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Advances in Fermentation & Cell Culture: The Keys to Upstream Productivity

BioPharm International Digital Edition, June 2010 Supplement (PDF)

A strategy for improved process operational procedures was developed that enabled us to supervise and automatically control a fermentation feeding process in 

. Exponential feeding was used so that the cells could be grown at the desired specific growth rate (µ) by programming the bioreactor software. By providing proper nutrient and operating conditions, this strategy allowed us to reach a high cell concentration in less time, thus minimizing operational costs during scale-up and maximizing volumetric productivity and reproducibility.  

The US Food and Drug Administration has been putting pressure on manufacturers to exert greater control over product quality by increasing their understanding of the data gathered from manufacturing processes. In particular, the process analytical technology (PAT) initiative calls on manufacturers to achieve those goals by:  

incorporating new techniques for online monitoring

applying online measurements to detect deviations from "in control situations" before the process fails

using automatic feedback controls to correct processes if significant deviations from the set-point profiles appear.

Indeed, a key component for the commercial success of a biopharmaceutical product is the ability to carry out large-scale manufacturing in a robust and reproducible fashion. 

The design and optimization of a fermentation process plays a key role in achieving high productivity and robustness at the production scale. In this article, we discuss a strategy to improve operational procedures in an 

fed-batch fermentation through automated process control. To assess the results, we have compared the automated process, which relies on an exponential feeding strategy, to the most commonly used model of stepwise increase of feeding. 

In fed-batch fermentations, the feeding strategies commonly used for increasing cell concentration include feeding at a constant rate, making stepwise increases in feeding, feeding based on feedback control (based on pH or dissolved oxygen measurements [pH-stat or DO-stat]), and exponential feeding. 

Exponential feeding makes use of an empirical model of cell growth to regulate the feeding rate. Ideally, by providing proper nutrient and operating conditions, the cells grow exponentially, achieving a high biomass concentration faster.

In our model for producing recombinant proteins in 

 fermentation is divided into two phases. In the first phase, biomass generation takes place. In the second phase, the cells are devoted to product formation, after protein production has been induced with IPTG. In this fed-batch strategy, all nutrients except for carbon and oxygen are in excess throughout the process. We compared two possible alternatives for process operation with this model. 

In the first approach, we combined simple indirect feedback methods (pH-stat and DO-stat) to determine when to start feeding, followed by manual stepwise increases in feeding based on glucose uptake.

In this setup, fermentation began with a short batch phase (with the presence of glucose in the culture media), but once the carbon source was consumed and the dissolved oxygen (DO) or pH level rose above an upper limit as a result of substrate depletion, a loop in the bioreactor programming software started feeding at a fixed speed. Then we increased the speed of the feeding pump manually according to the glucose and oxygen uptake rates, until the end of the fermentation process (Figure 1).  

In a second approach, we implemented an exponential feeding program designed according to the following equation:  

 is the selected specific growth rate (per h); and 

 (the initial biomass concentration in the bioreactor), V

 (the initial media volume in the bioreactor), µ (the specific growth rate), and biomass yield. 

In this approach, the growth media did not contain glucose and the process began in the exponential fed-batch mode, in which the substrate feeding rate controlled the specific growth rate (Figure 2).  

During exponential feeding, cells can be grown at a desired specific growth rate (µ

) by programming the bioreactor software for an exponential increase in the feeding rate. In this model, we assumed that the desired specific growth rate µ

 should be lower than the maximum growth rate, µ

 (the initial biomass concentration in the bioreactor) drops below the value used to calculate the initial feeding rate (F

), the cells will not be able to increase their growth rate by consuming the excess glucose in the media, as they are already growing at the maximum specific growth rate. On the other hand, at u

, the system will automatically compensate for a variation in the initial biomass concentration by increasing the growth rate, thus removing the deviation and ensuring a robust process.  

Therefore, before the process began, a feed rate profile (µ

 post-induction) was designed according to Equation 1 and programmed into  a programmable controller linked to the feed pump of the bioreactor. This was done by generating loops in the bioreactor software to correlate the feeding rate to µ

In both approaches, we used a biomass probe (Fogale Nanotech, Nimes, France). This instrument allowed us to carry out online monitoring of the growth curves during the batch and fed-batch phases and enabled us to detect deviations from the expected growth before the process failed. 

The nutrient composition of the feed media (basic defined mineral salt media with a high glucose concentration) and the fermentation variables such as temperature, pH, and DO were the same in both models. Samples were taken every hour during all fermentation runs. Cell growth also was followed by optical density measurement at 600 nm, and the biomass concentration was determined as dry cell weight (DCW) for further analysis in g/L. 

A comparison of the typical growth curves of 

 fermentations for the two models is shown in Figure 4. The measurements in picoFaraday/cm

 ) follow the same pattern as off-line OD and net weight (g/L) measurements (data not shown).  

In both approaches, the cultures remained in the lag phase during the first six hours. In the stepwise model, after the lag phase, the biomass increased and reached a specific growth rate of ~0.6/h (µ

) during the batch phase. Feeding was started once the glucose initially added to the medium was depleted (as determined by pH or a DO peak). At this point, the specific growth rate increased gradually from 0.08/h to 0.15/h in response to a manual increase in the feeding rate based on glucose consumption and biomass increases (Figure 4a). 

To ensure good yield with this approach, the operator must regulate the flow of the feed solution to keep glucose levels in the bioreactor below a critical value established for the process (in this case, 1 g/L). It should be taken into consideration that the specific growth rate must not drop below 0.1/h before the point of induction. 

In the automatically controlled feeding model, the biomass and feeding rate increased exponentially at µ

 until the induction point, when IPTG was added to the culture (Figure 4b).  

 during the exponential feeding period was 0.35/h, and the cells went through at least two generations of growth at that rate until they were induced five hours later. It should be noted that no batch phase was observed, because there was no glucose in the  basal media.  

