Search

Process development is typically associated with lengthy wet-lab experiments for the identification of good candidate setups and operating conditions. In this paper, we present the key features of a model-based approach for the identification and assessment of process design space (DSp), integrating the analysis of process performance and flexibility. The presented approach comprises three main steps: (1) model development & problem formulation, (2) DSp identification, and (3) DSp analysis. We demonstrate how such an approach can be used for the identification of acceptable operating spaces that enable the assessment of different operating points and quantification of process flexibility. The above steps are demonstrated on Protein A chromatographic purification of antibody-based therapeutics used in biopharmaceutical manufacturing. • Model-based approach for quantitative design space characterization. • Integrated assessment of design decisions, operating variables and process variabilities. • Application to chromatographic unit operation. [ABSTRACT FROM AUTHOR]

Clinical advances in the space of next-generation vaccines and medicines, together with the advent of platform technologies, have revolutionised the modus operandi of the biopharmaceutical industry. The industry's core mission for uninterrupted delivery of high-quality, efficacious drugs is now further challenged by bold and necessary targets to operate sustainably. In this paper, we discuss how threefold sustainability that integrates economic, environmental, and social objectives can be achieved through a combination of technological advances, expert knowledge and transparent dialogue. We summarise recent advances in biomanufacturing in terms of novel technologies, intracellular and process-level analytics, and computational models before providing an outlook on how Industry 5.0 principles can be adopted and applied to advance industrial practice. [ABSTRACT FROM AUTHOR]

Biologics represent the fastest growing sector of the pharmaceutical industry, yet their manufacture lags significantly behind that of small molecule drugs. This paper discusses the main product-related and process-related challenges during the development and production of therapeutic proteins, with particular focus on product heterogeneity and process monitoring and analytics. Emphasis is placed on novel contributions from the field of computational research that aim to enable the application of model-based process control strategies or are working towards the development of a digital twin of bioprocesses. Lastly, we review promising developments in the paradigm shift from batch to continuous processing. [ABSTRACT FROM AUTHOR]

Highlights • Glycosylation as a Critical Quality Attribute of biopharmaceuticals. • Review of the sixteen mathematical models for protein glycosylation that have been published since 2014. • Use of glycosylation models in bioprocess simulation, glycoengineering and glycoprofiling data analysis. • Unified strategies for modelling protein glycosylation applied to biopharmaceutical quality control and optimisation. Glycosylation is a critical quality attribute of biopharmaceuticals because it is a major source of structural variability that influences the in vivo safety and therapeutic efficacy of these products. Manufacturing process conditions are known to influence the monosaccharide composition and relative abundance of the complex carbohydrates bound to therapeutic proteins. Multiple computational tools have been developed to describe these process/product quality relationships in order to control and optimise the glycosylation of biopharmaceuticals. This review will provide a summary highlighting the strengths and weaknesses of each modelling strategy in their application towards cellular glycoengineering or bioprocess design and control. To conclude, potential unified glycosylation modelling approaches for biopharmaceutical quality assurance are proposed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Bioprocess design is hampered by lack of fundamental process models and data. [•] This hinders the move to a ‘quality-by-design’ (QbD) approach. [•] Quantitative understanding of how process variables affect product quality required. [•] PSE techniques are just beginning to be used to help identify the ‘Design Space’. [•] Increasing application of predictive process models is expected. [ABSTRACT FROM AUTHOR]

Abstract: Fed-batch cultures are used in producing monoclonal antibodies industrially. Existing protocols are developed empirically. Model-based tools aiming to improve productivity are useful with model reliability and computational demand being important. Herein, a systematic framework for developing predictive models is presented comprising of model development, global sensitivity analysis, optimal experimental design for parameter estimation, and predictive capability checking. Its efficacy and validity are demonstrated using a fed-batch structured/unstructured model of antibody-secreting hybridoma cultures. Global sensitivity analysis is first used to identify sensitive model parameters (initial values estimated from batch cultures). Information-rich data from an optimally designed fed-batch experiment are then used to estimate these parameters, resulting in good agreement between simulation and experimental results. Finally, the model''s predictive capability is confirmed by comparison with an independent set of fed-batch cultures. This approach systematises the process of developing predictive cell culture models at a minimum experimental cost, enabling model-based control and optimisation. [Copyright &y& Elsevier]

A comprehensive dynamic model to describe monoclonal antibody (MAb)-producing mammalian cell cultures is presented featuring: (i) cell growth kinetics, (ii) cell metabolism, (iii) MAb synthesis and secretion and (iv) MAb glycosylation. Its simulation results are in good agreement with experimental data for macroscopic culture variables and qualitative observations for glycosylation. The combined model allows us to examine in silico and a priori the impact of feeding schedules on the extent of MAb glycosylation and productivity. Optimisation results for different feeding strategies and their effect on glycosylation patterns are also presented, based on which product quality monitoring strategies can be developed. [Copyright &y& Elsevier]

