Precultures Grown under Fed‐Batch Conditions Increase the Reliability and Reproducibility of High‐Throughput Screening Results

One essential task in bioprocess development is strain selection. A common screening procedure consists of three steps: first, the picking of colonies; second, the execution of a batch preculture and main culture, e.g., in microtiter plates (MTPs); and third, the evaluation of product formation. Especially during the picking step, unintended variations occur due to undefined amounts and varying viability of transferred cells. The aim of this study is to demonstrate that the application of polymer‐based controlled‐release fed‐batch MTPs during preculture eliminates these variations. The concept of equalizing growth through fed‐batch conditions during preculture is theoretically discussed and then tested in a model system, namely, a cellulase‐producing Escherichia coli clone bank containing 32 strains. Preculture is conducted once in the batch mode and once in the fed‐batch mode. By applying the fed‐batch mode, equalized growth is observed in the subsequent main culture. Furthermore, the standard deviation of cellulase activity is reduced compared to that observed in the conventional approach. Compared with the strains in the batch preculture process, the first‐ranked strain in the fed‐batch preculture process is the superior cellulase producer. These findings recommend the application of the fed‐batch MTPs during preculture in high‐throughput screening processes to achieve accurate and reliable results.     

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

This study was performed in order to evaluate a new LED‐based 2D‐fluorescence spectrometer for in‐line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10‐L fed‐batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D‐fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed‐batch process was used for model validation and prediction. An almost continuous in‐line bioprocess monitoring was realized because 2D‐fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D‐fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D‐fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.     

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     

Advanced online monitoring of cell culture off‐gas using proton transfer reaction mass spectrometry

Mass spectrometry has been frequently applied to monitor the O₂ and CO₂ content in the off‐gas of animal cell culture fermentations. In contrast to classical mass spectrometry the proton transfer reaction mass spectrometry (PTR‐MS) provides additional information of volatile organic compounds by application of a soft ionization technology. Hence, the spectra show less fragments and can more accurately assigned to particular compounds. In order to discriminate between compounds of non‐metabolic and metabolic origin cell free experiments and fed‐batch cultivations with a recombinant CHO cell line were conducted. As a result, in total eight volatiles showing high relevance to individual cultivation or cultivation conditions could be identified. Among the detected compounds methanethiol, with a mass‐to‐charge ratio of 49, qualifies as a key candidate in process monitoring due to its strong connectivity to lactate formation. Moreover, the versatile and complex data sets acquired by PTR MS provide a valuable resource for statistical modeling to predict non direct measurable parameters. Hence, partial least square regression was applied to the complete spectra of volatiles measured and important cell culture parameters such as viable cell density estimated (R² = 0.86). As a whole, the results of this study clearly show that PTR‐MS provides a powerful tool to improve bioprocess‐monitoring for mammalian cell culture. Thus, specific volatiles emitted by cells and measured online by the PTR‐MS and complex variables gained through statistical modeling will contribute to a deeper process understanding in the future and open promising perspectives to bioprocess control. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:496–504, 2014     

Early‐stage sustainability assessment of biotechnological processes: A case study of citric acid production

Sustainability assessment using a life‐cycle approach is indispensable to contemporary bioprocess development. This assessment is particularly important for early‐stage bioprocess development. As early‐stage investigations of bioprocesses involve the evaluation of their ecological and socioeconomic effects, they can be adjusted more effectively and improved towards sustainability, thereby reducing environmental risk and production costs. Early‐stage sustainability assessment is an important precautionary practice and, despite limited data, a unique opportunity to determine the primary impacts of bioprocess development. To this end, a simple and robust method was applied based on the standardized life‐cycle sustainability assessment methodology and commercially available datasets. In our study, we elaborated on the yeast‐based citric acid production process with Yarrowia lipolytica assessing 11 different substrates in different process modes. The focus of our analysis comprised both cultivation and down‐stream processing. According to our results, the repeated batch raw glycerol based bioprocess alternative showed the best environmental performance. The second‐ and third‐best options were also glycerol‐based. The least sustainable processes were those using molasses, chemically produced ethanol, and soy bean oil. The aggregated results of environmental, economic, and social impacts display waste frying oil as the best‐ranked alternative. The bioprocess with sunflower oil in the batch mode ranked second. The least favorable alternatives were the chemically produced ethanol‐, soy oil‐, refined glycerol‐, and molasses‐based citric acid production processes. The scenario analysis demonstrated that the environmental impact of nutrients and wastewater treatment is negligible, but energy demand of cultivation and down‐stream processing dominated the production process. However, without energy demand the omission of neutralizers almost halves the total impact, and neglecting pasteurization also considerably decreases the environmental impact.     