IPTG was added at 60 g/L dry cell weight (corresponding to 5–6 pF/cm

 ) and the exponential feeding was programmed at a µ

 of 0.1/h during the rest of the fed batch period. 

In comparing the two fermentation processes, we see that with the stepwise feeding strategy it took 24 h to reach 3 pF/cm

  whereas with the exponential feeding strategy, the system reached 6 pF/cm

  in 18 h. It took longer to reach a high cell density in the stepwise model because the glucose additions were not optimized. 

To compare the protein yield and robustness of the exponential feeding process at different specific growth rates, we repeated the exponential fermentation strategy under the same operating conditions at least four times for each selected pre-induction specific growth rate (0.30/h, 0.33/h, 0.35/h, 0.37/h, and 0.4/h).  

We then calculated the standard deviation (SD) in the biomass concentration (as dry cell weight) at induction and harvest (Figure 5), and the concentration of the recombinant protein generated (in g/L) by quantifying Coomassie stained gels.  

 of 0.37/h, the SD decreased significantly during both induction and harvest compared to the other selected values. It is important to note, however, that except in the case of the specific growth rate of 0.4/h, the SD of the other preselected µ also were acceptable. 

  of 0.37/h, we also achieved a two-fold increase in protein production compared (9 g/L versus 4.5 g/L) to the stepwise feeding process (Figure 6). The yield of product per cell mass also was 30% higher in the exponential feeding setup, with a recombinant protein accumulation of 140 mg of protein/mg dry cell weight (data not shown).  

It is important to note that despite the high µ

 of 0.37/h, acetate production value was kept below 800 mg/L during the process, which may explain the protein concentration obtained. 

Because both overfeeding and underfeeding of nutrient in fed-batch operations is detrimental to cell growth and product formation, the development of a suitable feeding strategy is critical in fed-batch cultivation processes at production scale.  

Operator-controlled systems are bound to fail because a fermentation process must be carefully formulated and monitored. Because the production of recombinant proteins requires the use of prokaryotic cells which are by nature a fluctuating biological system, it is very important to implement a detailed system for controlling the growth medium, oxygen incorporation, pH, temperature, and glucose additions.  

Here, we suggest a robust model to overcome these problems while increasing protein production. In our particular case, productivity was doubled during the exponential feeding period and the elapsed fermentation time was reduced by 25%, enabling the cost-effective production of recombinant proteins. 

An exponential feeding strategy coupled with automation doubled protein yields while reducing fermentation time by 25%.

Developing an Automatically Controlled Feeding Process in an E. coli Fermentation Process for Recombinant Protein Production

Information technology helps integrate Quality by Design and process analytical technology into standard laboratory procedures and increase efficiency in process and product development. This article presents two case studies in which applying novel information technologies for an advanced bioreactor system improved process development. In the first study, users of a four-fold bioreactor system achieved a quick way to the optimized process and increased product yield nearly 10-fold. In the second study, seamless integration of analytical data allowed for implementation of predictive model control and comprehensive process automation. Along with the increased product quality, the number of experiments was significantly reduced.

In a market where a laboratory must be extremely cost-efficient and judicious with its time, laboratory members are always seeking research tools that will give them the edge they need for market leadership. Additionally, regulatory requirements set through the US Food and Drug Administration have recently become much more extensive. FDA initiatives such as Quality by Design (QbD) and process analytical technology (PAT) have proved to be an additional obstacle to overcome for some. However, these initiatives have driven other companies to speed up their process development. These players thus benefit from the FDA's efforts to strengthen product quality control. 

Following the QbD approach, the FDA promotes PAT as a system for designing, analyzing, and controlling manufacturing as a means of ensuring final product quality and consistency. The PAT concept demands that product quality can be achieved by design through a comprehensive understanding of the product and process risks, and with knowledge of how best to mitigate those risks. The benefits of these systems include improved product quality and efficiency, reduced production costs, and prevention of rejects and reprocessing. These improvements assure that the quality of the product is consistent through processes that are thoroughly developed and documented. Finally, a significant amount of labor and time can be saved and operator safety is increased because of the increased automation during the manufacturing process. 

Because of these new requirements, the amount of data generated has grown immensely. With this increase in data, bench-top bioreactor control systems that integrate new technological approaches to gather, document, and manage large amounts of data have become crucial for successful process development. 

The following two case studies demonstrate how cell culture and fermentation specialists meet key challenges in today's process development laboratories by applying information technologies of an advanced bioreactor system. 

When developing bioprocesses using a systematic parallel approach keeps time, materials and effort to a minimum while providing reproducible data, it optimizes results. 

During the development of an upstream drug substance process using 

, researchers at Richter-Helm Biologics carried out extensive parameter screening using a four-vessel parallel approach. This parallel approach led to a significant increase in productivity yields at the 1-L scale. The results were obtained with a recombinant 

 BL21 strain that carried a heat-inducible expression plasmid constructed by Richter-Helm Biologics. The target protein involved a human active pharmaceutical ingredient (API) that is primarily expressed in inclusion bodies (IB) as a fusion protein. 

In the initial step, a three-phase process was developed; the relevant parameters were automatically controlled. The DASGIP Control 4.0 software gathered and visualized the data during the cultivation process and stored it with the offline data in a central database. Richter-Helm Biologics used a generic fed-batch cultivation strategy as a starting point for the development of the upstream process. 

In the second step, researchers examined the key parameters for target protein expression as described in relevant literature (Table 1). The influence of various parameters on protein expression was compared in a total of 26 cultivations at 1-L scale. The studied parameters are highlighted in gray in the table. The DASGIP data management system supported and simplified the analysis and interpretation of the process-relevant information.    

