Oxygen Uptake Rate Soft-Sensing via Dynamic k L a Computation: Cell Volume and Metabolic Transition Prediction in Mammalian Bioprocesses

Investigating the metabolic load of monoclonal antibody production conveyed to an inducible CHO cell line using a transfer-rate online monitoring system

Shake flasks are a foundational tool in early process development by allowing high throughput exploration of the design space. However, lack of online data at this scale can hamper rapid decision making. Oxygen transfer rate (OTR) monitoring has been readily applied as an online process characterization tool at the benchtop bioreactor scale. Recent advances in modern sensing technology have allowed OTR monitoring to be available at the shake flask level. It is now possible to multiplex time-of-action (e.g., Induction, temperature shift, pH shift, feeding initiation, point of harvest) characterization studies by relying on careful analysis of OTR profile kinetics. As a result, there is potential to save time and capital expenditures while exploring process intensification studies though accurate and physiologically relevant online data. In this article, we detail the application of OTR monitoring to characterize the impact that recombinant protein production has on an inducible CHO cell line expressing Palivizumab. We then test out time-of-action studies to intensify protein production outcomes. We observe that recombinant protein expression causes a metabolic load that diminishes potential biomass growth. As a result, when compared to a control standard process, delaying induction and temperature shift has the potential to improve viable cell densities (VCD) by 2-fold thus increasing recombinant protein yield by over 30 %. The study also demonstrates that OTR can serve as a useful tool to detect cessation of exponential growth. Consequently, time-of-action points that are characteristic of inducible systems can be formulated accurately and reliably to maximize production performance.

CHO stable pool fed-batch process development of SARS-CoV-2 spike protein production: Impact of aeration conditions and feeding strategies

Technology scale-up and transfer are a fundamental and critical part of process development in biomanufacturing. Important bioreactor hydrodynamic characteristics such as working volume, overhead gas flow rate, volumetric power input (P/V), impeller type, agitation regimen, sparging aeration strategy, sparger type, and kLa must be selected based on key performance indicators (KPI) to ensure a smooth and seamless process scale-up and transfer. Finding suitable operational setpoints and developing an efficient feeding regimen to ensure process efficacy and consistency are instrumental. In this investigation, process development of a cumate inducible Chinese hamster ovary (CHO) stable pool expressing trimeric SARS-CoV-2 spike protein in 1.8 L benchtop stirred-tank bioreactors is detailed. Various dissolved oxygen levels and aeration air caps were studied to determine their impact on cell growth and metabolism, culture longevity, and endpoint product titers. Once hydrodynamic conditions were tuned to an optimal zone, various feeding strategies were explored to increase culture performance. Dynamic feedings such as feeding based on current culture volume, viable cell density (VCD), oxygen uptake rate (OUR), and bio-capacitance signals were tested and compared to standard bolus addition. Increases in integral of viable cell concentration (IVCC) (1.25-fold) and protein yield (2.52-fold), as well as greater culture longevity (extension of 5 days) were observed in dynamic feeding strategies when compared to periodic bolus feeding. Our study emphasizes the benefits of designing feeding strategies around metabolically relevant signals such as OUR and bio-capacitance signals.

Soft sensor for viable cell counting by measuring dynamic oxygen uptake rate

Regulatory authorities in biopharmaceutical industry emphasize process design by process understanding but applicable tools that are easy to implement are still missing. Soft sensors are a promising tool for the implementation of the Quality by Design (QbD) approach and Process Analytical Technology (PAT). In particular, the correlation between viable cell counting and oxygen consumption was investigated, but problems remained: Either the process had to be modified for excluding CO2 in pH control, or complex kLa models had to be set up for specific processes. In this work, a non-invasive soft sensor for simplified on-line cell counting based on dynamic oxygen uptake rate was developed with no need of special equipment. The dynamic oxygen uptake rates were determined by automated and periodic interruptions of gas supply in DASGIP® bioreactor systems, realized by a programmed Visual Basic script in the DASware® control software. With off-line cell counting, the two parameters were correlated based on linear regression and led to a robust model with a correlation coefficient of 0.92. Avoidance of oxygen starvation was achieved by gas flow reactivation at a certain minimum dissolved oxygen concentration. The soft sensor model was established in the exponential growth phase of a Chinese Hamster Ovary fed-batch process. Control studies showed no impact on cell growth by the discontinuous gas supply. This soft sensor is the first to be presented that does not require any specialized additional equipment as the methodology relies solely on the direct measurement of oxygen consumed by the cells in the bioreactor.

