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.

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.

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.

Escherichia coli σ 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 Effects of Lactose Induction on a Plasmid-Free T7 Expression System.

Recombinant production of pharmaceutical proteins like antigen binding fragments (Fabs) in the commonly-used production host 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.

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.

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.

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.