Table 1. Key parameters that influence target protein expression 

The strain that was used in the study exhibited excellent protein expression in the first four to six hours after induction. Afterwards, no notable target protein formation was observed. The researchers speculated that factors affecting continued expression were inadequate for growth rate caused by the linear feeding profile, genetic instability, or missing medium additives. Genetic instability was eliminated as a possible cause with the help of subsequent tests. Product yields were slightly improved to ~1.5 g/L by adding peptone to the feeding medium and by means of exponential feeding at a growth rate of µw = 0.2 h-1. However, significant protein expression occurred only during the first few hours after induction. 

In the third step, various expression temperatures and higher cell densities were examined with a parallel approach to reduce cell stress caused by the high induction temperature and high expression yield. The experiments were planned applying the principles of Design of Experiments (DOE), which is supported by DASGIP Control 4.0. The input parameters were defined as induction temperature and optical density at induction start. The specified target parameters were product yield and the ratio of insoluble to soluble protein expression (IB/sol-ratio).  

The results of the parallel approach are illustrated in Figure 1. All four of the parallel fermenters used identical medium and feeding profiles. Compared to an induction temperature of 42 °C, these results showed that the product expression phase could be slightly prolonged at 40 °C. By further reducing the induction temperature to 38 °C, target protein formation was sustained with the same high expression yield during the entire process, thus significantly improving the product titer to 4.5 g/L.  

A subsequent induction of the cultures at five hours after initial feeding, combined with an induction temperature of 38 °C, further raised the end concentration of the target protein to 7.5 g/L. Figure 2 shows the influence of induction temperature and cell density using statistical test planning. These results show that a low induction temperature in conjunction with a high cell density yields the optimum product titer.  

The researchers were able to increase the product concentration to 11 g/L by varying the peptone concentration and prolonging the expression phase. The process was then successfully scaled to 10 L to yield a product concentration of >20 g/L through improved oxygen input and the prolonged expression phase. 

"By implementing parallel processing and using identical start conditions for the same precultures, we were able to carry out fast and efficient parameter screening. Simple automation of the fermentation processes ensured the comparability and reproducibility of the cultivations," explains Christian Kaiser from Hamburg-based Richter-Helm Biologics. "The DASGIP system combines a wide range of centrally monitored and controlled parameters with numerous parallel reactors. As a result, we experienced a near 10-fold increase in the product yield, from 1.2 to 11 g/L," Kaiser adds. 

The critical product quality attributes must be defined and the effect of process variables on these specific attributes must be identified to implement PAT in bioprocess development. To obtain this type of information, the use of inline measurements and the integration of analytical data of the cultivation process is critical. However, a conventional bioreactor is limited to the control of dissolved oxygen (DO), pH, temperature, and agitation. A bioreactor generally does not have the capability to control all relevant analytes that can affect culture conditions and by extension, product quality. Hence, a bioreactor system integrating glucose and glutamine, lactate, NH4+, and biomass media concentration measurements is extremely helpful to increase product quality. 

At the University of Delaware, Babatunde A. Ogunnaike and his students have established the foundation for effective online, real-time control of glycosylation patterns on monoclonal antibodies produced with Chinese hamster ovary (CHO) cells. The control strategy, based on the premise that external culture conditions are known to affect glycosylation patterns (Table 2), involves an inner loop for base regulatory control of the key process variables and an outer loop to maintain the glycosylation pattern at a desired set-point. Unfortunately, with current technology, glycosylation measurements are not available frequently enough for traditional feedback loops. Therefore, the outer glycosylation control loop involves model-based control through estimation.  

Table 2. Effects of culture conditions on glycosylation 

For establishing base regulatory control of the key process variables known to affect glycosylation, they designed a bioreactor system with nutrient control and cellular metabolite monitoring in addition to typical bioreactor measurements. With this system, eight analytes (pH, Glutamate, Glutamine, Glucose, Lactate, N+, K+, NH4+) are measured with a Bioprofile 100+ bioanalyzer with autosampler (Nova Biomedical, Waltham, MA) that is equipped with object linking and embedding for process control (OPC) software. With the OPC connection, the Nova Bioprofile 100+ was integrated with the DASGIP Bioreactor Control Software, which allows for closed loop control to be implemented for glucose (Figure 3) and glutamine concentrations in the media. A multi-scale model using process variables to predict glycosylation patterns was developed to establish the outer glycosylation control loop. The controlled inputs of the model include glucose media concentration, glutamine media concentration, DO, pH, temperature, and agitation rate. Specific uptake or excretion rates of glucose, glutamine, lactate, and ammonia were computed by linear regression with biomass. This model allows for model predictive control and targeted modification of process variables.  

"This research has shown how a bioanalyzer equipped with an autosampler was integrated with the bioreactor system through OPC. This integration is advantageous because it allows for the implementation of feedback control and at-line monitoring of the nutrients and metabolites," said Ogunnaike. "The validated DASGIP/Nova reactor system demonstrated the successful control of glucose and glutamine media concentrations. With this system in place, we have established base regulatory control of the key process variables known to affect glycosylation, which is the first step in our strategy to achieve online glycosylation control," he added 

In the future, Ogunnaike and his students propose to characterize glycosylation macro-heterogeneity at-line by adding Groton Biosystem's automated reactor sampling system with Agilent's 2D HPLC to the current bioreactor system. In addition, liquid chromatography coupled to mass spectroscopy will be used to characterize glycan micro-heterogeneity offline. 

The case studies presented here are only two examples showing how information technology can help to increase efficiency in process and product development by the integration of Quality by Design and process analytical technology into standard laboratory procedures. Today's research and development laboratories face more challenges in an increasingly competitive worldwide market.  

The generation of larger and larger amounts of data often results in huge data graveyards. By using the capabilities of extended data mining software, one can simply link the process data with user-defined attributes such as the strain or cell line with the media composition, the controller set-points, and feeding profiles as well as with achieved product yields or viable cell densities. Thus, raw process data turns into usable research information. 

