Integrated process development: The key to improve Fab production in E. coli.

Bioprocess development and optimization is a challenging, costly, and time-consuming effort. In this multidisciplinary task, upstream processing (USP) and downstream processing (DSP) are conventionally considered distinct disciplines. This consideration fosters "one-way" optimization disregarding interdependencies between unit operations; thus, the full potential of the process chain cannot be achieved. Therefore, it is necessary to fully integrate USP and DSP process development to provide balanced biotechnological production processes. The aim of the present study was to investigate how different host/secretory signal/antigen binding fragment (Fab) combinations in E. coli expression systems influence USP, primary recovery performance and the final product quality. We ran identical fed-batch cultivations with 16 different expression clones to study growth and product formation kinetics, as well as centrifugation efficiency, viscosity, extracellular DNA, and endotoxin content, important parameters in DSP. We observed a severe influence on cell growth, product titer, extracellular product, and cell lysis, accompanied by a significant impact on the analyzed parameters of DSP performance. Our results provide the basis for future research on integrated process development considering interdependencies between USP and DSP; however, individual products need to be considered specifically. These interdependencies need to be understood for rational decision-making and efficient process development in research and industry.

High-throughput microbioreactor provides a capable tool for early stage bioprocess development.

Tremendous advancements in cell and protein engineering methodologies and bioinformatics have led to a vast increase in bacterial production clones and recombinant protein variants to be screened and evaluated. Consequently, an urgent need exists for efficient high-throughput (HTP) screening approaches to improve the efficiency in early process development as a basis to speed-up all subsequent steps in the course of process design and engineering. In this study, we selected the BioLector micro-bioreactor (µ-bioreactor) system as an HTP cultivation platform to screen E. coli expression clones producing representative protein candidates for biopharmaceutical applications. We evaluated the extent to which generated clones and condition screening results were transferable and comparable to results from fully controlled bioreactor systems operated in fed-batch mode at moderate or high cell densities. Direct comparison of 22 different production clones showed great transferability. We observed the same growth and expression characteristics, and identical clone rankings except one host-Fab-leader combination. This outcome demonstrates the explanatory power of HTP µ-bioreactor data and the suitability of this platform as a screening tool in upstream development of microbial systems. Fast, reliable, and transferable screening data significantly reduce experiments in fully controlled bioreactor systems and accelerate process development at lower cost.

Process adapted calibration improves fluorometric pH sensor precision in sophisticated fermentation processes.

Miniaturization and automation have become increasingly popular in bioprocess development in recent years, enabling rapid high-throughput screening and optimization of process conditions. In addition, advances in the bioprocessing industry have led to increasingly complex process designs, such as pH and temperature shifts, in microbial fed-batch fermentations for optimal soluble protein expression in a range of hosts. However, in order to develop an accurate scale-down model for bioprocess screening and optimization, small-scale bioreactors must be able to accurately reproduce these complex process designs. Monitoring methods, such as fluorometric-based pH sensors, provide elegant solutions for the miniaturization of bioreactors, however, previous research suggests that the intrinsic fluorescence of biomass alters the sigmoidal calibration curve of fluorometric pH sensors, leading to inaccurate pH control. In this article, we present results investigating the impact of biomass on the accuracy of a commercially available fluorometric pH sensor. Subsequently, we present our calibration methodology for more precise online measurement and provide recommendations for improved pH control in sophisticated fermentation processes.

Escherichia coli σ70 promoters allow expression rate control at the cellular level in genome-integrated expression systems.

BACKGROUND: The genome-integrated T7 expression system offers significant advantages, in terms of productivity and product quality, even when expressing the gene of interest (GOI) from a single copy. Compared to plasmid-based expression systems, this system does not incur a plasmid-mediated metabolic load, and it does not vary the dosage of the GOI during the production process. However, long-term production with T7 expression system leads to a rapidly growing non-producing population, because the T7 RNA polymerase (RNAP) is prone to mutations. The present study aimed to investigate whether two σ70 promoters, which were recognized by the Escherichia coli host RNAP, might be suitable in genome-integrated expression systems. We applied a promoter engineering strategy that allowed control of expressing the model protein, GFP, by introducing lac operators (lacO) into the constitutive T5 and A1 promoter sequences. RESULTS: We showed that, in genome-integrated E. coli expression systems that used σ70 promoters, the number of lacO sites must be well balanced. Promoters containing three and two lacO sites exhibited low basal expression, but resulted in a complete stop in recombinant protein production in partially induced cultures. In contrast, expression systems regulated by a single lacO site and the lac repressor element, lacIQ, on the same chromosome caused very low basal expression, were highly efficient in recombinant protein production, and enables fine-tuning of gene expression levels on a cellular level. CONCLUSIONS: Based on our results, we hypothesized that this phenomenon was associated with the autoregulation of the lac repressor protein, LacI. We reasoned that the affinity of LacI for the lacO sites of the GOI must be lower than the affinity of LacI to the lacO sites of the endogenous lac operon; otherwise, LacI autoregulation could not take place, and the lack of LacI autoregulation would lead to a disturbance in lac repressor-mediated regulation of transcription. By exploiting the mechanism of LacI autoregulation, we created a novel E. coli expression system for use in recombinant protein production, synthetic biology, and metabolic engineering applications.

