Continuous optical in-line glucose monitoring and control in CHO cultures contributes to enhanced metabolic efficiency while maintaining darbepoetin alfa product quality

Great efforts are directed towards improving productivity, consistency and quality of biopharmaceutical processes and products. One particular area is the development of new sensors for continuous monitoring of critical bioprocess parameters by using online or in-line monitoring systems. Recently, we developed a glucose biosensor applicable in single-use, in-line and long-term glucose monitoring in mammalian cell bioreactors. Now, we integrated this sensor in an automated glucose monitoring and feeding system capable of maintaining stable glucose levels, even at very low concentrations. We compared this fed-batch feedback system at both low (< 1 mM) and high (40 mM) glucose levels with traditional batch culture methods, focusing on glycosylation and glycation of the recombinant protein darbepoetin alfa (DPO) produced by a CHO cell line. We evaluated cell growth, metabolite and product concentration under different glucose feeding strategies and show that continuous feeding, even at low glucose levels, has no harmful effects on DPO quantity and quality. We conclude that our system is capable of tight glucose level control throughout extended bioprocesses and has the potential to improve performance where constant maintenance of glucose levels is critical.     

Continuous pH monitoring in a perfused bioreactor system using an optical pH sensor

Monitoring and regulating the pH of the solution in a bioprocess is one of the key steps in the success of bioreactor operation. An in-line optical pH sensor, based on the optical absorption properties of phenol red present in the medium, was developed and tested in this work for use in NASA space bioreactors based on a rotating wall-perfused vessel system supporting a baby hamster kidney (BHK-21) cell culture. The sensor was tested over three 30-day and one 124-day cell runs. The pH sensor initially was calibrated and then used during the entire cell culture interval. The pH reported by the sensor was compared to that measured by a fiber optically coupled Shimadzu spectrophotometer and a blood gas analyzer. The maximum standard error of prediction for all the four cell runs for development pH sensor against BGA was +/-0.06 pH unit and for the fiber optically coupled Shimadzu spectrophotometer against the blood gas analyzer was +/-0.05 pH unit. The pH sensor system performed well without need of recalibration for 124 days.     

An automated homogeneous method for quantifying polysorbate using fluorescence polarization

An automated fluorescence polarization (FP) assay has been developed for the quantitation of polysorbate in bioprocess samples. Using the lipophilic probe 5-dodecanoylaminofluorescein (DAF), polysorbate concentrations above the critical micelle concentration can be quantified by the FP increase that results when DAF inserts into the detergent micelles. The specificity, accuracy, and precision of this assay were defined for samples obtained from vaccine purification processes. Spike recoveries were 98-106% for purified products and 110-120% for crude process intermediates. The coefficients of variation for intra- and interassay precision were less than 9 and 14%, respectively. Because of the operational simplicity of the assay, all of the assay steps from sample preparation to data reduction were automated on a Tecan liquid-handling workstation. The combination of a rapid assay and an automated format makes this method well suited to the routine analysis of samples from trial purification processes which are carried out during the development of a vaccine or therapeutic protein. This method should be adaptable for the quantitation of other detergents into which DAF will insert.     

Observer-based online compensation of inner filter effect in monitoring fluorescence of GFP-expressing plant cell cultures

The green fluorescent protein (GFP) isolated from the jellyfish Aequorea victoria is a very useful reporter for real-time bioprocess sensing. GFP culture fluorescence is a composite signal that can be influenced by factors such as culture autofluorescence, inner filter effect (IFE), and photobleaching. These factors complicate accurate estimation of GFP concentrations from the culture fluorescence. IFE is especially problematic when using GFP in monitoring transgenic plant cell suspension cultures, due to the aggregated nature of the cells and the high biomass concentration in these culture systems. Reported approaches for online compensation of IFE in monitoring culture NADH fluorescence or bioluminescence require online measurement of biomass density or culture turbidity/optical density, in addition to fluorescence/bioluminescence measurement. In this study, culture GFP fluorescence was used successfully to estimate GFP concentration and other important states in bioreactor culture of transgenic tobacco cells, while the influences of IFE and culture autofluorescence were rectified without the need for an additional biomass sensor. This was achieved by setting up a novel model-based state observer. First, we developed an improved model for a backscatter fluorescence probe that takes into account the influence of IFE and autofluorescence on reporting culture GFP concentration from online fluorescence. The state observer was then established using the extended Kalman filter (EKF), based on the fluorescence probe model, a dynamic state model of the plant cell bioreactor, and online GFP fluorescence measurement. Several versions of the observer were introduced to address practical requirements associated with monitoring GFP fluorescence of plant cell cultures. The proposed approach offers an effective means for online compensation of IFE to enable quantitative interpretation of the culture fluorescence signals for accurate reporting of GFP or GFP-fusion protein expression.     