The next challenge is to keep this knowledge available in the long run. Open communication standards not only allow the integration of analyzers into bioreactor systems but also the integration of bioreactor systems into so-called historians. These long-term archives save all process relevant information beyond the single laboratory and can easily be accessed by supervisory control systems globally. Gaining more flexibility in online monitoring and control of the bioprocess is a matter of efficiency. Supervisory control systems and remote control through iPhone, iPad, or any other server provide bench-top scientists as well as laboratory and plant managers with multiple ways to control the running processes online and from any location. 

In conclusion, to meet today's challenges, process development and information technology must work in tandem to create the most beneficial and efficient outcome for their laboratory. 

The authors would like to thank Christian Kaiser from Richter-Helm Biologics, Hamburg, Germany, and Babatunde A. Ogunnaike, University of Delaware for their support and for the detailed insight into their research.   

, is the director of North American operations and 

 is a technical editor in marketing and communications, both at DASGIP AG, Jülich, Germany, +49 (0)2461 980 121, 

 1. Eiteman MA, Altman E. Overcoming acetate in Escherichia coli recombinant protein fermentations, Trends Biotechnol. 2006;24(11):530–6. 

 2. Sorensen H, Mortensen K. (2005) Soluble expression of recombinant proteins in the cytoplasm of Escherichia coli, Microbial Cell Factories, 4, Available from: 

http://www.microbialcellfactories.com/content/4/1/1

 3. Ramirez DM, Bentley WE. Fed-batch feeding and induction policies that improve foreign protein synthesis and stability by avoiding stress response. Biotechnol Bioeng. 1995;47:596–608. 

 4. Strandberg L, Enfors S-O. Factors influencing inclusion body formation in the production of a fused protein in Escherichia coli. Appl Env Microbiol. 1991;57(6):1669–74. 

 5. Choi JH, Keum KC, Lee SY. Production of recombinant proteins by high cell density cultures of Escherichia coli, Chem Eng Sci. 2006;61:876–85. 

 6. Borys MC, Linzer DIH, Papoutsakis ET. Ammonia affects the glycosylation patterns of recombinant mouse placental lactogen-1 by Chinese hamster ovary cells in a pH-dependent manner. Biotechnol Bioeng. 1994;43:505–14. 

 7. Gawlitzek M, Estacio M, Furch T, Kiss R. Identification of Cell Culture Conditions to Control N-Glycosylation Site-Occupancy of Recombinant Glycoproteins Expressed in CHO Cells. Biotechnology and Bioengineering. 2009;103:1164–75. 

Two case studies show how advanced information technologies make process development more efficient.

Using Information Technology to Enhance Upstream Productivity

Improving speed and quality, and reducing the cost of technology transfers is becoming increasingly important in the biopharmaceutical industry. Microbial fermentation processes have challenging equipment requirements, such as high heat and oxygen transfer rates, and ensuring minimal and consistent medium concentration changes resulting from condensate gain during steam-in-place and evaporative losses during media hold and fermentation. This article presents a platform approach to equipment characterization for microbial fermenters. These tests are used to gain understanding of the equipment capabilities before starting process runs. The platform presented here outlines a wet testing approach that has been successfully executed at pilot scale (300-L) for a dual-purpose reactor (used for both mammalian cell culture and microbial fermentation) and in a large-scale (10,000-L) fermenter. Comprehensive equipment characterization using a platform approach streamlines the technology transfer and maximizes success rates during process runs.     

An optimized and fast-growing microbial culture is a dynamic process, and ensuring that the equipment is suitable for meeting these challenges is necessary for project success. These cultures tend to have a high oxygen demand that the fermenter must be able to meet to sustain cell growth and desired productivity. The culture also generates significant amounts of heat that the fermenter must remove to maintain temperature control. To ensure success during actual process runs, it is important to understand oxygen supply and heat removal capabilities before operating the process in the fermenter. Water-based tests were developed to characterize heat removal and oxygen supply capacity to understand if any equipment modifications would be required to meet process requirements. This testing should be performed early in technology transfer facility fit activities to allow time for equipment modifications, if needed. 

Microbial media generally are batched into the fermenter and then steam sterilized-in-place (SIP). The SIP cycle is automated to ensure control within the required temperature range. Temperatures outside the acceptable range could result in insufficiently sterilized medium or overheated medium. Additionally, evaporation or condensation can occur during the SIP cycle. The change in fermenter weight should be characterized or eliminated during SIP operations to ensure the target medium concentration for optimal growth is achieved. After sterilization, the fermentation medium often is held at specified conditions until inoculation. Determining the evaporation rate during the medium hold is essential for achieving the correct medium concentration at the time of inoculation. To compensate for the evaporation rate, additional water can be added during media preparation, and adjustments to the air flow rate can be made to minimize evaporative losses. Water-based testing was developed to understand parameters around media SIP and hold conditions to ensure the target starting media concentration would be met. 

Table 1 lists the fermenter dimensions and operational settings for both pilot and large scales. 

Table 1. Pilot- and large-scale fermenter characteristics. The agitation setting was scaled between the pilot- and large-scale fermenters by maintaining the ratio of agitator power draw (P) per unit volume (V). 

To maintain constant temperature control the fermenter must have sufficient heat removal capacity to remove metabolic heat generated by the cells and mechanical heat generated by the agitator. A common heat transfer scale-up challenge stems from the fact that the relative heat transfer area decreases with increasing fermenter scale. The heat transfer rate (HTR) can be calculated using a simple heat transfer equation: 

 is the mass of water in the fermenter (kg), 

 is the heat capacity of water at 37 °C (4.181 kJ/kg/°C),

 is the temperature of water in the fermenter (°C), 

 is the rate of temperature change as the slope of water temperature versus time curve. 