Multivariate data analysis of process parameters affecting the growth and productivity of stable Chinese hamster ovary cell pools expressing SARS-CoV-2 spike protein as vaccine antigen in early process development

The recent COVID-19 pandemic revealed an urgent need to develop robust cell culture platforms which can react rapidly to respond to this kind of global health issue. Chinese hamster ovary (CHO) stable pools can be a vital alternative to quickly provide gram amounts of recombinant proteins required for early-phase clinical assays. In this study, we analyze early process development data of recombinant trimeric spike protein Cumate-inducible manufacturing platform utilizing CHO stable pool as a preferred production host across three different stirred-tank bioreactor scales (0.75, 1, and 10 L). The impact of cell passage number as an indicator of cell age, methionine sulfoximine (MSX) concentration as a selection pressure, and cell seeding density was investigated using stable pools expressing three variants of concern. Multivariate data analysis with principal component analysis and batch-wise unfolding technique was applied to evaluate the effect of critical process parameters on production variability and a random forest (RF) model was developed to forecast protein production. In order to further improve process understanding, the RF model was analyzed with Shapley value dependency plots so as to determine what ranges of variables were most associated with increased protein production. Increasing longevity, controlling lactate build-up, and altering pH deadband are considered promising approaches to improve overall culture outcomes. The results also demonstrated that these pools are in general stable expressing similar level of spike proteins up to cell passage 11 (~31 cell generations). This enables to expand enough cells required to seed large volume of 200-2000 L bioreactor.

3D printed autoclavable biocompatible biodegradable bioreactor vessels with integrated sparger made from poly-lactic acid

3D printing has become widespread for the manufacture of parts in various industries and enabled radically new designs. This trend has not spread to bioprocess development yet, due to a lack of material suitable for the current workflow, including sterilization by autoclaving. This work demonstrates that commercially available heat temperature stable poly-lactic acid (PLA) can be used to easily manufacture novel bioreactor vessels with included features like harvest tubes and 3D printed spargers. Temperature responsiveness was tested for PLA, temperature stable PLA (PLA-HP) and glass for temperatures relevant for insect and mammalian cell culture, including temperature shifts within the process. Stability at 27 °C and 37 °C as well as temperature shifts to 22 °C and 32 °C showed acceptable performance with slightly higher temperature overshoot for 3D printed vessels. A stable temperature is reached after 2 h for PLA, 3 h for PLA-HP and 1 h for glass reactors. Temperature can be maintained with a fluctuation of 0.1 °C for all materials. A 3D printed sparger design directly integrated into the vessel wall and bottom was tested under three different conditions (0.3 SLPH and 27 °C, 3 SLPH and 37 °C and 13 SLPH and 37 °C). The 3D printed sparger showed a better kLa than the L-Sparger with more pronounced differences for higher flowrates. An insect cell culture run in the novel vessel exhibited the same growth behavior as that in standard glass vessels, reaching the same maximum cell concentration. Being 3D printed from biodegradable materials, these bioreactors offer design flexibility for novel bioreactor formats. Additionally, their autoclavability allows seamless integration into standard workflows.

Innovative fixed bed bioreactor platform: Enabling linearly scalable adherent cell biomanufacturing with real-time biomass prediction from nutrient consumption

