The Gram-negative bacterium E. coli is the host of choice for a multitude of used recombinant proteins. Generally, cultivation is easy, media are cheap, and a high product titer can be obtained. However, harsh induction procedures using isopropyl β-d-1 thiogalactopyranoside as inducer are often referred to cause stress reactions, leading to a phenomenon known as “metabolic” or “product burden”. These high expressions of recombinant proteins mainly result in decreased growth rates and cell lysis at elevated induction times. Therefore, approaches tend to use “soft” or “tunable” induction with lactose and reduce the stress level of the production host. The usage of glucose as energy source in combination with lactose as induction reagent causes catabolite repression effects on lactose uptake kinetics and as a consequence reduced product titer. Glycerol—as an alternative carbon source—is already known to have positive impact on product formation when coupled with glucose and lactose in auto-induction systems, and has been referred to show no signs of repression when cultivated with lactose concomitantly. In recent research activities, the impact of different products on the lactose uptake using glucose as carbon source was highlighted, and a mechanistic model for glucose-lactose induction systems showed correlations between specific substrate uptake rate for glucose or glycerol (q_s,C) and the maximum specific lactose uptake rate (q_s,lac,max). In this study, we investigated the mechanistic of glycerol uptake when using the inducer lactose. We were able to show that a product-producing strain has significantly higher inducer uptake rates when being compared to a non-producer strain. Additionally, it was shown that glycerol has beneficial effects on viability of cells and on productivity of the recombinant protein compared to glucose.

The bacterium Escherichia coli is a major host for recombinant protein production of non-glycosylated products. Depending on the expression strategy, the recombinant protein can be located intracellularly, which often leads to protein aggregates inside of the cytoplasm, forming so the called inclusion bodies (IBs). When compared to other protein expression strategies, inclusion body formation allows high product titers and also the possibility of expressing proteins being toxic for the host. In the past years, the comprehension of inclusion bodies being only inactive protein aggregates changed, and the new term of non-classical inclusion bodies emerged. These inclusion bodies are believed to contain a reasonable amount of active protein within their structure. However, subsequent downstream processing, such as homogenisation of cells, centrifugation or solubilisation of IBs, is prone to variable process performance and is often known to result in low extraction yields. It is hypothesised that variations in IB quality attributes are responsible for those effects and that such attributes can be controlled by upstream process conditions. In this review, we address the impact of process design (process parameters) in the upstream on defined inclusion body quality attributes. The following topics are therefore addressed: (i) an overview of the range of inclusion body applications (including emerging technologies); (ii) analytical methods to determine quality attributes; and (iii) screws in process engineering to achieve the desired quality attributes for different inclusion body–based applications. Process parameters in the upstream can be used to trigger different quality attributes including protein activity, but are not exploited to a satisfying content yet. Design by quality approaches in the upstream are already considered for a multitude of existing processes. Further intensifying this approach may pave the industrial application for new IB-based products and improves IB processing, as discussed within this review.

The bacterium E. coli is a major host for recombinant protein production of non-glycosylated products. Depending on the expression strategy, the recombinant protein can be located intracellularly. In many cases the formation of inclusion bodies (IBs), protein aggregates inside of the cytoplasm of the cell, is favored in order to achieve high productivities and to cope with toxic products. However, subsequent downstream processing, including homogenization of the cells, centrifugation or solubilization of the IBs, is prone to variable process performance or can be characterized by low extraction yields as published elsewhere. It is hypothesized that variations in IB quality attributes (QA) are responsible for those effects and that such attributes can be controlled by upstream process conditions. This contribution is aimed at analyzing how standard process parameters, such as pH and temperature (T) as well as different controlled levels of physiological parameters, such as specific substrate uptake rates, can vary IB quality attributes. Classical process parameters like pH and T influence the expression of analyzed IB. The effect on the three QAs titer, size and purity could be successfully revealed. The developed data driven model showed that low temperatures and low pH are favorable for the expression of the two tested industrially relevant proteins. Based on this knowledge, physiological control using specific substrate feeding rate (of glucose) qs,Glu is altered and the impact is tested for one protein. Time dependent monitoring of IB QA—titer, purity, IB bead size—showed a dependence on classical process parameters pH and temperature. These findings are confirmed using a second industrially relevant strain. Optimized process conditions for pH and temperature were used to determine dependence on the physiological parameters, the specific substrate uptake rate (qs,Glu). Higher qs,Glu were shown to have a strong influence on the analyzed IB QAs and drastically increase the titer and purity in early time stages. We therefore present a novel approach to modulate—time dependently—quality attributes in upstream processing to enable robust downstream processing.