Strategies for maintaining temperature control involve equipment modifications such as: lowering chilled water or glycol temperature to increase the driving force for heat removal (

), ensuring jacket and coils have minimum resistance, and adding internal cooling coils to increase heat transfer surface area.  

The fermenters were filled to different target weights and heated to approximately 75 °C. The temperature set point was changed to 10 °C, which resulted in a 100% cooling output on the temperature control loop. The rate of temperature change was estimated by the slope of the linear portion of the temperature-versus-time curve from 41 °C to 34 °C. This range encompasses the process temperature set point of 37 °C. The HTR was then calculated using Equation 1.  

The fermenter must have sufficient oxygen transfer to supply enough oxygen to the culture to support cell respiration. Oxygen transfer rates (OTR) are lower as the scale increases because of potentially lower power per volume input, which causes a common scale-up problem. Process modifications can be explored to decrease growth rate and oxygen demand; however, sufficient oxygen supply may be achieved with modifications such as manipulating aeration (air supplemented with oxygen, gas flow rate), agitation (impeller design, number, and location; agitator speed), headspace pressure, and baffles. 

The above equipment settings can influence the OTR in a fermenter. These settings can be optimized to increase the oxygen driving force or the mass transfer coefficient (

) of the reactor. The OTR can be calculated using the following equation: 

 is the mass transfer coefficient (cm/h), 

 is the gas/liquid interface area per liquid volume (cm

 is the saturated dissolved oxygen concentration (mmoles/dm

 is the concentration of dissolved oxygen in the fermenter (mmoles/dm

 is a useful parameter because it helps determine whether a reactor can supply oxygen at a nonlimiting rate. Several methods can be used to estimate 

 During fermentation, a steady state mass balance calculation can be used to equate the oxygen uptake rate of the culture, as measured by a mass spectrometer, to the OTR. Additionally, chemicals such as sodium sulphite can be used to estimate 

 based on the rate of sodium sulphite oxidation, which is equivalent to the oxygen transfer rate.

Dynamic pressure and gassing methods also can be used to estimate 

. A dynamic pressure method can be used by measuring the change in dissolved oxygen (DO) concentration after a small change in system pressure. The dynamic gassing method measures the change in DO concentration versus time for predetermined step changes in the sparge gas oxygen concentration. These dynamic methods can be problematic in microbial systems with high 

 values because the oxygen concentration changes so rapidly that the DO sensor response becomes limiting. However, more accurate 

 can be estimated by accounting for the DO sensor kinetics.

The fermenter must have an SIP cycle that controls temperature to ensure efficient sterilization for bioburden control, but also avoids exposing the media components to excessive temperatures that could cause nutrient degradation. Evaporation or condensation can occur during the SIP cycle. It is desirable to characterize (or eliminate) the change in fermenter weight during SIP operations to ensure the target starting medium concentration is achieved. If significant evaporation or condensation occurs, this should be compensated for in the amount of water that is used to batch the medium. Scale effects of the heat-up and cool-down times of the medium as part of the SIP cycle also should be characterized because these durations can significantly change with scale and may have an unexpected effect on media performance.  

In this study, the fermenters were filled with water and three SIP cycles were performed in series with full cooling between experiments, to understand variability related to evaporation or condensation.  

Evaporative Losses During Media Hold Characterization 

To allow for schedule flexibility during production and to ensure a robust process is implemented, the sterilized media must be held for a period of time before inoculation. If the medium is agitated and air is applied to the fermenter, evaporation may occur during this hold period even if a condenser is installed on the gas outlet line of the fermenter. Determining the evaporation rate is essential for achieving the correct medium concentration at the time of inoculation. To compensate for the evaporation rate, additional water can be added during medium preparation; however, this necessitates a fixed hold time for every batch. Alternately, adjustments to the air flow rate can be made to minimize evaporative losses.  

At the 300-L pilot scale, water was held under process conditions (temperature, air flow rate, volume, agitation, pressure). Holding the medium at process conditions is advantageous because it eliminates the need for any equilibration time before inoculation. Medium hold data from historical processes in the large scale fermenter (10,000-L) indicated significant weight loss during hold periods. Therefore, for large-scale testing, the airflow rate was decreased from the process set point to a pre-inoculation hold-specific set point. Fermenter weight was continually monitored and used to calculate the evaporation rate at both scales. Weight was plotted against hold time and the slope was used as an estimate for evaporative loss occurring during the media hold.  

The oxygen uptake rate (OUR) for the culture in question is approximately 300 mmol/L/h. The heat generated by the cells can be estimated using the common method shown in Equation 3: 

 is the oxygen uptake rate (mmol/kg/h), and 

Per Equation 3, the heat generated by the cells is approximately 150 kJ/kg/h. Typically, the next largest contributor of heat to the fermenter is the agitator. This was measured at large scale using the agitator current draw and estimated to be approximately 18 kJ/kg/h. As such, it is considered a small contributor to the overall heat load, and the HTR required for the fermenters is estimated as 150 kJ/kg/h. 

Figure 1 shows the HTR calculated using Equation 1 for each fermenter weight tested at the pilot and large scale, respectively. The calculated HTR at pilot scale varied between 555 and 750 kJ/kg/h with the fermenter weights from 150 to 300 kg, and at large scale the HTR varied between 200 and 235 kJ/kg/h with the fermenter weights between 5,000 and 10,000 kg; therefore both fermenters had sufficient HTR capacity to support the process need of 150 kJ/kg/h. 

Figure 1. Heat transfer rate at pilot and large scales. 

