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The bioprocessing industry is undergoing a significant transformation as digitalization becomes increasingly integrated into workflows. Technologies such as cloud computing, artificial intelligence (AI) and automation are offering new ways to optimize processes, enhance scalability and ensure consistent product quality.
To explore how digitalization is transforming upstream bioprocessing, Technology Networks recently spoke with Simon Wieninger, business manager of software & digital solutions bioprocess at Eppendorf SE Bioprocess Center. In this interview, Simon discusses the critical role digital tools play in improving bioprocessing efficiency, how cloud technologies and data harmonization can overcome key challenges and the importance of maintaining robust data security.
Anna MacDonald (AM):
Senior Science Editor
Technology Networks
Anna is a senior science editor at Technology Networks. She holds a first-class honors degree in biological sciences from the University of East Anglia. Before joining Technology Networks she helped organize scientific conferences.
Digital solutions, such as cloud computing and AI, have been discussed in the context of Industry 4.0 for quite some time. What is their relevance in upstream bioprocessing, and will their importance increase in the future?
Business Manager Software & Digital Solutions
Eppendorf
Creating digital solutions to empower scientists and biotech companies to make the most of their lab work. This is the core goal Simon pursues as the business manager for bioprocess at Eppendorf.
A particularly transformative development in the bioprocess industry is the rise of cell and gene therapies (CGT). Digitalization has already dramatically changed the way researchers work in bioprocess development in general, but in the context of CGT, digital technologies offer especially significant improvements. Why? Because in cell therapy, the cell itself is the product. Therefore, it is crucial to focus on the control, efficiency and predictability of the process, as the growth environment influences the characteristics and behavior of the cells. Even more than in bioprocesses for the production of proteins, peptides and small molecules, consistent growth environments are essential to achieve a cell therapy product of consistent quality.
The challenge and solution alike lie in the wealth of information scientists gather, including data on bioprocess parameters, cell growth and cell characteristics. To manage this complexity and leverage the data, companies must take advantage of digital products and services.
Senior Science Editor
Technology Networks
Anna is a senior science editor at Technology Networks. She holds a first-class honors degree in biological sciences from the University of East Anglia. Before joining Technology Networks she helped organize scientific conferences.
Can you give some concrete examples of how digital technologies can help improve upstream bioprocessing?
SSW:
Business Manager Software & Digital Solutions
Eppendorf
Creating digital solutions to empower scientists and biotech companies to make the most of their lab work. This is the core goal Simon pursues as the business manager for bioprocess at Eppendorf.
The improvement of upstream bioprocessing is based on data and will therefore benefit from advanced digital technologies. There are many examples of how bioprocess data is used for process control and optimization; let’s have a look at a few of them.
It is crucial to gather data from the bioprocess to identify the critical process parameters that affect product yield and quality. Related to that, data is required to establish an optimal growth environment for cells, such as maintaining glucose levels that are optimal for cell growth and viability. Gathering real-time data about the glucose concentration in the culture medium and transferring it to the bioprocess control software enables the implementation of automated feedback loops to maintain a constant glucose concentration.
When we consider ways to enhance the efficiency of bioprocess development, the use of data plays a pivotal role. For instance, bioprocess data serves as the foundation for developing in silico models, which enable researchers to simulate cell growth and product formation. By leveraging these models, the number of wet lab experiments needed for bioprocess optimization can be reduced, ultimately accelerating process development.
The amount and complexity of data represent a significant challenge in upstream bioprocessing. In a bioprocess laboratory, it is common to encounter disparate data sets from a variety of devices and vendors. During cell cultivation, parameters such as temperature, pH and nutrient concentration are monitored using integrated sensors and external analytical devices. Additionally, data is collected on the operation of actuators utilized to regulate these parameters, such as the agitation speed, gas flow rate and pump activity. Finally, data is gathered on the process output, including cell number and viability, marker protein expression and byproduct formation in the culture medium, using a range of analytical devices. One bioprocess run can produce as many as 200 data tracks, making it complex to draw the right conclusions. To make the most of this data, it is essential to have it available in a usable format, meaning it needs to be harmonized for easier integration and analysis.
Two digital technologies are particularly useful in this context: Cloud applications and communication standards like OPC UA (Open Platform Communication Unified Architecture). OPC UA facilitates communication between devices from different vendors, enabling integration and automated feedback loops for bioprocess control. To illustrate, OPC communication allows for the integration of a sensor from vendor A with the bioprocess control software from vendor B. This integration enables the sensor readings to be utilized by the software and automated feedback loops for bioprocess control to be implemented. Cloud solutions provide the infrastructure for data harmonization, allowing data from different runs and controllers, as well as external analyzers, to be available in one place that can be accessed from everywhere.
Imagine how much easier it would be to analyze, contextualize and collaborate on data without juggling multiple Excel files, calculations and different analysis software tools. Furthermore, it paves the path for better use of data for deeper process insights based on advanced process modeling and AI applications.
Senior Science Editor
Technology Networks
Anna is a senior science editor at Technology Networks. She holds a first-class honors degree in biological sciences from the University of East Anglia. Before joining Technology Networks she helped organize scientific conferences.
Many digital solutions require transferring proprietary bioprocess data into the cloud. What are your thoughts regarding data security?
SSW:
Business Manager Software & Digital Solutions
Eppendorf
Creating digital solutions to empower scientists and biotech companies to make the most of their lab work. This is the core goal Simon pursues as the business manager for bioprocess at Eppendorf.
Data security is a fundamental aspect when using cloud technologies. We benefit from the fact that cloud technologies developed in other industries have well-established security standards that we can use for our solutions. For example, the cloud-based software solution BioNsight® cloud from Eppendorf, which facilitates the storing, analysis and contextualizing of bioprocess data from different runs and devices in one central place, is hosted and developed on Microsoft’s SOC2 and ISO 27001 certified Azure® cloud service.
Our software development team follows a comprehensive, secure development lifecycle covering all phases of engineering. Security is an important pillar throughout the development process. We can find numerous examples in the biopharmaceutical industry where cloud-based software solutions are being used effectively, for example for managing clinical trial data. In my view, their widespread adoption demonstrates that data security can be handled effectively in today's digital landscape.
Senior Science Editor
Technology Networks
Anna is a senior science editor at Technology Networks. She holds a first-class honors degree in biological sciences from the University of East Anglia. Before joining Technology Networks she helped organize scientific conferences.
How do you think digital technologies in bioprocessing will develop in the coming years?
SSW:
Business Manager Software & Digital Solutions
Eppendorf
Creating digital solutions to empower scientists and biotech companies to make the most of their lab work. This is the core goal Simon pursues as the business manager for bioprocess at Eppendorf.
Cloud solutions are an enabling technology that will help make better use of modeling and AI applications for bioprocess development. In the coming years, I expect digital technologies currently emerging to become standard in the lab. One driver for the advancement of cloud technologies is their widespread use in daily and professional lives, which we can now leverage for the bioprocess industry. I believe the same will happen with other technologies, like AI. While control of parameters such as temperature, pH and culture feeding are well established, there is a critical need for advanced operational decision-making to increase throughput and efficiency of upstream bioprocessing. Using AI technology to identify optimal experimental setups can greatly accelerate the acquisition of valuable knowledge about cultivation processes.
In general, faster adoption of state-of-the-art digital technologies will lead us to much-needed improvements in data usage. This will help gain deeper process insights, improve collaboration among scientists, institutions, pharmaceutical companies and suppliers and ultimately contribute to improving human living conditions.