Data reconciliation of concentration estimates from mid‐infrared and dielectric spectral measurements for improved on‐line monitoring of bioprocesses

Real‐time data reconciliation of concentration estimates of process analytes and biomass in microbial fermentations is investigated. A Fourier‐transform mid‐infrared spectrometer predicting the concentrations of process metabolites is used in parallel with a dielectric spectrometer predicting the biomass concentration during a batch fermentation of the yeast Saccharomyces cerevisiae. Calibration models developed off‐line for both spectrometers suffer from poor predictive capability due to instrumental and process drifts unseen during calibration. To address this problem, the predicted metabolite and biomass concentrations, along with off‐gas analysis and base addition measurements, are reconciled in real‐time based on the closure of mass and elemental balances. A statistical test is used to confirm the integrity of the balances, and a non‐negativity constraint is used to guide the data reconciliation algorithm toward positive concentrations. It is verified experimentally that the proposed approach reduces the standard error of prediction without the need for additional off‐line analysis. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Optimization of norovirus virus‐like particle production in Pichia pastoris using a real‐time near‐infrared bioprocess monitor

The production of norovirus virus‐like particles (NoV VLPs) displaying NY‐ESO‐1 cancer testis antigen in Pichia pastoris BG11 Mut⁺ has been enhanced through feed‐strategy optimization using a near‐infrared bioprocess monitor (RTBio^® Bioprocess Monitor, ASL Analytical, Inc.), capable of monitoring and controlling the concentrations of glycerol and methanol in real‐time. The production of NoV VLPs displaying NY‐ESO‐1 in P. pastoris has potential as a novel cancer vaccine platform. Optimization of the growth conditions resulted in an almost two‐fold increase in the expression levels in the fermentation supernatant of P. pastoris as compared to the starting conditions. We investigated the effect of methanol concentration, batch phase time, and batch to induction transition on NoV VLP‐NY‐ESO‐1 production. The optimized process included a glycerol transition phase during the first 2 h of induction and a methanol concentration set point of 4 g L^?1 during induction. Utilizing the bioprocess monitor to control the glycerol and methanol concentrations during induction resulted in a maximum NoV VP1‐NY‐ESO‐1 yield of 0.85 g L^?1. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:518–526, 2016     

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     

Process model comparison and transferability across bioreactor scales and modes of operation for a mammalian cell bioprocess

A Monod kinetic model, logistic equation model, and statistical regression model were developed for a Chinese hamster ovary cell bioprocess operated under three different modes of operation (batch, bolus fed‐batch, and continuous fed‐batch) and grown on two different bioreactor scales (3 L bench‐top and 15 L pilot‐scale). The Monod kinetic model was developed for all modes of operation under study and predicted cell density, glucose glutamine, lactate, and ammonia concentrations well for the bioprocess. However, it was computationally demanding due to the large number of parameters necessary to produce a good model fit. The transferability of the Monod kinetic model structure and parameter set across bioreactor scales and modes of operation was investigated and a parameter sensitivity analysis performed. The experimentally determined parameters had the greatest influence on model performance. They changed with scale and mode of operation, but were easily calculated. The remaining parameters, which were fitted using a differential evolutionary algorithm, were not as crucial. Logistic equation and statistical regression models were investigated as alternatives to the Monod kinetic model. They were less computationally intensive to develop due to the absence of a large parameter set. However, modeling of the nutrient and metabolite concentrations proved to be troublesome due to the logistic equation model structure and the inability of both models to incorporate a feed. The complexity, computational load, and effort required for model development has to be balanced with the necessary level of model sophistication when choosing which model type to develop for a particular application. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2013     

Biosensor online control of citric acid production from glucose by Yarrowia lipolytica using semicontinuous fermentation

Our study aimed at the development of an effective method for citric acid production from glucose by use of the yeast Yarrowia lipolytica. The new method included an automated bioprocess control using a glucose biosensor. Several fermentation methodologies including batch, fed‐batch, repeated batch and repeated fed‐batch cultivation were tested. The best results were achieved during repeated fed‐batch cultivation: Within 3 days of cycle duration, approximately 100 g/L citric acid were produced. The yields reached values between 0.51 and 0.65 g/g and the selectivity of the bioprocess for citric acid was as high as 94%. Due to the elongation of the production phase of the bioprocess with growth‐decoupled citric acid production, and by operating the fermentation in cycles, an increase in citric acid production of 32% was achieved compared with simple batch fermentation.     