On-line biomass estimation in biosurfactant production process by <Emphasis Type="Italic">Candida lipolytica</Emphasis> UCP 988

Biomass is an important variable in biosurfactant production process. However, such bioprocess variable, usually, is collected by sampling and determined by off-line analysis, with significant time delay. Therefore, simple and reliable on-line biomass estimation procedures are highly desirable. An artificial neural network model (ANN) is presented for the on-line estimation of biomass concentration, in biosurfactant production by Candida lipolytica UCP 988, as a nonlinear function of pH and dissolved oxygen. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consists of one hidden layer with four neurons. The performance of the ANN was checked using experimental data. The results obtained indicate a very good predictive capacity for the ANN-based software sensor with values of R2 of 0.969 and RMSE of 0.021 for biomass concentration. Estimated biomass using the ANN was proved to be a simple, robust and accurate method.     

Affinity Chromatography: An Enabling Technology for Large‐Scale Bioprocessing

Affinity chromatography (AC) has been used in large‐scale bioprocessing for almost 40 years and is considered the preferred method for primary capture in downstream processing of various types of biopharmaceuticals. The objective of this mini‐review is to provide an overview of a) the history of bioprocess AC, b) the current state of platform processes based on affinity capture steps, c) the maturing field of custom developed bioprocess affinity resins, d) the advantages of affinity capture‐based downstream processing in comparison to other forms of chromatography, and e) the future direction for bioprocess scale AC. The use of AC can result in economic advantages by enabling the standardization of process development and the manufacturing processes and the use of continuous operations in flexible multiproduct production suites. These concepts are discussed from a growing field of custom affinity bioprocess resin perspective. The custom affinity resins not only address the need for a capture resin for non‐platformable processes, but also can be employed in polishing applications, where they are used to define and control drug substance composition by separating specific product variants from the desired product form.     

Biomass soft sensor for a Pichia pastoris fed-batch process based on phase detection and hybrid modeling

A common control strategy for the production of recombinant proteins in Pichia pastoris using the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO₂ in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% and R² of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of the P. pastoris bioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.     

Combinatorial impact of Sec signal peptides from Bacillus subtilis and bioprocess conditions on heterologous cutinase secretion by Corynebacterium glutamicum

The impact of Sec signal peptides (SPs) from Bacillus subtilis in combination with isopropyl-β- d -1-thiogalactopyranoside concentration and feeding profile was investigated for heterologous protein secretion performance by Corynebacterium glutamicum using cutinase as model enzyme. Based on a comprehensive data set of about 150 bench-scale bioreactor cultivations in fed-batch mode and choosing the cutinase yield as objective, it was shown that relative secretion performance for bioprocesses remains very similar, irrespective of the applied SP enabling Sec-mediated cutinase secretion. However, to achieve the maximal absolute cutinase yield, careful adjustment of bioprocess conditions was found to be necessary. A model-based, two-step multiple regression approach resembled the collected data in a comprehensive way. The corresponding results suggest that the choice of the heterologous Sec SP and its interaction with the adjusted exponential feeding profile is highly relevant to maximize absolute cutinase yield in this study. For example, the impact of Sec SP is high at low growth rates and low at high growth rates. However, promising Sec SPs could be inferred from less complex batch cultivations. The extensive data were also evaluated in terms of cutinase productivity, highlighting the well-known trade-off between yield and productivity in bioprocess development in detail. Conclusively, only the right combination of target protein, Sec SP, and bioprocess conditions is the key to success.     

On the controllability of continuous fermentation processes

Simulation may be used as a powerful tool for accelerating bioprocess design. This paper demonstrates the use of simulations in exploring the nature and impact of the interactions that exist in a typical bioprocess for the recovery of an intracellular protein. The study shows that an integrated approach to design must be adopted in order to achieve acceptable process designs. Data from a fed-batch fermentation, with verified models for cell harvesting, cell disruption and cell debris removal have been integrated to demonstrate the consequence of process design and operating decisions on the resulting process performance. The trade-offs between product recovery and the extent of cell debris removal for a range of operating conditions have been represented through a series of windows of operation which show how process conditions must be altered in order for given process performance levels to be realised. The capacity to account for process performance including the impact of interactions is seen as a pre-requisite for rigorous bioprocess sequence design and optimisation.     