Scalable single-use adherent cell-based biomanufacturing platforms are essential for unlocking the full potential of cell and gene therapies. The primary objective of this study is to design and develop a novel fixed bed bioreactor platform tailored specifically for scaling up adherent cell culture. The bioreactor comprises a packed bed of vertically stacked woven polyethylene terephthalate mesh discs, sandwiched between two-fluid guide plates. Leveraging computational fluid dynamics modeling, we optimized bioreactor design to achieve uniform flow with minimal shear stress. Residence time distribution measurements demonstrated excellent flow uniformity with plug flow characteristics. Periodic media sampling coupled with offline analysis revealed minimal gradients of crucial metabolites (glucose, glutamine, lactate, and ammonia) across the bioreactor during cell growth. Furthermore, the bioreactor platform demonstrated high performance in automated cell harvesting, with ≈96% efficiency and ≈98% viability. It also exhibited linear scalability in both operational parameters and performance for cell culture and adeno-associated virus vector production. We developed mathematical models based on oxygen uptake rates to accurately predict cell growth curves and estimate biomass in real-time. This study demonstrates the effectiveness of the developed fixed-bed bioreactor platform in enabling scalable adherent cell-based biomanufacturing with high productivity and process control.

Robust and resource-efficient production process suitable for large-scale production of baculovirus through high cell density seed train and optimized infection strategy

The aim of this study was the development of a scalable production process for high titer (108 pfu/mL and above) recombinant baculovirus stocks with low cell line-derived impurities for the production of virus-like particles (VLP). To achieve this, we developed a high cell density (HCD) culture for low footprint cell proliferation, compared different infection strategies at multiplicity of infection (MOI) 0.05 and 0.005, different infection strategies and validated generally applicable harvest criteria of cell viability ≤ 80%. We also investigated online measurable parameters to observe the baculovirus production. The infection strategy employing a very low virus inoculum of MOI 0.005 and a 1:2 dilution with fresh medium one day after infection proved to be the most resource efficient. There, we achieved higher cell-specific titers and lower host cell protein concentrations at harvest than other tested infection strategies with the same MOI, while saving half of the virus stock for infecting the culture compared to other tested infection strategies. HCD culture by daily medium exchange was confirmed as suitable for seed train propagation, infection, and baculovirus production, equally efficient as the conventionally propagated seed train. Online measurable parameters for cell concentration and average cell diameter were found to be effective in monitoring the production process. The study concluded that a more efficient VLP production process in large scale can be achieved using this virus stock production strategy, which could also be extended to produce other proteins or extracellular vesicles with the baculovirus expression system.

Assessment of hybrid fixed and moving bed biofilm applications for wastewater treatment capacity increase - In situ tests in El-Gouna WWTP, Egypt

This paper provides a procedure for comparing the performance of different biofilm carrier medias and their surrounding suspended biomass through oxygen uptake rate (OUR) tests. For in situ (oxygen uptake rate (OUR) measurements, three identical lab scale biofilm reactors were set up at the El Gouna wastewater treatment plant (WWTP). In this setup, two options of media for moving-bed biofilm reactors (MBBR) and one media for fixed-bed biofilm reactors (FBBR) were compared. The WWTP also used the same carrier in a real scale hybrid application to analyze how the interactions between the carrier type and the suspended biomass influences the overall performance. The in situ OUR approach is recommended to measure the contribution of the biofilm fixed biomass under site specific conditions. Specifically, settleability and diffusion limitations are the two opposite poles that cannot be predicted adequately for mild climate conditions based on the literature. A biofilm carrier application can add but actually can also reduce the capacity in a hybrid activated sludge system: The added MBBR-media was able to grind down the sludge flocs forming a poorly settleable suspended biomass. The added FBBR-media can lead to extracellular polymeric substances (EPS) rich biofilms that contribute very little as substrate and oxygen are unavailable for the microorganisms present in the biofilm. In this application of the comparison procedure, Kaldnes K1 like MBBR media was compared with a recycling MBBR carrier option (poly propylene bottle caps) and Jäger Envirotech "BioCurlz™" FBBR media. The study showed higher average rates for the MBBR but decreased settleability. The FBBR showed higher peak rates when flushed to break up the biofilm and well settleable sludge. The determination of OUR per g of volatile solids (SOUR) showed comparable results for all the carriers and in warm conditions, only the capacity to accommodate biomass determines the contribution of the carrier.

