Bioprocess Control: Current Progress and Future Perspectives

Unveiling the potential of specific growth rate control in fed-batch fermentation: bridging the gap between product quantity and quality

During the epoch of sustainable development, leveraging cellular systems for production of diverse chemicals via fermentation has garnered attention. Industrial fermentation, extending beyond strain efficiency and optimal conditions, necessitates a profound understanding of microorganism growth characteristics. Specific growth rate (SGR) is designated as a key variable due to its influence on cellular physiology, product synthesis rates and end-product quality. Despite its significance, the lack of real-time measurements and robust control systems hampers SGR control strategy implementation. The narrative in this contribution delves into the challenges associated with the SGR control and presents perspectives on various control strategies, integration of soft-sensors for real-time measurement and control of SGR. The discussion highlights practical and simple SGR control schemes, suggesting their seamless integration into industrial fermenters. Recommendations provided aim to propose new algorithms accommodating mechanistic and data-driven modelling for enhanced progress in industrial fermentation in the context of sustainable bioprocessing.

Bioprocess in-line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM)

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

Single cell analysis of Chinese hamster ovary cells during a bioprocess using a novel dynamic imaging system

Reliable monitoring of mammalian cells in bioreactors is essential to biopharmaceutical production. Trypan blue exclusion is a method of determining cell density and viability that has been used for over one hundred years to monitor cells in culture and is the current standard method in biomanufacturing. This method has many disadvantages however and there is a growing demand for more detailed and in-line measurements of cell growth in bioreactors. This article assesses a novel dynamic imaging system for single cell analysis. This data shows that comparable total cell density, viable cell density and percentage viability data shown here, generated by the imaging system, aligned well with conventional trypan blue counting methods for an industrially relevant Chinese Hamster Ovary (CHO) cell line. Furthermore, detailed statistical analysis shows that the classification system used by the PharmaFlow system can reveal trends of interest in monitoring the health of mammalian cells over a 6-day bioreactor culture. The system is also capable of sampling at-line, removing the necessity for taking samples off-line and enabling real time monitoring of cells in a bioreactor culture.

Control strategy for biopharmaceutical production by model predictive control

The biopharmaceutical industry is rapidly advancing, driven by the need for cutting-edge technologies to meet the growing demand for life-saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real-time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.

From Efficiency to Yield: Exploring Recent Advances in CHO Cell Line Development for Monoclonal Antibodies

The increasing demand for biosimilar monoclonal antibodies (mAbs) has prompted the development of stable high-producing cell lines while simultaneously decreasing the time required for screening. Existing platforms have proven inefficient, resulting in inconsistencies in yields, growth characteristics, and quality features in the final mAb products. Selecting a suitable expression host, designing an effective gene expression system, developing a streamlined cell line generation approach, optimizing culture conditions, and defining scaling-up and purification strategies are all critical steps in the production of recombinant proteins, particularly monoclonal antibodies, in mammalian cells. As a result, an active area of study is dedicated to expression and optimizing recombinant protein production. This review explores recent breakthroughs and approaches targeted at accelerating cell line development to attain efficiency and consistency in the synthesis of therapeutic proteins, specifically monoclonal antibodies. The primary goal is to bridge the gap between rising demand and consistent, high-quality mAb production, thereby benefiting the healthcare and pharmaceutical industries.

Current achievements, strategies, obstacles, and overcoming the challenges of the protein engineering in Pichia pastoris expression system

Yeasts serve as exceptional hosts in the manufacturing of functional protein engineering and possess industrial or medical utilities. Considerable focus has been directed towards yeast owing to its inherent benefits and recent advancements in this particular cellular host. The Pichia pastoris expression system is widely recognized as a prominent and widely accepted instrument in molecular biology for the purpose of generating recombinant proteins. The advantages of utilizing the P. pastoris system for protein production encompass the proper folding process occurring within the endoplasmic reticulum (ER), as well as the subsequent secretion mediated by Kex2 as a signal peptidase, ultimately leading to the release of recombinant proteins into the extracellular environment of the cell. In addition, within the P. pastoris expression system, the ease of purifying recombinant protein arises from its restricted synthesis of endogenous secretory proteins. Despite its achievements, scientists often encounter persistent challenges when attempting to utilize yeast for the production of recombinant proteins. This review is dedicated to discussing the current achievements in the usage of P. pastoris as an expression host. Furthermore, it sheds light on the strategies employed in the expression system and the optimization and development of the fermentative process of this yeast. Finally, the impediments (such as identifying high expression strains, improving secretion efficiency, and decreasing hyperglycosylation) and successful resolution of certain difficulties are put forth and deliberated upon in order to assist and promote the expression of complex proteins in this prevalent recombinant host.