Horseradish Peroxidase Combined With Oxidase Enzymes a Valuable Bioanalytical Tool: Lactate Oxidase – A Case Study

Enzymatic biosensors have been extensively investigated for real‐time bioprocess monitoring and other online analysis. However, implementation of biosensors has been strongly hindered by their limited stability. This work reports a significant improvement of the stability of the immobilized oxidases by in situ reduction of the harmful H₂O₂. Thus, stabilized oxidases can serve as the basis for ethanol, glucose, and lactate sensors, with the ability to operate for long periods of time with virtually no change in activity. As an example, a lactate sensor, containing lactate oxidase aimed for bioprocess monitoring, has been described and characterized. Operational stabilities that allow up to 8 h continuous lactate conversion with virtually no activity loss have been achieved. The described system based on the in situ stabilization strategy is a promising new tool for the development of online analyses.     

Physiological description of multivariate interdependencies between process parameters,morphology and physiology during fed‐batch penicillin production

Optimization of productivity and economics of industrial bioprocesses requires characterization of interdependencies between process parameters and process performance. In the case of penicillin production, as in other processes, process performance is often closely interlinked with the physiology and morphology of the organism used for production. This study presents a systematic approach to efficiently characterize the physiological effects of multivariate interdependencies between bioprocess design parameters (spore inoculum concentration, pO₂ control level and substrate feed rate), morphology, and physiology. Method development and application was performed using the industrial model process of penicillin production. Applying traditional, statistical bioprocess analysis, multivariate correlations of raw bioprocess design parameters (high spore inoculum concentration, low pO₂ control as well as reduced glucose feeding) and pellet morphology were identified. A major drawback of raw design parameter correlation models; however, is the lack of transferability across different process scales and regimes. In this context, morphological and physiological bioprocess modeling based on scalable physiological parameters is introduced. In this study, raw parameter effects on pellet morphology were efficiently summarized by the physiological parameter of the biomass yield per substrate. Finally, for the first time to our knowledge, the specific growth rate per spore was described as time‐independent determinant for switching from pellet to disperse growth during penicillin production and thus introduced as a novel, scalable key process parameter for pellet morphology and process performance. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:689–699, 2014     

On-chip microdialysis system with flow-through sensing components

Microdialysis probes have been used for diabetes treatment as continuous monitoring system coupled to a glucose sensor. An on-chip microdialysis system with in-line sensing electrodes is demonstrated. As a first step towards greater biosensor integration with this miniaturized microdialysis system, a stacked system with in-line sensing electrodes was developed. Impedance electrodes sputtered within the microchannels were used to determine fluid electrical resistance from a dialyzed phosphate buffered saline (PBS) solution, which characterizes solution conductivity as a function of PBS concentration. The permeability of the membrane to the salt ions was obtained as 0.246+/-0.028 microm/s (15 nm pores). Subsequently, experiments measuring PBS dialysis in the time-domain at 64.4% recovery were conducted. The PBS concentration of the reservoir was changed in both a step response and sinusoidally with an 800 s period. The subsequently measured impedance indicates that the system is able to continuously track concentration changes in the reservoir with a 210 s system response delay. Most of this delay is due to the dead volume within the tubing between the syringe pumps and the microsystem. In addition, the predicted response was modeled using linear systems theory and matches the experimental measurements (r=0.98). This system is expected to have the proper sensitivity to track physiologically relevant concentration changes of biomolecules such as glucose (which has a physiological maximum change rate of approximately 4 mg/dl min with a periodicity of 1h or greater) with minimal lag time and amplitude reduction.     

