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The rapidly growing market of monoclonal antibodies (mAbs) within the biopharmaceutical industry has incentivised numerous works on the design of more efficient production processes. Protein A affinity chromatography is regarded as one of the best processes for the capture of mAbs. Although the screening of Protein A resins has been previously examined, process flexibility has not been considered to date. Examining performance alongside flexibility is crucial for the design of processes that can handle disturbances arising from the feed stream. In this work, we present a model-based approach for the identification of design spaces, enhanced by machine learning. We demonstrate its capabilities on the design of a Protein A chromatography unit, screening five industrially relevant resins. The computational results favourably compare to experimental data and a resin performance comparison is presented. An improvement on the computational time by a factor of 300,000 is achieved using the machine learning aided methodology. This allowed for the identification of 5,120 different design spaces in only 19 h., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Maria M Papathanasiou reports financial support was provided by Engineering and Physical Sciences Research Council. Cleo Kontoravdi & Maria M Papathanasiou reports financial support was provided by Engineering and Physical Sciences Research Council. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Improved understanding of heterogeneous cellulose hydrolysis by cellulases is the basis for optimising enzymatic catalysis-based cellulosic biorefineries. A detailed mechanistic model is developed to describe the dynamic adsorption/desorption and synergistic chain-end scissions of cellulases (endoglucanase, exoglucanase, and β-glucosidase) upon amorphous cellulose. The model can predict evolutions of the chain lengths of insoluble cellulose polymers and production of soluble sugars during hydrolysis. Simultaneously, a modelling framework for uncertainty analysis is built based on a quasi-Monte-Carlo method and global sensitivity analysis, which can systematically identify key parameters, help refine the model and improve its identifiability. The model, initially comprising 27 parameters, is found to be over-parameterized with structural and practical identification problems under usual operating conditions (low enzyme loadings). The parameter estimation problem is therefore mathematically ill posed. The framework allows us, on the one hand, to identify a subset of 13 crucial parameters, of which more accurate confidence intervals are estimated using a given experimental dataset, and, on the other hand, to overcome the identification problems. The model's predictive capability is checked against an independent set of experimental data. Finally, the optimal composition of cellulases cocktail is obtained by model-based optimisation both for enzymatic hydrolysis and for the process of simultaneous saccharification and fermentation.

With the current wealth of transcriptomic data, it is possible to design genetically-encoded biosensors for the detection of stress responses and apply these to high-throughput bioprocess development and monitoring of cellular health. Such biosensors can sense extrinsic factors such as nutrient or oxygen deprivation and shear stress, as well as intrinsic stress factors like oxidative damage and unfolded protein accumulation. Alongside, there have been developments in biosensing hardware and software applicable to the field of genetically-encoded biosensors in the near future. This review discusses the current state-of-the-art in biosensors for monitoring cultures during biological manufacturing and the future challenges for the field. Connecting the individual achievements into a coherent whole will enable the application of genetically-encoded biosensors in industry., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Glycosylation is a critical attribute of therapeutic proteins given its impact on the clinical safety and efficacy of these molecules. The biochemical process of glycosylation is inextricably dependent on metabolism and ensuing availability of nucleotides and nucleotide sugars (NSs) during cell culture. Herein, we present a comprehensive methodology to extract and quantify these metabolites from cultured cells. To establish the full protocol, two methods for the extraction of these compounds were evaluated for efficiency, and the requirement for quenching and washing the sample was assessed. A chromatographic method based on anion exchange has been optimized to separate and quantify eight nucleotides and nine NSs in less than 30 min. Degradation of nucleotides and NSs under extraction conditions was evaluated to aid in selection of the most efficient extraction protocol. We conclude that the optimized chromatographic method is quick, robust, and sensitive for quantifying nucleotides and NSs. Furthermore, our results show that samples taken from cell culture should be treated with 50% v/v acetonitrile and do not require quenching or washing for reliable extraction of nucleotides and NSs. This comprehensive protocol should prove useful in determining the impact of nucleotide and NS metabolism on protein glycosylation., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Mathematical modelling is a powerful tool for the organisation and analysis of biological data. Both stoichiometric and kinetic models have been applied to the investigation of cellular metabolism in a variety of bacterial, yeast and mammalian hosts to elucidate metabolic network structure, optimise fermentation conditions and improve genetic engineering strategies among others. The current challenge is to interrelate different levels of information, from the genome to the transcriptome, the proteome and the metabolome, and experimental data from widely used high-throughput techniques to recreate a given phenotype and ultimately to make predictions about network and cellular behaviour., (Copyright © 2011 Elsevier Ltd. All rights reserved.)