Thursday, June 4, 2015
Wednesday, June 3, 2015
Controllable Parameters of Mammalian Cell Culture
Einstein famously said:
Insanity [is] doing the same thing over and over again and expecting different results.Which got me thinking...
Question: Is there such a thing as doing the same thing over and over again and getting different results?
Answer: Biotech Manufacturing
Rob Caren once asked: "How hard can (large-scale) cell culture be? It's ONE unit operation."
He also observed that large-scale chromatography shouldn't be that hard either:
- Send the pool through that fixed-bed reactor.
- When the optical density is not zero shut this valve and open that valve.
- Try not to send product to drain.
On both counts, he's right, but I can only speak to the cell culture side.
When running bioreactors, there are a few parameters that are within your control. These parameters are sometimes called "knobs" because the production team can literally go to the control system and "turn a knob" (or click a few buttons on the SCADA) to change the parameter.
For the bioreactor or fermentor, those knobs are:
pH. The intracellular pH is known to change the activity of enzymes that run the rates of metabolic reactions. Since cells regulate intracellular pH, the best you can do is to control the extracellular pH. In reality, the pH for the process is specified. And if specified well, the specification will come with a target range and a proven acceptable range. At commercial scale, you ought to be able to operate within the proven acceptable range. For more information on pH control in mammalian cultures, see this.
Dissolved oxygen. Maximizing cellular productivity means aerobic metabolism. From university biochemistry, we know that anaerobic metabolism produces far less energy (2 ATP) than aerobic metabolism (38 ATP). We also know from chemistry that the solubility of oxygen in water is quite low (<10 mg/L when temperature > 15 degC). Therefore, it is important that the bioreactor supplies oxygen. Some bioreactors supply air and others supplement with oxygen. While the dissolved oxygen range is typically specified by the process, the air supply, air/oxygen mix, flow rates and sparge-type can be determined by the facility. As previously discussed, dissolved oxygen depends on other parameters such as agitation and temperature and can be changed within the specified range.
Temperature. Temperature control happens with the bioreactor jacket where water is flowed around the outside surface of the bioreactor. When the temperature gets too hot, the control system sends cooler water; and when the temperature of the culture gets too cold, the control system sends hotter water. While temperature is typically specified, there are processes that will intentionally cool the culture to reduce the rate of metabolic reactions and extend culture viability. Also, since temperature is defined in a range, the setpoint is a turnable knob.
Agitation. Agitation is typically not specified by the process, just that the cells must be suspended (i.e. not settled on the bottom of the bioreactor). In practice agitation rate is determined by power-to-volume calculations and stays constant for the bioreactor, nonetheless, this is a manipulatable parameter when running bioreactors.
Timing of Inoculation. Inoculation density is often specified by the process. But there's no way to "dial down" or "dial up" inoc density in a control system somewhere like you can with pH, dissolved oxygen, temperature and agitation. When cells grow, the cell density naturally increases, so the way to control inoculation density is to time it (i.e. wait vs. not wait).
Timing of feeds. In fed-batch cultures, additional nutrients are added to the culture. The additional nutrients tend to increase the osmolality and the additional volume can help dilute the cellular waste (like ammonium). Not in all processes, but in some processes, the timing of feeds have been shown to impact culture productivity.
Timing of shifts. Some processes are specified with changes in setpoints of the aforementioned parameters (e.g. pH or Temperature). The shift specifications come in the form of: "When the culture duration reaches X hours" or "when the cell density reaches Y x 106," then change the set point up/down.
When building multivariate models, it is crucial that controllable parameters are modeled as factors and here's why:
When your model shows can correlate significant main effects and interactions to some process output (e.g. titer or quality attribute), you can actually step out of theory and prove it in practice.
When running bioreactors, there are a few parameters that are within your control. These parameters are sometimes called "knobs" because the production team can literally go to the control system and "turn a knob" (or click a few buttons on the SCADA) to change the parameter.
For the bioreactor or fermentor, those knobs are:
pH. The intracellular pH is known to change the activity of enzymes that run the rates of metabolic reactions. Since cells regulate intracellular pH, the best you can do is to control the extracellular pH. In reality, the pH for the process is specified. And if specified well, the specification will come with a target range and a proven acceptable range. At commercial scale, you ought to be able to operate within the proven acceptable range. For more information on pH control in mammalian cultures, see this.
Dissolved oxygen. Maximizing cellular productivity means aerobic metabolism. From university biochemistry, we know that anaerobic metabolism produces far less energy (2 ATP) than aerobic metabolism (38 ATP). We also know from chemistry that the solubility of oxygen in water is quite low (<10 mg/L when temperature > 15 degC). Therefore, it is important that the bioreactor supplies oxygen. Some bioreactors supply air and others supplement with oxygen. While the dissolved oxygen range is typically specified by the process, the air supply, air/oxygen mix, flow rates and sparge-type can be determined by the facility. As previously discussed, dissolved oxygen depends on other parameters such as agitation and temperature and can be changed within the specified range.
Temperature. Temperature control happens with the bioreactor jacket where water is flowed around the outside surface of the bioreactor. When the temperature gets too hot, the control system sends cooler water; and when the temperature of the culture gets too cold, the control system sends hotter water. While temperature is typically specified, there are processes that will intentionally cool the culture to reduce the rate of metabolic reactions and extend culture viability. Also, since temperature is defined in a range, the setpoint is a turnable knob.
Agitation. Agitation is typically not specified by the process, just that the cells must be suspended (i.e. not settled on the bottom of the bioreactor). In practice agitation rate is determined by power-to-volume calculations and stays constant for the bioreactor, nonetheless, this is a manipulatable parameter when running bioreactors.
Timing of Inoculation. Inoculation density is often specified by the process. But there's no way to "dial down" or "dial up" inoc density in a control system somewhere like you can with pH, dissolved oxygen, temperature and agitation. When cells grow, the cell density naturally increases, so the way to control inoculation density is to time it (i.e. wait vs. not wait).
Timing of feeds. In fed-batch cultures, additional nutrients are added to the culture. The additional nutrients tend to increase the osmolality and the additional volume can help dilute the cellular waste (like ammonium). Not in all processes, but in some processes, the timing of feeds have been shown to impact culture productivity.
Timing of shifts. Some processes are specified with changes in setpoints of the aforementioned parameters (e.g. pH or Temperature). The shift specifications come in the form of: "When the culture duration reaches X hours" or "when the cell density reaches Y x 106," then change the set point up/down.
When building multivariate models, it is crucial that controllable parameters are modeled as factors and here's why:
When your model shows can correlate significant main effects and interactions to some process output (e.g. titer or quality attribute), you can actually step out of theory and prove it in practice.
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