For both fermenters, only the bottom impeller was submerged at the lowest test weight. The maximum heat transfer rate was observed at 200 and 7,000 kg, the pilot-scale and large-scale, respectively. The upper impeller is partially or just barely submerged at these weights. The lowest HTR was observed at the maximum working volume of 300 kg for the pilot scale and 10,000 kg for the large-scale fermenter. The lower HTR at the maximum working volume is expected and likely caused by the decreased surface area to volume ratio at the higher volume. Another factor that may have contributed to the heat removal rates was the air sparge rate, which was kept constant for all fermenter weights; therefore, the volume of sparge air per volume of water (VVM) decreased as the fermenter weight increased (shown in Figure 1). Introducing gas into the liquid likely reduces the apparent density of the fluid and because of gas hold-up, the liquid comes in contact with additional cooling surface.  

 of the pilot scale reactor at processing conditions, the dynamic gassing method was explored. A procedure was drafted to measure the 

 while accounting for the DO sensor lag. This method development is on-going.  

Table 2 summarizes the gain or loss observed during SIP cycles at pilot and large scale. For the large scale, no experiments were performed because historical SIP data showed minimal and consistent gain and losses. Testing performed at pilot scale demonstrated that the weight change during SIP also was consistent and minimal. Therefore, it was concluded that the water amount used to batch medium did not need to be adjusted to account for any considerable gain or loss during the SIP operation. From the evaluation of data from SIP cycles done on actual media at the pilot scale, it can be concluded that media and water behave similarly and that water is a good surrogate for testing purposes before starting actual production runs. 

Table 2. Sterilize-in-place (SIP) gain or loss results. Pilot scale test results are presented. For large scale, historical SIP data are presented.  

Figure 2 shows the evaporation rate as observed at the pilot scale. The test was performed at process conditions. Tests 1 and 2 show very consistent results with an average evaporative loss of approximately 0.12% of starting fermenter weight/h. Media hold conditions or the amount of water used to prepare the medium should be further evaluated to ensure the target starting media concentration is met. 

Figure 2. Pilot-scale evaporative losses. Tests 1 and 2 were performed with the condenser on the fermenter enabled, and test 3 was performed with the condenser disabled. The condenser reduces the evaporative loss by almost a factor of two. 

Large-scale data demonstrated that the need for any water adjustments to achieve the target media concentration is eliminated when the airflow rate is decreased during the media hold. With the airflow rate at 10% of the processing set point, the evaporative loss was negligible. Over approximately 40 h, the reactor weight dropped by approximately 0.02% of starting weight (Figure 3), which is within the calibration tolerance of the fermenter load cells. At the end of the hold, the air flow rate was increased to the processing set point. The data indicated that no additional equilibration time was needed to achieve DO saturation. 

Figure 3. Large-scale evaporative losses. 

The wet testing presented in this article represents a platform approach to characterizing microbial fermenters. These tests can be used to gain an understanding of the equipment capabilities before starting actual process runs and further ensure project timelines and success criteria are met. Measuring the heat removal and oxygen transfer capability of a fermenter helps ensure a reactor meets process needs. Water-based testing can be performed to help develop appropriate media SIP and hold conditions, thus ensuring the target starting media concentration is met. 

These equipment characterization studies are not required to be performed for every new product that is introduced into a facility as long as major equipment changes have not occurred (e.g., SIP cycles). However, it is useful to confirm that there have been no shifts in equipment capability by periodically repeating these tests. 

Wet testing of microbial fermenters allows for a greater understanding of the equipment's capabilities.

Best Practices for Microbial Fermenter Equipment Characterization

Media compositions used for cultivating recombinant cell lines not only directly influence the cells' physiological phenotype and fermentation performance, and quality of the expressed product, but the development of novel media formulations also provide a mechanism of generating manufacturing intellectual property for a particular product. Biopharmaceutical companies and media suppliers are therefore recognizing the strategic importance of continued investment in the development of novel media formulations. Historically, raw materials involved in the production of biologics have included complex animal-derived components. Although a decade has passed since cases of bovine spongiform encephalopathy and the resulting transmissible spongiform encephalopathies were reported, their association with raw materials is still a major concern. Establishing a well-defined media formulation containing no animal-derived components is the ultimate aim for most biopharmaceutical companies. There are now well researched, chemically defined alternatives available that promise to meet these goals.     

In recent years, a market surge in biopharmaceuticals has warranted extensive research and development in advanced cell culturing techniques and subsequent methods of optimizing bioprocesses. Media development is one of the most critical stages in biopharmaceutical manufacturing. Indeed media components have such a strong impact, they can account for up to 30% of the total production cost.

  This area of development offers the potential to dramatically improve yield and quality of the expressed product.

The ideal cell culture medium, whether for use in mammalian or microbial systems, is one that provides process consistency, robustness, and batch-to-batch reproducibility. Essentially, a raw material must meet most, if not all, criteria listed below.

It will produce maximum product yield or biomass per gram of substrate used.

It will produce the maximum  product concentration or biomass.

It will permit the maximum rate of product formation.

There will be a minimum yield of undesired products.

It will be of a consistent quality and be readily available throughout the year.

It will cause minimal problems during media preparation and sterilization.

It will cause minimal problems during other steps of the production process, particularly fermentation, extraction, purification, and waste treatment.

For many years, economic constraints have dictated media composition. This has led to the widespread use of complex, readily available raw materials, making large-scale fermentations reliant on cheap sources of carbon and nitrogen, which are often by-products of other industries such as corn steep liquor from the corn-starch industry and beet molasses from the sugar industry. Combining such complex components with animal-derived hydrolysates has increased the scope of product manufacture, creating high yielding processes at minimal costs. The abundance of biosynthetic precursors and growth-promoting agents make complex media the composition of choice, primarily because they accelerate growth and enhance productivity. Complex raw materials usually are derived from animal-sourced processes, such as hydrolysed peptones and sera. They also may be non-animal–derived, such as the by-products beet molasses and corn steep liquor. They can contain numerous individual components, some of which are semi-characterized and many more are uncharacterized. In many cases, biomass yields are greater with complex and semi-defined media than with chemically defined media. However, data from physiological studies is more difficult to interpret and can be clouded or influenced by many intrinsic parameters. 