Mimicking CHO large-scale effects in the single multicompartment bioreactor: A new approach to access scale-up behavior

During the scale-up of biopharmaceutical production processes, insufficiently predictable performance losses may occur alongside gradients and heterogeneities. To overcome such performance losses, tools are required to explain, predict, and ultimately prohibit inconsistencies between laboratory and commercial scale. In this work, we performed CHO fed-batch cultivations in the single multicompartment bioreactor (SMCB), a new scale-down reactor system that offers new access to study large-scale heterogeneities in mammalian cell cultures. At volumetric power inputs of 20.4-1.5 W m-3, large-scale characteristics like long mixing times and dissolved oxygen (DO) heterogeneities were mimicked in the SMCB. Compared to a reference bioreactor (REFB) set-up, the conditions in the SMCB provoked an increase in lactate accumulation of up to 87%, an increased glucose uptake, and reduced viable cell concentrations in the stationary phase. All are characteristic for large-scale performance. The unique possibility to distinguish between the effects of changing power inputs and observed heterogeneities provided new insights into the potential reasons for altered product quality attributes. Apparently, the degree of galactosylation in the evaluated glycan patterns changed primarily due to the different power inputs rather than the provoked heterogeneities. The SMCB system could serve as a potent tool to provide new insights into scale-up behavior and to predict cell line-specific drawbacks at an early stage of process development.

Machine learning methods for predicting the key metabolic parameters of Halomonas elongata DSM 2581 T

Ectoine is generally produced by the fermentation process of Halomonas elongata DSM 2581 ^T, which is one of the primary industrial ectoine production techniques. To effectively monitor and control the fermentation process, the important parameters require accurate real-time measurement. However, for ectoine fermentation, three critical parameters (cell optical density, glucose, and product concentration) cannot be measured conveniently in real-time due to time variation, strong coupling, and other constraints. As a result, our work effectively created a series of hybrid models to predict the values of these three parameters incorporating both fermentation kinetics and machine learning approaches. Compared with the traditional machine learning models, our models solve the problem of insufficient data which is common in fermentation. In addition, a simple kinetic modeling is only applicable to specific physical conditions, so different physical conditions require refitting the function, which is tedious to operate. However, our models also overcome this limitation. In this work, we compared different hybrid models based on 5 feature engineering methods, 11 machine-learning approaches, and 2 kinetic models. The best models for predicting three key parameters, respectively, are as follows: CORR-Ensemble (R²: 0.983 ± 0.0, RMSE: 0.086 ± 0.0, MAE: 0.07 ± 0.0), SBE-Ensemble (R²: 0.972 ± 0.0, RMSE: 0.127 ± 0.0, MAE: 0.078 ± 0.0), and SBE-Ensemble (R²:0.98 ± 0.0, RMSE: 0.023 ± 0.001, MAE: 0.018 ± 0.001). To verify the universality and stability of constructed models, we have done an experimental verification, and its results showed that our proposed models have excellent performance. KEY POINTS: • Using the kinetic models for producing simulated data • Through different feature engineering methods for dimension reduction • Creating a series of hybrid models to predict the values of three parameters in the fermentation process of Halomonas elongata DSM 2581 ^T.

Improvement of HEK293 Cell Growth by Adapting Hydrodynamic Stress and Predicting Cell Aggregate Size Distribution

HEK293 is a widely used cell line in the fields of research and industry. It is assumed that these cells are sensitive to hydrodynamic stress. The aim of this research was to use particle image velocimetry validated computational fluid dynamics (CFD) to determine the hydrodynamic stress in both shake flasks, with and without baffles, and in stirred Minifors 2 bioreactors to evaluate its effect on the growth and aggregate size distribution of HEK293 suspension cells. The HEK FreeStyle^TM 293-F cell line was cultivated in batch mode at different specific power inputs (from 63 W m-3 to 451 W m-3), whereby ≈60 W m-3 corresponds to the upper limit, which is what has been typically described in published experiments. In addition to the specific growth rate and maximum viable cell density VCDmax, the cell size distribution over time and cluster size distribution were investigated. The VCDmax of (5.77±0.02)·106cellsmL-1 was reached at a specific power input of 233 W m-3 and was 23.8% higher than the value obtained at 63 W m-3 and 7.2% higher than the value obtained at 451 W m-3. No significant change in the cell size distribution could be measured in the investigated range. It was shown that the cell cluster size distribution follows a strict geometric distribution whose free parameter p is linearly dependent on the mean Kolmogorov length scale. Based on the performed experiments, it has been shown that by using CFD-characterised bioreactors, the VCDmax can be increased and the cell aggregate rate can be precisely controlled.