Improvement of bioprocess monitoring: development of novel concepts

The advancement of bioprocess monitoring will play a crucial role to meet the future requirements of bioprocess technology. Major issues are the acceleration of process development to reduce the time to the market and to ensure optimal exploitation of the cell factory and further to cope with the requirements of the Process Analytical Technology initiative. Due to the enormous complexity of cellular systems and lack of appropriate sensor systems microbial production processes are still poorly understood. This holds generally true for the most microbial production processes, in particular for the recombinant protein production due to strong interaction between recombinant gene expression and host cell metabolism. Therefore, it is necessary to scrutinise the role of the different cellular compartments in the biosynthesis process in order to develop comprehensive process monitoring concepts by involving the most significant process variables and their interconnections. Although research for the development of novel sensor systems is progressing their applicability in bioprocessing is very limited with respect to on-line and in-situ measurement due to specific requirements of aseptic conditions, high number of analytes, drift, and often rather low physiological relevance. A comprehensive survey of the state of the art of bioprocess monitoring reveals that only a limited number of metabolic variables show a close correlation to the currently explored chemical/physical principles. In order to circumvent this unsatisfying situation mathematical methods are applied to uncover "hidden" information contained in the on-line data and thereby creating correlations to the multitude of highly specific biochemical off-line data. Modelling enables the continuous prediction of otherwise discrete off-line data whereby critical process states can be more easily detected. The challenging issue of this concept is to establish significant on-line and off-line data sets. In this context, online sensor systems are reviewed with respect to commercial availability in combination with the suitability of offline analytical measurement methods. In a case study, the aptitude of the concept to exploit easily available online data for prediction of complex process variables in a recombinant E. coli fed-batch cultivation aiming at the improvement of monitoring capabilities is demonstrated. In addition, the perspectives for model-based process supervision and process control are outlined.     

Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data

In this investigation, the fermentation step of a standard mammalian cell-based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off-line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non-error rate of prediction of 100%.     

ATR-FTIR sensor development for continuous on-line monitoring of chlorinated aliphatic hydrocarbons in a fixed-bed bioreactor

This article describes the continuous on-line monitoring of a dechlorination process by a novel attenuated total reflection-Fourier transform infrared (ATR-FTIR) sensor. This optical sensor was developed to measure noninvasively part-per-million (ppm) concentrations of trichloroethylene (TCE), tetrachloroethylene (PCE), and carbon tetrachloride (CT) in the aqueous effluent of a fixed-bed dechlorinating bioreactor, without any prior sample preparation. The sensor was based on an ATR internal reflection element (IRE) coated with an extracting hydrophobic polymer, which prevented water molecules from interacting with the infrared (IR) radiation. The selective diffusion of chlorinated compound molecules from aqueous solution into the polymer made possible their detection by the IR beam. With the exclusion of water the detection limits were lowered, and measurements in the low ppm level became possible. The best extracting polymer was polyisobutylene (PIB) in the form of a 5.8-microm thick film, which afforded a detection limit of 2, 3, and 2. 5 mg/L (ppm) for TCE, PCE, and CT, respectively. Values of the enrichment factors between the polymer coating and the water matrix of these chloro-organics were determined experimentally and were compared individually with predictions obtained from the slopes of absorbance/concentration curves for the three analytes. Before coupling the ATR-FTIR sensor to the dechlorinating bioreactor, preliminary spectra of the chlorinated compounds were acquired on a laboratory scale configuration in stop-flow and flow-through closed-loop modes. In this way, it was possible to study the behavior and direct response of the optical sensor to any arbitrary concentration change of the analytes. Subsequently, the bioreactor was monitored with the infrared sensor coupled permanently to it. The sensor tracked the progression of the analytes' spectra over time without perturbing the dechlorinating process. To calibrate the ATR-FTIR sensor, a total of 13 standard mixtures of TCE, PCE and CT at concentrations ranging from 0 to 60 ppm were selected according to a closed symmetrical experimental design derived from a 3(2) full-factorial design. The above range of concentrations chosen for calibration reflected typical values during normal bioreactor operation. Several partial least squares (PLS) calibration models were generated to resolve overlapping absorption bands. The standard error of prediction (SEP) ranged between 0.6 and 1 ppm, with a relative standard error of prediction (RSEP) between 3 and 6% for the three analytes. The accuracy of this ATR-FTIR sensor was checked against gas chromatography (GC) measurements of the chlorocompounds in the bioreactor effluents. The results demonstrate the efficiency of this new sensor for routine continuous on-line monitoring of the dechlorinating bioreactor. This strategy is promising for bioprocess control and optimization.     