RNA-based products have emerged as one of the most promising and strategic technologies for global vaccination, infectious disease control, and future therapy development. The assessment of critical quality attributes (CQAs), product–process interactions, relevant process analytical technologies, and process modeling capabilities can feed into a robust Quality by Design (QbD) framework for future development, design, and control of manufacturing processes. QbD implementation will help the RNA technology reach its full potential and will be central to the development, pre-qualification, and regulatory approval of rapid response, disease-agnostic RNA platform production processes. Pfizer-BioNTech's (BNT162b2) and Moderna's (mRNA-1713) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the first RNA-based biologics to be approved for human use and mass produced. RNA technology holds great promise beyond infectious disease prophylaxis, from cancer and gene therapy to treatments against cardiovascular and autoimmune diseases. Current production processes have been developed and scaled up at an unprecedented speed, mostly under a Quality by Testing paradigm. Recent research has highlighted a strong link between product and process development. The application of advanced analytical and modeling technologies could rapidly reshape and digitize manufacturing processes. [ABSTRACT FROM AUTHOR]

The reliability and methodology of genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells have advanced. CHO-GEMs have aided in cell line and process development, thus impacting on biomanufacturing efficiency. An integrative model structure can incorporate multiple layers and capture condition-specific cell regulation. Integration of CHO-GEMs with artificial intelligence (AI) and advanced algorithms will enable autonomous bioreactor management for digital biomanufacturing. Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms. [ABSTRACT FROM AUTHOR]

Downstream processing of protein products of mammalian cell culture currently accounts for the largest fraction of the total production cost. A major challenge is the removal of host cell proteins, which are cell-derived impurities. Host cell proteins are potentially immunogenic and can compromise product integrity during processing and hold-up steps. There is an increasing body of evidence that the type of host cell proteins present in recombinant protein preparations is a function of cell culture conditions and handling of the harvest cell culture fluid. This, in turn, can affect the performance of downstream purification steps as certain species are difficult to remove and may require bespoke process solutions. Herein, we review recent research on the interplay between upstream process conditions, host cell protein composition and their downstream removal in antibody production processes, identifying opportunities for increasing process understanding and control. We further highlight advances in analytical and computational techniques that can enable the application of quality by design. [ABSTRACT FROM AUTHOR]

• Bayesian optimization for sequential-modular flowsheet optimization. • Global sensitivity analysis as a dimensionality reduction technique. • Simulation-based and surrogate-based Bayesian optimization. • Two industrially relevant case studies for plasmid DNA and dimethyl ether production. This work proposes a framework for simulation-based and surrogate-based reduced space Bayesian optimization of process flowsheets. The framework uses global sensitivity analysis for dimensionality reduction via the identification of critical process variables that contribute significantly to the variability of the objective function (e.g. productivity and operating costs). Both simulation- and surrogate-based algorithms are applied to a biopharmaceutical and a chemical process simulator for the production of plasmid DNA and dimethyl ether (DME), respectively. Their capabilities are assessed in terms of the trade-off between computational effectiveness and solution accuracy. Results indicate that simulation-based Bayesian optimization achieves better objective function values, while surrogate-based Bayesian optimization is more computationally effective. [ABSTRACT FROM AUTHOR]

Highlights: [•] Lead optimisation, preclinical development and clinical trials are the most costly stages of drug development. [•] Predicting manufacturing challenges can expedite and reduce costs of development. [•] Computational models can instruct the design of a therapeutic protein. [•] Mechanistic models are useful instruments in the design of a bioprocess. [•] Analytics available on- or at-line are facilitating the application of modelling. [ABSTRACT FROM AUTHOR]

Abstract: This work presents a holistic ‘closed loop’ approach for the development of models of biological systems. The ever-increasing availability of experimental information necessitates the advancement of a systematic methodology to organise and utilise these data. Herein, we present a biological model building framework that maps the treatment of the information from the initial conception of the model, through its experimental validation and finally to its application in model-based optimisation studies. We highlight and discuss current issues associated with the development of mathematical models of biological systems and share our perspective towards a holistic ‘closed loop’ approach that will facilitate the control of the in vitro through the in silico. [Copyright &y& Elsevier]

CE–MS glycoproteomics and glycomics. In intact methods, the glycans are identified while attached to the entire protein backbone, exposing the glycosylation profile and providing information on the macro-heterogeneity and site occupancy. In middle-up and bottom-up analysis, the protein is digested into sububits or smaller peptide fragments, revealing specific glycoforms and elucidating micro-heterogeneity in a site-specific approach. In released glycans techniques, the glycans are completely released from the protein molecule through chemical or enzymatic means. Unlike the middle-up and bottom-up techniques, released glycans do not offer site-specific information, but can achieve excellent levels of sensitivity in glycan microheterogeneity identification. [Display omitted] 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. [ABSTRACT FROM AUTHOR]