Viable cell estimation of mammalian cells using off-gas-based oxygen uptake rate and aging-specific functional

This study developed an estimation routine for counting the viable cells in an in vitro fed-batch Chinese hamster ovary cultivation that relies on off-gas information and inlet gas mixture knowledge. We computed the oxygen uptake rate bound to the bioreactor exhaust gas outlet when the inlet gas mixture was stationary. Our mammalian biosynthesis analysis determined the stoichiometric parameters as a function of the average population age. We cross-validated an identical algorithm for mammalian and microbial cultivations and found that the' 99% confidence band of the model generally overlapped with the error bars defined from observations. The resulting RMSE and MAE averages were 0.188 and 0.14e⁹cells L^-1, respectively, when estimating the viable mammalian cell count. The validation for the estimation of total bacterial biomass yielded an MAE and RMSE of 1.78 g L^-1 and 2.53 g L^-1, respectively. Moreover, our proposed approach provides an online estimation of the average population age for both aerobically cultivated microorganisms.

Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths

Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.

Modelling the biological treatment process aeration efficiency: application of the artificial neural network algorithm

The biological treatment process (BTP) is responsible for removing chemical oxygen demand (COD) and ammonia using microorganisms present in wastewater. The BTP consumes large quantities of energy due to the transfer of oxygen using air pumps/blowers. Energy consumption in the BTP is due to low solubility of oxygen, which results in low aeration efficiency (AE). The aim of the study was to develop an AE model that can be used to monitor the performance of the BTP. Multilayer perceptron artificial neural network (MLP ANN) algorithm was used to model AE in the BTP. The performance of the AE model was evaluated using R², mean square error (MSE), and root mean square error (RMSE). Sensitivity analysis was performed on the AE model to determine variables that drive AE. The results of the study showed that MLP ANN algorithm was able to model AE. R², MSE, and RMSE results were 0.939, 0.0025, and 0.05, respectively, during testing phase. Sensitivity analysis results showed that temperature (34.6%), COD (21%), airflow rate (19.1%), and OTR/K_La (15.7%) drive AE. At high temperatures, the viscosity of wastewater is low which enables oxygen to penetrate the wastewater, resulting in high AE. The AE model can be used to predict the performance of the BTP, which will assist in minimizing energy consumption.

Sensitive real-time on-line estimator for oxygen transfer rates in fermenters

Recombinant Escherichia coli grown in large-scale fermenters are used extensively to produce plasmids and biopharmaceuticals. One method commonly used to control culture growth is predefined glucose feeding, often an exponential feeding profile. Predefined feeding profiles cannot adjust automatically to metabolic state changes, such as the metabolic burden associated with recombinant protein expression or high-cell density associated stresses. As the culture oxygen consumption rates indicates a culture's metabolic state, there exist several methods to estimate the oxygen uptake rate (OUR). These common OUR methods have limited application since these approaches either disrupt the oxygen supply, rely on empirical relationships, or are unable to account for latency and filtering effects. In this study, an oxygen transfer rate (OTR) estimator was developed to aid OUR prediction. This non-disruptive OTR estimator uses the dissolved oxygen and the off-gas oxygen concentration, in parallel. This new OTR estimator captures small variations in OTR due to physical and chemical manipulations of the fermenter, such as in stir speed variation, glucose feeding rate change, and recombinant protein expression. Due its sensitivity, this non-disruptive real-time OTR estimator could be integrated with feed control algorithms to maintain the metabolic state of a culture to a desired setpoint.