Current challenges and recent advances on the path towards continuous biomanufacturing

Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

Current and future technologies for monitoring cultured meat: A review

The high population growth rate, massive animal food consumption, fast economic progress, and limited food resources could lead to a food crisis in the future. There is a huge requirement for dietary proteins including cultured meat is being progressed to fulfill the need for meat-derived proteins in the diet. However, production of cultured meat requires monitoring numerous bioprocess parameters. This review presents a comprehensive overview of various widely adopted techniques (optical, spectroscopic, electrochemical, capacitive, FETs, resistive, microscopy, and ultrasound) for monitoring physical, chemical, and biological parameters that can improve the bioprocess control in cultured meat. The methods, operating principle, merits/demerits, and the main open challenges are reviewed with the aim to support the readers in advancing knowledge on novel sensing systems for cultured meat applications.

Steps toward a digital twin for functional food production with increased health benefits

Lactobacillus rhamnosus (L. rhamnosus) is a commensal bacterium with health-promoting properties and with a wide range of applications within the food industry. To improve and optimize the control of L. rhamnosus biomass production in batch and fed-batch bioprocesses, this study proposes the application of artificial neural network (ANN) modelling to improve process control and monitoring, with potential future implementation as a basis for a digital twin. Three ANNs were developed using historical data from ten bioprocesses. These ANNs were designed to predict the biomass in batch bioprocesses with different media compositions, predict biomass in fed-batch bioprocesses, and predict the growth rate in fed-batch bioprocesses. The immunomodulatory effect of the L. rhamnosus samples was examined and found to elicit an anti-inflammatory response as evidenced by the inhibition of IL-6 and TNF-α secretion. Overall, the findings of this study reinforce the potential of ANN modelling for bioprocess optimization aimed at improved control for maximising the volumetric productivity of L. rhamnosus as an immunomodulatory agent with applications in the functional food industry.

An Approach for the Estimation of Concentrations of Soluble Compounds in E. coli Bioprocesses

Accurate estimations of the concentrations of soluble compounds are crucial for optimizing bioprocesses involving Escherichia coli (E. coli). This study proposes a hybrid model structure that leverages off-gas analysis data and physiological parameters, including the average biomass age and specific growth rate, to estimate soluble compounds such as acetate and glutamate in fed-batch cultivations We used a hybrid recurrent neural network to establish the relationships between these parameters. To enhance the precision of the estimates, the model incorporates ensemble averaging and information gain. Ensemble averaging combines varying model inputs, leading to more robust representations of the underlying dynamics in E. coli bioprocesses. Our hybrid model estimates acetates with 1% and 8% system precision using data from the first site and the second site at GSK plc, respectively. Using the data from the second site, the precision of the approach for other solutes was as fallows: isoleucine -8%, lactate and glutamate -9%, and a 13% error for glutamine., These results, demonstrate its practical potential.

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

Automated quality analysis in continuous downstream processes for small-scale applications

Development of integrated, continuous biomanufacturing (ICB) processes brings along the challenge of streamlining the acquisition of data that can be used for process monitoring, product quality testing and process control. Manually performing sample acquisition, preparation, and analysis during process and product development on ICB platforms requires time and labor that diverts attention from the development itself. It also introduces variability in terms of human error in the handling of samples. To address this, a platform for automatic sampling, sample preparation and analysis for use in small-scale biopharmaceutical downstream processes was developed. The automatic quality analysis system (QAS) consisted of an ÄKTA Explorer chromatography system for sample retrieval, storage, and preparation, as well as an Agilent 1260 Infinity II analytical HPLC system for analysis. The ÄKTA Explorer system was fitted with a superloop in which samples could be stored, conditioned, and diluted before being sent to the injection loop of the Agilent system. The Python-based software Orbit, developed at the department of chemical engineering at Lund university, was used to control and create a communication framework for the systems. To demonstrate the QAS in action, a continuous capture chromatography process utilizing periodic counter-current chromatography was set up on an ÄKTA Pure chromatography system to purify the clarified harvest from a bioreactor for monoclonal antibody production. The QAS was connected to the process to collect two types of samples: 1) the bioreactor supernatant and 2) the product pool from the capture chromatography. Once collected, the samples were conditioned and diluted in the superloop before being sent to the Agilent system, where both aggregate content and charge variant composition were determined using size-exclusion and ion-exchange chromatography, respectively. The QAS was successfully implemented during a continuous run of the capture process, enabling the acquisition of process data with consistent quality and without human intervention, clearing the path for automated process monitoring and data-based control.

Advances in bioreactor control for production of biotherapeutic products

Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing

Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

Robust control designs for microalgae cultivation in continuous photobioreactors

Microalgae are used to produce renewable biofuels and high-value components and in bioremediation and CO2 sequestration tasks. These increasing applications, in conjunction with a desirable constant large-scale productivity, motivate the development and application of practical controllers. This paper addresses the application of robust control schemes for microalgae cultivation in continuous photobioreactors. Due to the model uncertainties and external perturbations, robust control designs are required to guarantee the desired microalgae productivity. Furthermore, simple controller designs are desirable for practical implementation purposes. Therefore, two robust control designs are applied and evaluated in this paper for two relevant case studies of microalgae cultivation in photobioreactors. The first control design is based on an enhanced simple-input output model with uncertain estimation. The second control design is the robust nonlinear model predictive control considering different uncertain scenarios. Numerical simulations of two case studies aimed at lipid production and CO2 capture under different conditions are presented to evaluate the robust closed-loop performance.