Image, graphical abstract • Constrained global sensitivity analysis proposed for design space identification. • cGSA explores range of process inputs in silico subject to output constraints. • Applied to model of antibody-producing Chinese hamster ovary (CHO) cell culture system. • Findings verified experimentally for antibody titre and glycosylation as outputs. The manufacture of protein-based therapeutics presents unique challenges due to limited control over the biotic phase. This typically gives rise to a wide range of protein structures of varying safety and in vivo efficacy. Herein we propose a computational methodology, enabled by the application of constrained Global Sensitivity Analysis, for efficiently exploring the operating range of process inputs in silico and identifying a design space that meets output constraints. The methodology was applied to an antibody-producing Chinese hamster ovary (CHO) cell culture system: we explored >8000 feeding strategies to identify a subset of manufacturing conditions that meet constraints on antibody titre and glycan distribution as an attribute of product quality. Our computational findings were then verified experimentally, confirming the applicability of this approach to a challenging production system. We envisage that this methodology can significantly expedite bioprocess development and increase operational flexibility. [ABSTRACT FROM AUTHOR]

The recently engineered Geobacillus thermoglucosidasius strain is an industrially potent thermophilic ethanologen. We employ a systematic approach to improve our understanding of the fermentation process using cellobiose as the substrate. Dynamic metabolic flux analysis clearly shows that the fluxes from pyruvate to lactate and formate are both strictly constrained throughout the process and that both the maximum ethanol yield (0.46C/C) and the maximum specific productivity (19.8 mmol C (g DCW) −1 h −1 ) occur at late-exponential growth phase. Accordingly, extreme pathway analysis reduces the metabolic network into a macro reaction scheme, on which a dynamic metabolic model is built. The model is validated with experimental data, parameters are identified with confidence intervals, and global sensitivity analysis (Sobol׳ method) is performed. Model-based optimization predicts that ethanol productivity could increase from 34.2 in a typical batch process to 55.3 mmol L −1 h −1 in an optimum fed-batch process with higher ethanol yield. Furthermore, the optimal operating regime was identified to be continuous fermentation process with gas stripping, in which a high ethanol productivity of 113 mmol L −1 h −1 , i.e. , 26.8 mmol C (g DCW) −1 h −1 , corresponding to 90.2% of the maximum theoretical ethanol yield could be achieved. [ABSTRACT FROM AUTHOR]

Abstract: Traditional ethanol fermentation becomes inhibitory to microbial growth at ethanol concentrations that depend on the producer organism, leading to reduced ethanol productivity. Continuous ethanol removal from the fermenter could increase productivity and potentially reduce the cost of product recovery. In this work, continuous ethanol removal via in situ gas stripping in a stirred tank reactor has been investigated as a means of reducing growth inhibition and improving productivity. A dynamic mathematical model that couples ethanol fermentation with gas stripping has been developed. This has been linked to a flash separation model to represent the initial steps of product recovery. Global sensitivity analysis was used to reduce the number of uncertain parameters, the values of which were estimated with satisfactory accuracy using experimental data for ethanol production from a metabolically engineered strain of the thermophile Geobacillus thermoglucosidasius growing on cellobiose. Simulation results show that continuous ethanol fermentation with product removal by gas stripping is feasible, with the associated energy requirement, costs of gas compression and fermenter agitation being a function of the stripping gas flow rate. Finally, the conditions under which gas stripping is a practical product recovery method were established. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

• Stoichiometric model accurately describes CHO cell metabolism. • Artificial neural network used to model secreted antibody glycosylation. • Hybrid framework links extracellular culture conditions to recombinant protein quality. • Framework can be updated in-process with commonly monitored process variables. • Sets basis for bioprocess control. In-process quality control of biotherapeutics, such as monoclonal antibodies, requires computationally efficient process models that use readily measured process variables to compute product quality. Existing kinetic cell culture models can effectively describe the underlying mechanisms but require considerable development and parameterisation effort. Stoichiometric models, on the other hand, provide a generic, parameter-free means for describing metabolic behaviour but do not extend to product quality prediction. We have overcome this limitation by integrating a stoichiometric model of Chinese hamster ovary (CHO) cell metabolism with an artificial neural network that uses the fluxes of nucleotide sugar donor synthesis to compute the profile of Fc N-glycosylation, a critical quality attribute of antibody therapeutics. We demonstrate that this hybrid framework accurately computes glycan distribution profiles using a set of easy-to-obtain experimental data, thus providing a platform for process control applications. [ABSTRACT FROM AUTHOR]

• Development of a hybrid model for CHO cell culture using in silico training datasets • Model development focuses on antibody product synthesis and glycosylation • Glycosylation is one of main quality indicators for therapeutic antibodies • Hybrid model used as basis for the design of dynamic optimisation studies • Hybrid model used for reactive scheduling studies that assure product quality Chinese hamster ovary (CHO) cell culture systems are the most widely used platform for the industrial production of monoclonal antibodies (mAbs). The optimisation of manufacturing conditions for these high-value products is largely conducted off-line with little or no monitoring of mAb quality in-process. Here, we propose DigiGlyc, a hybrid model of these systems that predicts the critical quality attribute of mAb galactosylation. Having shown that DigiGlyc describes a wide range of experimental data well, we demonstrate that it can be used for the design of reactive optimisation studies. This hybrid formulation offers considerable gains in computational speed compared to mechanistic models with no loss in fidelity and can therefore underpin future online control and optimisation studies. [ABSTRACT FROM AUTHOR]