Growth precursors found in such complex components may be channelled directly into anabolic pathways, thus saving metabolic energy.

  This offers an immediate advantage in terms of culture metabolism but ultimately makes process definition and development problematic.  

Fermentations using complex media have worked successfully for many years, and strain selection is often based around current media and cultivation conditions. Media development, however, is largely empirical, with little research into defining such raw materials. For example, a modified strain of the yeast 

, which had been labelled the ultimate solution to lignocellulose-derived xylose, was found to require yeast extract, additional hexose sugar, and oxygenation,

  thus making growth in a chemically defined media difficult without intensive investigation. Therefore, the final industrial environment must be considered to prevent unanticipated costs at later stages. Both 

 have extensive biosynthetic capacity and can grow well in defined media. In contrast, the biosynthetic capacity of many lactic acid bacteria are limited and they require complex or extensively supplemented media for efficient growth.

The quest for increased productivity combined with patient safety has encouraged biopharmaceutical companies to invest more time and money into their processes and for raw material suppliers to explore new chemically defined versions of media.  

There are two areas that are generating the most interest these days. The first is the elimination of complex and undefined media components and their replacement with known chemical composition and the second is the replacement of animal-derived materials with non-animal–derived materials. With the exception of some processes, most non-animal–derived materials and chemically defined media equivalents do not meet the same performance criteria as their counterparts by not meeting the specific nutrient requirements of individual cell lines. Keeping this in mind, the most effective approach to the development of chemically defined media is to design the media formulation based on the specific nutritional needs of the cell line.

  In today's biopharmaceutical industry, the following criteria significantly dictate process development:   

primary raw materials will meet regulatory requirements

they will consist of non-animal–derived components

A shift toward chemically defined media increases process cost and economically this must be offset against improvement elsewhere in the process, whether this be increased titers or simpler purification requirements.    

Chemical definition at the laboratory scale is relatively inexpensive and can be vital for the investigation of many factors including physiological and biochemical assessment of growth, but work at this scale must be centered on the eventual scaled-up process.  

The economic implications imposed on a process following the switch to a chemically defined medium are somewhat offset by reductions in raw material variability and reduced controlled and uncontrolled process deviations. This in turn favors batch-to-batch consistency in products and permits simpler purification strategies. Process predictability has become important for biopharmaceutical manufacturers, and using components of a defined chemical composition can reduce process fluctuations, improve overall process control, and enhance operability.  

Furthermore, it is far easier to optimize a process of known biochemical composition than to base development on empirical methods. Most mathematical Design of Experiments (DoE) based on raw material evaluation give more significant and meaningful results if used on chemically defined compositions and can be beneficial in the switch from complex to defined. The advantages and disadvantages of replacing complex media with chemically defined media are listed in Table 1.    

Table 1. A comparison between the major advantages and disadvantages of complex and chemically defined media 

Media development and the need for chemically defined and non-animal–derived materials has become very significant in today's industry to eliminate potential risks to patients.  

Animal-derived components have hit the headlines on several occasions. The 1970s brought cases of mycoplasma contamination, endotoxin concerns were raised in the 1980s, and bovine spongiform encephalitis (BSE) and the resulting transmissible spongiform encephalopathy (TSE) fears dominated the 1990s. The biopharmaceutical industry has since tightened controls over the sourcing and final composition of raw materials. In February 2002, the US Food and Drug Administration strengthened its regulations for BSE protection systems for the pharmaceutical sector to reduce theoretical risks associated with it. 

Animal cell culture was first established for the production of viral vaccines in the 1950s, using monkey kidney cells to produce the polio vaccine. Successful growth of this adherent cell line depended on the addition of serum to the growth media.   

Bovine serum contains numerous beneficial components, including macromolecular proteins, low-molecular weight nutrients, anti-oxidants, and carrier proteins for water-insoluble components such as hormones, lipids, amino acids, and globulins. In addition, serum contains a high level of albumin and transferrin. The former is believed to protect cells from shear forces and stress factors generated under bioreactor conditions, such as fluctuations in pH or nutrient concentrations, and the latter is an iron-carrying glycoprotein needed by CHO and NS0 cell lines for optimal growth. 

Despite the positive properties of bovine serum, this undefined raw material can contain adventitious agents and detrimental by-products such as bacterial endotoxins or immunogenic contaminants. More commonly, it can contain viral contaminants, which often do not produce cytopathic effects or morphological changes in cell cultures, and once present, viral contaminants are almost impossible to remove. Other potential contaminants include fungi, prions, and bacteria/mycoplasma, thus raising concerns about patient safety.  

It was not until the middle of the 1960s that a serum-free medium was first used for mammalian cell culture.

  To achieve the same growth-promoting effects as experienced with serum, it was necessary to replace the role of serum with other animal-derived components such as albumin, insulin, and transferrin, which for many years were obtained from animal-derived sources.  

The adaptation of mammalian cells to serum-free conditions greatly increases the safety of the subsequent bioproduct and there are regulations to avoid animal-origin components for fermentation. Research into serum replacement gained momentum in the 1970s, when studies replaced the complex serum component with an equally complex animal-derived protein hydrolysate, thus still presenting a contamination risk to the bioproduct. This work was subsequently followed by their replacement with non-animal–derived protein hydrolysates, such as vegetable peptones. These are being used extensively in the industry to replace serum and now chemically defined serum replacements are widely available.   

Unfortunately for a few cell lines, chemically defined media do not adequately meet the specific nutrient requirements, so media screening has become exhaustive and supplementation may still be necessary in many instances. Fortunately in these cases, many of the necessary supplements, including insulin, transferrin, and albumin are now readily available from non-bovine sources. This enhances their overall biological safety but side-steps the issue of eliminating media complexity and subsequently process complexity. Only further raw material development will make such changes easier. 

