Information technology helps integrate Quality by Design and process analytical technology into standard laboratory procedures
and increase efficiency in process and product development. This article presents two case studies in which applying novel
information technologies for an advanced bioreactor system improved process development. In the first study, users of a four-fold
bioreactor system achieved a quick way to the optimized process and increased product yield nearly 10-fold. In the second
study, seamless integration of analytical data allowed for implementation of predictive model control and comprehensive process
automation. Along with the increased product quality, the number of experiments was significantly reduced.
In a market where a laboratory must be extremely cost-efficient and judicious with its time, laboratory members are always
seeking research tools that will give them the edge they need for market leadership. Additionally, regulatory requirements
set through the US Food and Drug Administration have recently become much more extensive. FDA initiatives such as Quality
by Design (QbD) and process analytical technology (PAT) have proved to be an additional obstacle to overcome for some. However,
these initiatives have driven other companies to speed up their process development. These players thus benefit from the FDA's
efforts to strengthen product quality control.
Following the QbD approach, the FDA promotes PAT as a system for designing, analyzing, and controlling manufacturing as a
means of ensuring final product quality and consistency. The PAT concept demands that product quality can be achieved by design
through a comprehensive understanding of the product and process risks, and with knowledge of how best to mitigate those risks.
The benefits of these systems include improved product quality and efficiency, reduced production costs, and prevention of
rejects and reprocessing. These improvements assure that the quality of the product is consistent through processes that are
thoroughly developed and documented. Finally, a significant amount of labor and time can be saved and operator safety is increased
because of the increased automation during the manufacturing process.
Because of these new requirements, the amount of data generated has grown immensely. With this increase in data, bench-top
bioreactor control systems that integrate new technological approaches to gather, document, and manage large amounts of data
have become crucial for successful process development.
The following two case studies demonstrate how cell culture and fermentation specialists meet key challenges in today's process
development laboratories by applying information technologies of an advanced bioreactor system.
Richter Helm Biologics
Consistent Parallel Processing
When developing bioprocesses using a systematic parallel approach keeps time, materials and effort to a minimum while providing
reproducible data, it optimizes results.
During the development of an upstream drug substance process using Escherichia coli, researchers at Richter-Helm Biologics carried out extensive parameter screening using a four-vessel parallel approach. This
parallel approach led to a significant increase in productivity yields at the 1-L scale. The results were obtained with a
recombinant E. coli BL21 strain that carried a heat-inducible expression plasmid constructed by Richter-Helm Biologics. The target protein involved
a human active pharmaceutical ingredient (API) that is primarily expressed in inclusion bodies (IB) as a fusion protein.
In the initial step, a three-phase process was developed; the relevant parameters were automatically controlled. The DASGIP
Control 4.0 software gathered and visualized the data during the cultivation process and stored it with the offline data in
a central database. Richter-Helm Biologics used a generic fed-batch cultivation strategy as a starting point for the development
of the upstream process.
In the second step, researchers examined the key parameters for target protein expression as described in relevant literature
(Table 1). The influence of various parameters on protein expression was compared in a total of 26 cultivations at 1-L scale.
The studied parameters are highlighted in gray in the table. The DASGIP data management system supported and simplified the
analysis and interpretation of the process-relevant information.
Table 1. Key parameters that influence target protein expression