are based on combating ER stress are finally presented.

A systematic research strategy is proposed for the control and optimisation of monoclonal antibody (MAb) production from mammalian cell cultures. This approach consists of a novel model-based methodology for the design of dynamic experiments to minimise the required experimentation and to improve parameter precision, as well as model-based control and optimisation techniques. This framework is to be applied to a novel integrated, unstructured, dynamic model, which incorporates growth kinetics, cell death, cell metabolism, cell cycle, and MAb synthesis, glycosylation, and production; the simulation results of which demonstrate its satisfactory predictive capability. For the purpose of unique parameter estimation, global sensitivity analysis is performed on the proposed model, through which the parameters with significant impact on the model output, i.e. MAb concentration, are identified

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.