The Effects of Lactose Induction on a Plasmid-Free E. coli T7 Expression System.

Recombinant production of pharmaceutical proteins like antigen binding fragments (Fabs) in the commonly-used production host Escherichia coli presents several challenges. The predominantly-used plasmid-based expression systems exhibit the drawback of either excessive plasmid amplification or plasmid loss over prolonged cultivations. To improve production, efforts are made to establish plasmid-free expression, ensuring more stable process conditions. Another strategy to stabilize production processes is lactose induction, leading to increased soluble product formation and cell fitness, as shown in several studies performed with plasmid-based expression systems. Within this study we wanted to investigate lactose induction for a strain with a genome-integrated gene of interest for the first time. We found unusually high specific lactose uptake rates, which we could attribute to the low levels of lac-repressor protein that is usually encoded not only on the genome but additionally on pET plasmids. We further show that these unusually high lactose uptake rates are toxic to the cells, leading to increased cell leakiness and lysis. Finally, we demonstrate that in contrast to plasmid-based T7 expression systems, IPTG induction is beneficial for genome-integrated T7 expression systems concerning cell fitness and productivity.

Implementation of a Fully Automated Microbial Cultivation Platform for Strain and Process Screening.

Advances in molecular biotechnology have resulted in the generation of numerous potential production strains. Because every strain can be screened under various process conditions, the number of potential cultivations is multiplied. Exploiting this potential without increasing the associated timelines requires a cultivation platform that offers increased throughput and flexibility to perform various bioprocess screening protocols. Currently, there is no commercially available fully automated cultivation platform that can operate multiple microbial fed-batch processes, including at-line sampling, deep freezer off-line sample storage, and complete data handling. To enable scalable high-throughput early-stage microbial bioprocess development, a commercially available microbioreactor system and a laboratory robot are combined to develop a fully automated cultivation platform. By making numerous modifications, as well as supplementation with custom-built hardware and software, fully automated milliliter-scale microbial fed-batch cultivation, sample handling, and data storage are realized. The initial results of cultivations with two different expression systems and three different process conditions are compared using 5 L scale benchmark cultivations, which provide identical rankings of expression systems and process conditions. Thus, fully automated high-throughput cultivation, including automated centralized data storage to significantly accelerate the identification of the optimal expression systems and process conditions, offers the potential for automated early-stage bioprocess development.

Integrated Process Modeling-A Process Validation Life Cycle Companion.

During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical process development and production, where multiple unit operations are stacked together and interact with each other. Therefore, we want to present the application of Monte Carlo (MC) simulation using an integrated process model (IPM) that enables estimation of process capability even in early stages of process validation. Once the IPM is established, its capability in risk and criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic production control strategies that take interactions of process parameters of multiple unit operations into account. Moreover, IPMs can be trained with development data, refined with qualification runs, and maintained with routine manufacturing data which underlines the lifecycle concept. These applications will be shown by means of a process characterization study recently conducted at a world-leading contract manufacturing organization (CMO). The new IPM methodology therefore allows anticipation of out of specification (OOS) events, identify critical process parameters, and take risk-based decisions on counteractions that increase process robustness and decrease the likelihood of OOS events.

Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1.

Identification of critical process parameters that impact product quality is a central task during regulatory requested process validation. Commonly, this is done via design of experiments and identification of parameters significantly impacting product quality (rejection of the null hypothesis that the effect equals 0). However, parameters which show a large uncertainty and might result in an undesirable product quality limit critical to the product, may be missed. This might occur during the evaluation of experiments since residual/un-modelled variance in the experiments is larger than expected a priori. Estimation of such a risk is the task of the presented novel retrospective power analysis permutation test. This is evaluated using a data set for two unit operations established during characterization of a biopharmaceutical process in industry. The results show that, for one unit operation, the observed variance in the experiments is much larger than expected a priori, resulting in low power levels for all non-significant parameters. Moreover, we present a workflow of how to mitigate the risk associated with overlooked parameter effects. This enables a statistically sound identification of critical process parameters. The developed workflow will substantially support industry in delivering constant product quality, reduce process variance and increase patient safety.