Bayesian optimization is a stochastic, global black‐box optimization algorithm. By combining Machine Learning with decision‐making, the algorithm can optimally utilize information gained during experimentation to plan further experiments—while balancing exploration and exploitation. Although Design of Experiments has traditionally been the preferred method for optimizing bioprocesses, AI‐driven tools have recently drawn increasing attention to Bayesian optimization within bioprocess engineering. This review presents the principles and methodologies of Bayesian optimization and focuses on its application to various stages of bioprocess engineering in upstream and downstream processing.

Escherichia coli still serves as a beloved workhorse for the production of many biopharmaceuticals as it fulfills essential criteria, such as having fast doubling times, exhibiting a low risk of contamination, and being easy to upscale. Most industrial processes in E. coli are carried out in fed-batch mode. However, recent trends show that the biotech industry is moving toward time-independent processing, trying to improve the space–time yield, and especially targeting constant quality attributes. In the 1950s, the term “chemostat” was introduced for the first time by Novick and Szilard, who followed up on the previous work performed by Monod. Chemostat processing resulted in a major hype 10 years after its official introduction. However, enthusiasm decreased as experiments suffered from genetic instabilities and physiology issues. Major improvements in strain engineering and the usage of tunable promotor systems facilitated chemostat processes. In addition, critical process parameters have been identified, and the effects they have on diverse quality attributes are understood in much more depth, thereby easing process control. By pooling the knowledge gained throughout the recent years, new applications, such as parallelization, cascade processing, and population controls, are applied nowadays. However, to control the highly heterogeneous cultivation broth to achieve stable productivity throughout long-term cultivations is still tricky. Within this review, we discuss the current state of E. coli fed-batch process understanding and its tech transfer potential within continuous processing. Furthermore, the achievements in the continuous upstream applications of E. coli and the continuous downstream processing of intracellular proteins will be discussed.

Biopharmaceutical drug substances are generally produced using fermentation technology and are subsequently purified in the following downstream process. For the determination of critical quality attributes (CQAs), such as target protein titer and purity, monitoring tools are required before quality control analysis. We herein present a novel reversed phase liquid chromatography method (RPLC), which enables facile and robust protein quantification during upstream and downstream processing of intracellularly produced proteins in E. coli. The overall goal was to develop a fast, robust and mass spectrometry compatible method which can baseline resolve and quantify each protein of interest. Method development consisted of three steps, oriented on an Analytical Quality by Design (AQbD) workflow: (i) the stationary phase as primary parameter was chosen based on state-of-the art technology thus minimizing protein on-column adsorption and providing high efficiency, (ii) secondary parameters (i.e. gradient conditions and column temperature) were optimized applying chromatographic modeling, and (iii) the established Method Operable Design Region (MODR) was challenged and confirmed during robustness testing, performed in-silico and experimentally by a Design of experiment (DoE) based approach. Finally, we validated the RPLC method for pivotal validation parameters (i.e. linearity, limit of quantification, and repeatability) and compared it for protein quantification against a well-established analytical methodology. The outcome of this study shows (i) a protocol for RPLC development using an AQbD principle for new method generation and (ii) a highly versatile RPLC method, suited for quick and straightforward recombinant protein titer measurement being applicable for the detection of a broad range of proteins.