Impact of microplastics on the treatment performance of constructed wetlands: Based on substrate characteristics and microbial activities

Presence of microplastics (MPs) in wastewater has posed a huge ecosystem risk. Constructed wetlands (CWs) can effectively intercept MPs, while with MPs accumulation the response of CWs' performance is still unclear. In order to evaluate those effects, we conducted a 370-day experiment using CW microcosms fed with different levels (0, 10, 100, and 1000 μg/L) of polystyrene (PS) MPs (diameter: 50-100 μm). Results showed that nitrogen removal efficiency was increased (by 3.9%-24.7%) during the first 60 days and then decreased (by 7.1%-41.3%) with MPs accumulating, but no obvious change in COD and TP removal was observed. From further analysis, MPs accumulation changed the biofilm composition (TOC content increased from 41.4% to 52.7%), substrate porosity (electrical resistivity increased by 1.2-2.4 folds), and oxygen mass transfer (|K_La,O2| increased from 3.5% to 18.6%). Moreover, the microbial dynamics presented a higher abundance of nitrifiers (Nitrospira and Nitrosomonas) during the 60-day experiment and a lower abundance in the last days, while denitrifiers (Thauera, Thiobacillus, and Anaerolinea) had a high relative abundance throughout the experiment, being consistent with the variation of nitrification and denitrification rates. Finally, structural equation model analysis proved that due to the changes of substrate characteristics and microbial compositions and activities, the obvious decrease in nitrification efficiency was a direct reason for the decline of nitrogen removal during 370-day MPs accumulation. Overall, our study first prove that MPs accumulation can cause a series of changes in physicochemical and microbial characteristics of substrate, and ultimately affect the nitrogen-transforming process in CWs. Although our conclusions were based on the lab-scale CWs being different from the real wetlands, we hope that the conclusions can provide the effective regulatory strategies to guide the control of MPs in the actual wetlands.

Non-invasive and time-resolved measurement of the respiration activity of Chinese hamster ovary cells enables prediction of key culture parameters in shake flasks

BACKGROUND

Shake flasks are frequently used for mammalian cell suspension cultures. For process development and routine culture monitoring, information on culture behaviour is needed early on.

MAIN METHODS AND MAJOR RESULTS

Here, cell-specific oxygen uptake rates (qO₂ ) of two CHO cell lines were determined from shake flask experiments by simultaneous measurement of oxygen transfer rates (OTR) and viable cell concentrations (VCC). For cell line one, qO₂ decreased from 2.38∙10^-10  mmol cell^-1 h^-1 to 1.02∙10^-10  mmol cell^-1  h^-1 during batch growth. For cell line two, qO₂ was constant (1.90∙10^-10  mmol h^-1 ). Determined qO₂ values were used to calculate the VCC from OTR data. Cumulated oxygen consumption and glucose consumption were correlated for both cell lines and enabled calculation of glucose concentrations from OTR data. IgG producing cell line one had an oxygen demand of ∼15 mmol_oxygen g_glucose ^-1 , cell line two consumed ∼5 mmol_oxygen g_glucose ^-1 . The established correlations for determination of VCC and glucose were successfully transferred to subsequent cultivations for both cell lines. Combined measurement of the OTR and the carbon dioxide transfer rate enabled quantitative determination of the lactate concentration (production and consumption) without sampling.

CONCLUSIONS AND IMPLICATIONS

Taken together, non-invasive measurement of the respiration activity enabled time-resolved determination of key culture parameters for increased process understanding in shake flasks. This article is protected by copyright. All rights reserved.

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.

Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments

Reliable process development is accompanied by intense experimental effort. The utilization of an intensified design of experiments (iDoE) (intra-experimental critical process parameter (CPP) shifts combined) with hybrid modeling potentially reduces process development burden. The iDoE can provide more process response information in less overall process time, whereas hybrid modeling serves as a commodity to describe this behavior the best way. Therefore, a combination of both approaches appears beneficial for faster design screening and is especially of interest at larger scales where the costs per experiment rise significantly. Ideally, profound process knowledge is gathered at a small scale and only complemented with few validation experiments on a larger scale, saving valuable resources. In this work, the transferability of hybrid modeling for Chinese hamster ovary cell bioprocess development along process scales was investigated. A two-dimensional DoE was fully characterized in shake flask duplicates (300 ml), containing three different levels for the cultivation temperature and the glucose concentration in the feed. Based on these data, a hybrid model was developed, and its performance was assessed by estimating the viable cell concentration and product titer in 15 L bioprocesses with the same DoE settings. To challenge the modeling approach, 15 L bioprocesses also comprised iDoE runs with intra-experimental CPP shifts, impacting specific cell rates such as growth, consumption, and formation. Subsequently, the applicability of the iDoE cultivations to estimate static cultivations was also investigated. The shaker-scale hybrid model proved suitable for application to a 15 L scale (1:50), estimating the viable cell concentration and the product titer with an NRMSE of 10.92% and 17.79%, respectively. Additionally, the iDoE hybrid model performed comparably, displaying NRMSE values of 13.75% and 21.13%. The low errors when transferring the models from shaker to reactor and between the DoE and the iDoE approach highlight the suitability of hybrid modeling for mammalian cell culture bioprocess development and the potential of iDoE to accelerate process characterization and to improve process understanding.