Parallel steady state studies on a milliliter scale accelerate fed‐batch bioprocess design for recombinant protein production with Escherichia coli

In general, fed‐batch processes are applied for recombinant protein production with Escherichia coli (E. coli). However, state of the art methods for identifying suitable reaction conditions suffer from severe drawbacks, i.e. direct transfer of process information from parallel batch studies is often defective and sequential fed‐batch studies are time‐consuming and cost‐intensive. In this study, continuously operated stirred‐tank reactors on a milliliter scale were applied to identify suitable reaction conditions for fed‐batch processes. Isopropyl β‐d ‐1‐thiogalactopyranoside (IPTG) induction strategies were varied in parallel‐operated stirred‐tank bioreactors to study the effects on the continuous production of the recombinant protein photoactivatable mCherry (PAmCherry) with E. coli. Best‐performing induction strategies were transferred from the continuous processes on a milliliter scale to liter scale fed‐batch processes. Inducing recombinant protein expression by dynamically increasing the IPTG concentration to 100 µM led to an increase in the product concentration of 21% (8.4 g L^?1) compared to an implemented high‐performance production process with the most frequently applied induction strategy by a single addition of 1000 µM IPGT. Thus, identifying feasible reaction conditions for fed‐batch processes in parallel continuous studies on a milliliter scale was shown to be a powerful, novel method to accelerate bioprocess design in a cost‐reducing manner. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1426–1435, 2016     

Hydrolysis of liquefied corn starch in a membrane reactor

The objective of this study was to develop a continuous hydrolysis process for the enzymatic saccharification of liquefied corn starch using a membrane reactor. A residence time distribution study confirmed that the membrane reactor could be modeled as a simple continuous stirred tank reactor (CSTR). Kinetic studies indicated that the continuous reactor operated in the first-order region with respect to substrate concentration at substrate concentrations greater than 200 g/L. At a residence time of 1 h and an enzyme concentration of 1 g/L, the maximum reaction velocity (V(m)) was 3.86 g glucose/L min and the apparent Michaelis constant (K(m) (')) was 562 g/L. The K(m) (') value for the continuous reactor was 2-7 times greater than that obtained in a batch reactor.Kinetic data were fit to a model based on the Michaelis-Menten rate expression and the design equation for a CSTR. Application of the model at low reactor space times was successful. At space times of 6 min or less, the model predicted the reactor's performance reasonably well. Additional work on the detection and quantitation of reversion products formed by glucoamylase is required. Isolation, detection, and quantitation of reversion products by HPLC was difficult. Detailed analysis on the formation of these reversion products could lead to better reactor designs in the future.     

Downscaling screening cultures in a multifunctional bioreactor array-on-a-chip for speeding up optimization of yeast-based lactic acid bioproduction

A key challenge for bioprocess engineering is the identification of the optimum process conditions for the production of biochemical and biopharmaceutical compounds using prokaryotic as well as eukaryotic cell factories. Shake flasks and bench-scale bioreactor systems are still the golden standard in the early stage of bioprocess development, though they are known to be expensive, time-consuming, and labor-intensive as well as lacking the throughput for efficient production optimizations. To bridge the technological gap between bioprocess optimization and upscaling, we have developed a microfluidic bioreactor array to reduce time and costs, and to increase throughput compared with traditional lab-scale culture strategies. We present a multifunctional microfluidic device containing 12 individual bioreactors (V_t = 15 µl) in a 26 mm × 76 mm area with in-line biosensing of dissolved oxygen and biomass concentration. Following initial device characterization, the bioreactor lab-on-a-chip was used in a proof-of-principle study to identify the most productive cell line for lactic acid production out of two engineered yeast strains, evaluating whether it could reduce the time needed for collecting meaningful data compared with shake flasks cultures. Results of the study showed significant difference in the strains' productivity within 3 hr of operation exhibiting a 4- to 6-fold higher lactic acid production, thus pointing at the potential of microfluidic technology as effective screening tool for fast and parallelizable industrial bioprocess development.     

Multi-day pre-clinical demonstration of glucose/galactose binding protein-based fiber optic sensor

We report here the first pre-clinical demonstration of continuous glucose tracking by fluorophore-labeled and genetically engineered glucose/galactose binding protein (GGBP). Acrylodan-labeled GGBP was immobilized in a hydrogel matrix at the tip of a small diameter optical fiber contained in a stainless steel needle. The fiber optic biosensors were inserted subcutaneously into Yucatan and Yorkshire swine, and the sensor response to changing glucose levels was monitored at intervals over a 7-day period. Sensor mean percent error on day 7 was 16.4±5.0% using a single daily reference blood glucose value to calibrate the sensor. The GGBP sensor's susceptibility to common interferents was tested in a well-plate system using human sera. No significant interference was observed from the tested interferents except for tetracycline at the drug's maximum plasma concentration. The robust performance of the GGBP-based fiber optic sensor in swine models and resistance to interferents indicates the potential of this technology for continuous glucose monitoring in humans.