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization

Upstream bioprocess characterization and optimization are time and resource‐intensive tasks. Regularly in the biopharmaceutical industry, statistical design of experiments (DoE) in combination with response surface models (RSMs) are used, neglecting the process trajectories and dynamics. Generating process understanding with time‐resolved, dynamic process models allows to understand the impact of temporal deviations, production dynamics, and provides a better understanding of the process variations that stem from the biological subsystem. The authors propose to use DoE studies in combination with hybrid modeling for process characterization. This approach is showcased on Escherichia coli fed‐batch cultivations at the 20L scale, evaluating the impact of three critical process parameters. The performance of a hybrid model is compared to a pure data‐driven model and the widely adopted RSM of the process endpoints. Further, the performance of the time‐resolved models to simultaneously predict biomass and titer is evaluated. The superior behavior of the hybrid model compared to the pure black‐box approaches for process characterization is presented. The evaluation considers important criteria, such as the prediction accuracy of the biomass and titer endpoints as well as the time‐resolved trajectories. This showcases the high potential of hybrid models for soft‐sensing and model predictive control.     

Model-based analysis on the relationship of signal quality to real-time extraction of information in bioprocesses

Quality by design (QbD) is a current structured approach to design processes yielding a quality product. Knowledge and process understanding cannot be achieved without proper experimental data; hence requirements for measurement error and frequency of measurement of bioprocess variables have to be defined. In this contribution, a model-based approach is used to investigate impact factors on calculated rates to predict the obtainable information from real-time measurements (= signal quality). Measurement error, biological activity, and averaging window (= period of observation) were identified as biggest impact factors on signal quality. Moreover, signal quality has been set in context with a quantifiable measure using statistical error testing, which can be used as a benchmark for process analytics and exploitation of data. Results have been validated with data from an E. coli batch process. This approach is useful to get an idea which process dynamics can be observed with a given bioprocess setup and sampling strategy beforehand.     

Generic Raman‐based calibration models enabling real‐time monitoring of cell culture bioreactors

Raman‐based multivariate calibration models have been developed for real‐time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO‐based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1004–1013, 2015     

Fed‐batch CHO cell t‐PA production and feed glutamine replacement to reduce ammonia production

Industrial therapeutic protein production has been greatly improved through fed‐batch development. In this study, improvement to the productivity of a tissue‐plasminogen activator (t‐PA) expressing Chinese hamster ovary (CHO) cell line was investigated in shake flask culture through the optimization of the fed‐batch feed and the reduction of ammonia generation by glutamine replacement. The t‐PA titer was increased from 33 mg/L under batch conditions to 250 mg/L with daily feeding starting after three days of culture. A commercially available fed‐batch feed was supplemented with cotton seed hydrolysate and the four depleted amino acids, aspartic acid, asparagine, cysteine, and tyrosine. The fed‐batch operation increased the generation of by‐products such as lactate and ammonia that can adversely affect the fed‐batch performance. To reduce the ammonia production, a glutamine‐containing dipeptide, pyruvate, glutamate, and wheat gluten hydrolysate, were investigated as glutamine substitutes. To minimize the lag phase as the cells adjusted to the new energy source, a feed glutamine replacement process was developed where the cells were initially cultured with a glutamine containing basal medium to establish cell growth followed by feeding with a feed containing the glutamine substitutes. This two‐step feed glutamine replacement process not only reduced the ammonia levels by over 45% but, in the case of using wheat gluten hydrolysate, almost doubled the t‐PA titer to over 420 mg/L without compromising the t‐PA product quality or glycosylation pattern. The feed glutamine replacement process combined with optimizing other feed medium components provided a simple, practical, and effective fed‐batch strategy that could be applied to the production of other recombinant therapeutic proteins. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2013     

Bringing Stem Cell‐Based Therapies for Type 1 Diabetes to the Clinic: Early Insights from Bioprocess Economics and Cost‐Effectiveness Analysis