Soft-sensors application for automated feeding control in high-throughput mammalian cell cultures

The ever-increasing demand for biopharmaceuticals has created the need for improving the overall productivity of culture processes. One such operational concept that is considered is fed-batch operations as opposed to batch operations. However, optimal fed-batch operations require complete knowledge of the cell culture to optimize the culture conditions and the nutrients feeding. For example, when using high-throughput small-scale bioreactors to test multiple clones that do not behave the same, depletion or overfeeding of some key components can occur if the feeding strategy is not individually optimized. Over the recent years, various solutions for real-time measuring of the main cell culture metabolites have been proposed. Still, the complexity in the implementation of these techniques has limited their use. Soft-sensors present an opportunity to overcome these limitations by indirectly estimate these variables in real-time. This manuscript details the development of a new soft-sensor based fed-batch strategy to maintain substrate concentration (glucose and glutamine) at optimal levels in small-scale multi parallel CHO cultures. Two alternatives to the standard feeding strategy were tested: an OUR soft-sensor-based strategy for glucose and glutamine (Strategy 1) and a dual OUR for glutamine and CO₂ /alkali addition for glucose soft-sensor strategy (Strategy 2). The results demonstrated the applicability of the OUR soft-sensor based strategy to optimize glucose and glutamine feedings, which yielded a 21% increase in final viable cell density (VCD) and a 31% in erythropoietin (EPO) titer compared with the reference one. However, CO2/alkali addition soft-sensor suffered from insufficient data to relate alkali addition with glucose consumption. As a result, the culture was overfed with glucose resulting in a 4% increase on final VCD, but a 9% decrease in final titer compared to the Reference Strategy. This article is protected by copyright. All rights reserved.

Off-Gas-Based Soft Sensor for Real-Time Monitoring of Biomass and Metabolism in Chinese Hamster Ovary Cell Continuous Processes in Single-Use Bioreactors

In mammalian cell culture, especially in pharmaceutical manufacturing and research, biomass and metabolic monitoring are mandatory for various cell culture process steps to develop and, finally, control bioprocesses. As a common measure for biomass, the viable cell density (VCD) or the viable cell volume (VCV) is widely used. This study highlights, for the first time, the advantages of using VCV instead of VCD as a biomass depiction in combination with an oxygen-uptake- rate (OUR)-based soft sensor for real-time biomass estimation and process control in single-use bioreactor (SUBs) continuous processes with Chinese hamster ovary (CHO) cell lines. We investigated a series of 14 technically similar continuous SUB processes, where the same process conditions but different expressing CHO cell lines were used, with respect to biomass growth and oxygen demand to calibrate our model. In addition, we analyzed the key metabolism of the CHO cells in SUB perfusion processes by exometabolomic approaches, highlighting the importance of cell-specific substrate and metabolite consumption and production rate qS analysis to identify distinct metabolic phases. Cell-specific rates for classical mammalian cell culture key substrates and metabolites in CHO perfusion processes showed a good correlation to qOUR, yet, unexpectedly, not for qGluc. Here, we present the soft-sensoring methodology we developed for qPyr to allow for the real-time approximation of cellular metabolism and usage for subsequent, in-depth process monitoring, characterization and optimization.