Robust Control Based on Modeling Error Compensation of Microalgae Anaerobic Digestion

Microalgae are used to produce renewable biofuels (biodiesel, bioethanol, biogas, and biohydrogen) and high-value-added products, as well as in bioremediation and CO2 sequestration tasks. In the case of anaerobic digestion of microalgae, biogas can be produced from mainly proteins and carbohydrates. Anaerobic digestion is a complex process that involves several stages and is susceptible to operational instability due to various factors. Robust controllers with simple structure and design are necessary for practical implementation purposes and to achieve a proper process operation despite process variabilities, uncertainties, and complex interactions. This paper presents the application of a control design based on the modeling error compensation technique for the anaerobic digestion of microalgae. The control design departs from a low-order input–output model by enhancement with uncertainty estimation. The results show that achieving desired organic pollution levels and methanogenic biomass concentrations as well as minimizing the effect of external perturbations on a benchmark case study of the anaerobic digestion of microalgae is possible with the proposed control design.

Advances in Komagataella phaffii Engineering for the Production of Renewable Chemicals and Proteins

The need for a more sustainable society has prompted the development of bio-based processes to produce fuels, chemicals, and materials in substitution for fossil-based ones. In this context, microorganisms have been employed to convert renewable carbon sources into various products. The methylotrophic yeast Komagataella phaffii has been extensively used in the production of heterologous proteins. More recently, it has been explored as a host organism to produce various chemicals through new metabolic engineering and synthetic biology tools. This review first summarizes Komagataella taxonomy and diversity and then highlights the recent approaches in cell engineering to produce renewable chemicals and proteins. Finally, strategies to optimize and develop new fermentative processes using K. phaffii as a cell factory are presented and discussed. The yeast K. phaffii shows an outstanding performance for renewable chemicals and protein production due to its ability to metabolize different carbon sources and the availability of engineering tools. Indeed, it has been employed in producing alcohols, carboxylic acids, proteins, and other compounds using different carbon sources, including glycerol, glucose, xylose, methanol, and even CO2.

Model predictive control for steady-state performance in integrated continuous bioprocesses

Perfusion bioreactors are commonly used for the continuous production of monoclonal antibodies (mAb). One potential benefit of continuous bioprocessing is the ability to operate under steady-state conditions for an extended process time. However, the process performance is often limited by the feedback control of feed, harvest, and bleed flow rates. If the future behavior of a bioprocess can be adequately described, predictive control can reduce set point deviations and thereby maximize process stability. In this study, we investigated the predictive control of biomass in a perfusion bioreactor integrated to a non-chromatographic capture step, in a series of Monte-Carlo simulations. A simple algorithm was developed to estimate the current and predict the future viable cell concentrations (VCC) of the bioprocess. This feature enabled the single prediction controller (SPC) to compensate for process variations that would normally be transported to adjacent units in integrated continuous bioprocesses (ICB). Use of this SPC strategy significantly reduced biomass, product concentration, and harvest flow variability and stabilized the operation over long periods of time compared to simulations using feedback control strategies. Additionally, we demonstrated the possibility of maximizing product yields simply by adjusting perfusion control strategies. This method could be used to prevent savings in total product losses of 4.5–10% over 30 days of protein production.

Exploring synthetic biology for the development of a sensor cell line for automated bioprocess control

Unfavorable process conditions lead to adverse cultivation states, limited cell growth and thus hamper biotherapeutic protein production. Oxygen deficiency or hyperosmolality are among the most critical process conditions and therefore require continuous monitoring. We established a novel sensor CHO cell line with the ability to automatically sense and report unwanted process conditions by the expression of destabilized fluorescent proteins. To this end, an inducible real-time system to detect hypoxia by hypoxia response elements (HREs) of vascular endothelial growth factor (VEGF) origin reporting limitations by the expression of destabilized green fluorescent protein (GFP) was created. Additionally, we established a technique for observing hyperosmolality by exploiting osmotic response elements (OREs) for the expression of unstable blue fluorescent protein (BFP, FKBP-BFP), enabling the simultaneous automated supervision of two bioprocess parameters by using a dual sensor CHO cell line transfected with a multiplexable monitoring system. We finally also provided a fully automated in-line fluorescence microscopy-based setup to observe CHO cells and their response to varying culture conditions. In summary, we created the first CHO cell line, reporting unfavorable process parameters to the operator, and provided a novel and promising sensor technology accelerating the implementation of the process analytical technology (PAT) initiative by innovative solutions.

Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes

The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally, perspectives as to how this can be leveraged to improve both understanding and outcomes of cell culture processes are discussed.

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.