Differentiation of pluripotent stem cells (PSCs) into β cells could provide insulin independence for type 1 diabetes (T1D) patients. This approach would reduce the clinical complications that most patients managed on intensive insulin therapy (IIT) face. However, bottlenecks of PSC manufacturing and limited engraftment of encapsulated cells hinder the long‐term effectiveness of these therapies. A bioprocess decision‐support tool is combined with a disease state‐transition model to evaluate the cost‐effectiveness of the stem cell‐based therapy against IIT. Clinical effectiveness is assessed in quality‐adjusted life years (QALYs). Manufacturing costs per patient reduce from $430 000 to $160 000 with optimization of batch size and annual demand. For 96% of the patients, cell therapy improves the quality of life compared to IIT. Cost savings are achieved for 2% of the population through prevention of renal disease. The therapy is cost‐effective for 3.4% of patients when a willingness to pay (WTP) of up to $150 000 per QALY is considered. A 75% cost reduction in the cell therapy price increases cost‐effectiveness likelihood to 51% at $100 000 per QALY. This study highlights the need for scalable manufacturing platforms for stem cell therapies, as well as to prioritizing access to the therapy to patients with an increased likelihood of costly complications.     

Temporally segmented modelling: a route to improved bioprocess monitoring using near infrared spectroscopy?

Near infrared spectroscopy (NIRS) was used to monitor an industrial bioprocess for the production of the antibiotic, tylosin, using a segmented modelling approach. Models were built over the entire time course of the fermentation from 0 to 150 h, and also in two distinct phases or segments of the bioprocess from 50 to 100 h (synthetic phase) and from 100 to 150 h (stationary phase). All models were validated externally and the performance of the full range and segmented models compared. The standard error of prediction (SEP) of the segmented models was less in both 50–100 h and 100–150 h and the correlation highest in the 50–100 h range. This would suggest that data segmentation is potentially a useful method of accommodating the impact of the pronounced matrix changes which occur in some bioprocesses in NIRS models for key analytes. While there are many reports on bioprocess monitoring using NIRS, there have been no previous studies on the use of segmented NIR models within a bioprocess as a means of accommodating matrix change.     

Fed‐batch synthesis of galacto‐oligosaccharides with Aspergillus oryzae β‐galactosidase using optimal control strategy

Fed‐batch synthesis of galacto‐oligosaccharides (GOS) from lactose with β‐galactosidase from Aspergillus oryzae was evaluated experimentally and reaction yield was maximized via optimal control technique. The optimal lactose and enzyme feed flow rate profiles were determined using a model for GOS synthesis previously reported by the authors. Experimentally it was found that fed‐batch synthesis allowed an increase on the maximum total GOS concentration from 115 (batch synthesis) to 218 g L^?1 as consequence of the increase in total sugars concentration from 40 to 58% w/w. Such high concentration of total sugars was not attainable in batch operation because of the low solubility of lactose at the reaction temperature (40°C). Simulations predicted a GOS yield of 32.5 g g^?1 in fed‐batch synthesis under optimal conditions, while experimentally the same yield as in batch synthesis was obtained (28 g g^?1). Besides, an enrichment of total oligosaccharides in GOS with a high polymerization degree (GOS‐5 and GOS‐6) was observed in the fed‐batch synthesis. Experimental profiles for all sugars were similar to the ones predicted by simulation, which supports the use of this methodology for the optimization of GOS synthesis. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:59–67, 2014     

A multivariate calibration procedure for UV/VIS spectrometric monitoring of BHK‐21 cell metabolism and growth

Monitoring mammalian cell culture with UV–vis spectroscopy has not been widely explored. The aim of this work was to calibrate Partial Least Squares (PLS) models from off‐line UV–vis spectral data in order to predict some nutrients and metabolites, as well as viable cell concentrations for mammalian cell bioprocess using phenol red in culture medium. The BHK‐21 cell line was used as a mammalian cell model. Spectra of samples taken from batches performed at different dissolved oxygen concentrations (10, 30, 50, and 70% air saturation), in two bioreactor configurations and with two strategies to control pH were used to calibrate and validate PLS models. Glutamine, glutamate, glucose, and lactate concentrations were suitably predicted by means of this strategy. Especially for glutamine and glucose concentrations, the prediction error averages were lower than 0.50 ± 0.10 mM and 2.21 ± 0.16 mM, respectively. These values are comparable with those previously reported using near infrared and Raman spectroscopy in conjunction with PLS. However, viable cell concentration models need to be improved. The present work allows for UV–vis at‐line sensor development, decrease cost related to nutrients and metabolite quantifications and establishment of fed‐batch feeding schemes. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:241–248, 2014     

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.