Supplementing animal and human cell culture with sera of animal origin still remains a standard practice despite its reputation as an ill-defined, highly variable component. More and more companies are, however, choosing to develop biologics in serum-free conditions. For the manufacture of recombinant therapeutics using bacteria, casein- or meat-derived hydrolysates are frequently used in such fermentations. The trend in this area is to replace casein-derived hydrolysates because most of them are manufactured with animal-origin enzyme systems. Many media suppliers put a significant amount of work into developing hydrolysates produced using non-animal–derived enzyme systems such as vegetable hydrolysates. Quest International (Irvine, CA) was the first to market non-animal–derived protein hydrolysates for serum replacement, maintaining at least the same productivity as when serum was used. Since this breakthrough, a series of non-animal–derived protein hydrolysates have been developed such as the Hy-Pep series, including Hy-Soy, Hy-Rice, Hy-Cotton, Hy-Wheat, Hy-Pea, and Hy-Yest, all developed as serum replacement alternatives. Complementing these is a series of large-scale bulk fermentation hydrolysates. In July 2009, SAFC Biosciences (Lenexa, KS) introduced EX-CELL CD Hydrolysate Fusion, the first chemically defined cell culture supplement based on hydrolysate characterization that promises to match the capabilities of traditional undefined hydrolysate raw materials in CHO, NS0, and Sp2/0 bioprocesses. In November 2009, SAFC Biosciences further introduced EX-CELL ANTIFOAM, a non-animal–origin antifoam to increase consistency, efficiency, and stability of cell culture manufacturing. 

Movement to less complex and animal-origin free culture media does not come without its share of problems. The term 

 refers to any component not directly derived from eukaryotic animals (excluding higher plants, fungi, protozoa, and algae) and media suppliers are now being asked to provide certificates of origin of their components ensuring their source is non-animal–derived, and if it is, then it is from a country not affected by the BSE outbreak.  

Suppliers of raw materials are under no obligation to provide raw materials free from contaminants, but audits performed by biopharmaceutical companies exercise stringent guidelines and specifications highlighted by the regulatory authorities. Raw materials must be fit for their intended use and show a high degree of consistency between batches. This generally presents no problem because most raw material suppliers adhere to current good manufacturing practices (cGMP) and are ISO 9001:2000 accredited, which is  ensuring the relevant SOP documentation is readily available on request.  

The degree to which a product has come into contact with a raw material of animal origin is defined as a level. The primary level indicates that the finished product itself and its associated manufacturing process are free from animal-derived components. 

Secondary level classification is the most common, and ensures that raw materials used to manufacture a primary raw material to be used in a manufacturing process are animal-origin free. For example a peptone derived from 

 grown in a culture containing non-animal–derived components would be classified as secondary level.  

The most challenging standard of a component is the tertiary level, requiring that starting raw materials, needed to produce secondary raw materials, are animal-origin free. A raw material can be classified as tertiary level if it contains animal-derived components that are classified as very low risk. This requires an extensive audit trail and because of difficulties in establishing, monitoring, controlling, and maintaining such stringent guidelines, many  biopharmaceuticals manufacturers are avoiding tertiary level components and settling with secondary level materials. Even tertiary level components can cause a debate. Should material be classified as tertiary level if it has been at all associated with material of animal origin? Establishing animal-origin–free facilities requires that all of its suppliers provide animal-origin-free products, and that all of their suppliers do the same. Even if one company uses no animal-derived components, there remains a risk of contaminant carryover from the processing steps of its suppliers.

Biopharmaceutical companies are beginning to take a responsible approach to media formulation, and significant effort is being invested in the development of well defined, animal-origin free alternatives. It is important, however, to establish actual risk when deciding the level of animal-origin free components beyond the primary level. If a critical or expensive bioproduct such as insulin is to be manufactured, then the biopharmaceutical manufacturer may well be justified in setting raw material specifications. Many are seeking secondary level animal-origin–free insulin despite the injectable being a primary level product.

  This shows that before considering the animal-origin–free level of a raw material, it is important to review all aspects of the process and determine the overall potential risk of the product.  

The quest for increased productivity and better process control combined with patient safety has encouraged biopharmaceutical companies to use chemically defined media for cell culture.

Balancing Media Development with Patient Safety

Protein hydrolysates are routinely used as cell culture supplements to enhance the overall performance of many biopharmaceutical production systems. This enhancement is subject to the additive effect of the native hydrolysate components and the supplemented growth or production medium. Therefore, it is necessary to experimentally determine the proper hydrolysate dosage for a given hydrolysate medium combination that provides the desired optimization effect such as better growth promotion, enhanced cell viability, increased target protein production, or a combination of all three. In mammalian systems, hydrolysates have been used in combination with a variety of other supplements to help reduce or eliminate serum requirements in systems using traditional basal media. Today, many high-performing, richly formulated chemically defined media have become available as stand-alone substrates for biopharmaceutical production. This article shows that these chemically defined media can benefit from the addition of hydrolysates and other supplements. It also demonstrates that in other cases, plant-derived hydrolysates can partially replace a significant portion of the active ingredients in these rich media.       

Plant-derived hydrolysates can be used as valuable and practical tools to improve cell culture performance.

BioPharm International-06-02-2010

BioPharm International Digital Edition, June 2010 Supplement (PDF)

Developing an Automatically Controlled Feeding Process in an E. coli Fermentation Process for Recombinant Protein Production

Using Information Technology to Enhance Upstream Productivity

Best Practices for Microbial Fermenter Equipment Characterization

Balancing Media Development with Patient Safety

Partial Replacement of Chemically Defined Media with Plant-Derived Protein Hydrolysates