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:
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 Escherichia coli 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.1
In our model for producing recombinant proteins in E. coli, 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.1,2
in which F(t) is the feeding rate at time t (in L/h); F0 is the initial feeding rate (in L/h); uset is the selected specific growth rate (per h); and t is the time (in h). The coefficient F0 is known to be a function of X0 (the initial biomass concentration in the bioreactor), V0 (the initial media volume in the bioreactor), µ (the specific growth rate), and biomass yield.
During exponential feeding, cells can be grown at a desired specific growth rate (µset) by programming the bioreactor software for an exponential increase in the feeding rate. In this model, we assumed that the desired specific growth rate µset should be lower than the maximum growth rate, µmax. This is because at uset = umax, if the value of X0 (the initial biomass concentration in the bioreactor) drops below the value used to calculate the initial feeding rate (F0), 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 uset < umax, 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.
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.