An oxygen-uptake-rate-based estimator of the specific growth rate in Escherichia coli BL21 strains cultivation processes

The cell cultivation process in a bioreactor is a high-value manufacturing process that requires excessive monitoring and control compatibility. The specific cell growth rate is a crucial parameter that describes the online quality of the cultivation process. Most methods and algorithms developed for online estimations of the specific growth rate controls in batch and fed-batch microbial cultivation processes rely on biomass growth models. In this paper, we present a soft sensor – a specific growth rate estimator that does not require a particular bioprocess model. The approach for online estimation of the specific growth rate is based on an online measurement of the oxygen uptake rate. The feasibility of the estimator developed in this study was determined in two ways. First, we used numerical simulations on a virtual platform, where the cell culture processes were theoretically modeled. Next, we performed experimental validation based on laboratory-scale (7, 12, 15 L) bioreactor experiments with three different Escherichia coli BL21 cell strains.

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.

Bioprocess Control: Current Progress and Future Perspectives

Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

Efficient production of recombinant secretory IgA against Clostridium difficile toxins in CHO-K1 cells

Despite the essential role secretory IgAs play in the defense against pathogenic invasion and the proposed value of recombinant secretory IgAs as novel therapeutics, currently there are no IgA-based therapies in clinics. Secretory IgAs are complex molecules and the major bottleneck limiting their therapeutic potential is a reliable recombinant production system. In this report, we addressed this issue and established a fast and robust production method for secretory IgAs in CHO-K1 cells using BAC-based expression vectors. As a proof of principle, we produced IgAs against Clostridium difficile toxins TcdA and TcdB. Recombinant secretory IgAs produced using our expression system showed comparable titers to IgGs, widely used as therapeutic biologicals. Importantly, secretory IgAs produced using our method were functional and could efficiently neutralize Clostridium difficile toxins TcdA and TcdB. These results show that recombinant secretory IgAs can be efficiently produced, thus opening the possibility to use them as therapeutic agents in clinics.

History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance

Biological systems are typically composed of highly interconnected subunits and possess an inherent complexity that make monitoring, control and optimization of a bioprocess a challenging task. Today a toolset of modeling techniques can provide guidance in understanding complexity and in meeting those challenges. Over the last four decades, computational performance increased exponentially. This increase in hardware capacity allowed ever more detailed and computationally intensive models approaching a "one-to-one" representation of the biological reality. Fueled by governmental guidelines like the PAT initiative of the FDA, novel soft sensors and techniques were developed in the past to ensure product quality and provide data in real time. The estimation of current process state and prediction of future process course eventually enabled dynamic process control. In this review, past, present and envisioned future of models in biotechnology are compared and discussed with regard to application in process monitoring, control and optimization. In addition, hardware requirements and availability to fit the needs of increasingly more complex models are summarized. The major techniques and diverse approaches of modeling in industrial biotechnology are compared, and current as well as future trends and perspectives are outlined.

PEI-Mediated Transient Transfection of High Five Cells at Bioreactor Scale for HIV-1 VLP Production

High Five cells are an excellent host for the production of virus-like particles (VLPs) with the baculovirus expression vector system (BEVS). However, the concurrent production of high titers of baculovirus hinder the purification of these nanoparticles due to similarities in their physicochemical properties. In this study, first a transient gene expression (TGE) method based on the transfection reagent polyethylenimine (PEI) is optimized for the production of HIV-1 VLPs at shake flask level. Furthermore, VLP production by TGE in High Five cells is successfully demonstrated at bioreactor scale, resulting in a higher maximum viable cell concentration (5.1 × 10⁶ cell/mL), the same transfection efficiency and a 1.8-fold increase in Gag-eGFP VLP production compared to shake flasks. Metabolism analysis of High Five cells indicates a reduction in the consumption of the main metabolites with respect to non-transfected cell cultures, and an increase in the uptake rate of several amino acids when asparagine is depleted. Quality assessment by nanoparticle tracking analysis and flow virometry of the VLPs produced shows an average size of 100–200 nm, in agreement with immature HIV-1 viruses reported in the literature. Overall, this work demonstrates that the High Five/TGE system is a suitable approach for the production of VLP-based vaccine candidates and other recombinant proteins.