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


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

BioPharm International Supplements


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

Experimental Design

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

Figure 1
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:

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.

Figure 2
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 (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.

Figure 3
Therefore, before the process began, a feed rate profile (µset pre-induction and µset 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 µset (Figure 3).

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.

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