It’s one thing to think about a transition to Bioprocessing 4.0, and quite another to make it happen. Any such transition is a systems-level project—which is not to say that it involves integrating multiple systems into a functional whole. Existing bioprocessing systems do that already. Rather, transitioning to Bioprocessing 4.0 is about engineering a more tightly integrated whole, one that is responsive, flexible, and even self-optimizing.
It may sound as though a company working at the Bioprocessing 4.0 level would have to emulate a complex living organism. It would need to sense, remember, and learn—and modify its behavior accordingly. That’s not far from the truth.
If Bioprocessing 4.0 has a hallmark, it is the presence of feedback loops in which instrument-embedded sensors transmit environmental signals to computation platforms, and computation platforms respond to the signals by sending instructions back to instruments—all in real time. Of course, Bioprocessing 4.0 isn’t quite so tidy. Unlike an autonomous living organism, Bioprocessing 4.0 is extracorporeal. Instead of relying on a brain, for example, it may take its computations to the cloud. Also, other entities—including human bodies and brains—are still needed.
Bioprocessing 4.0, then, has a social dimension, which is to say, it thrives on connections. These connections span data, instruments, platforms, people, and more. Forging these connections and making them work is a difficult task. Fortunately, the task attracts helpers, specialists of the sort that appear in this article.
Where to start
To advance toward Bioprocessing 4.0, a biomanufacturer needs an objective assessment of its starting point. Biomanufacturers attempting such assessments may seek assistance from specialists. For example, the specialists at Sartorius Stedim Biotech can help a biomanufacturer obtain a holistic view of a bioprocess, a view that encompasses all unit operations, upstream and downstream.
“We have started to work on improving the mechanistic understanding of the underlying kinetics of advanced biotransformations, as well as protein separations and purification,” says Mark Demesmaeker, PhD, head of data analytics at Sartorius. “We will launch a new hybrid modeling platform this year that enables pairing multivariate modeling of critical process parameters with online parameter estimations of, for example, cellular metabolic fluxes. Such chemometric models can be enhanced by extracting data from multidimensional spectra—for example, from Raman spectroscopy—into metabolite concentration estimates.”
Ultimately, this work will enable a more robust process. Specific benefits that Demesmaeker hopes for include shortened process development cycles, increased product yields, and improved efficiency in downstream procedures.
Even more lies ahead for technology from Sartorius, suggests Artur Miguel Arsenio, PhD, a head of product management at Sartorius. The company’s process analytical technology select PAT union he points out, can already “collect inline data for advanced process-control strategies.”
“We’re also working toward further evolving [these] controls and analytics,” he continues, “by unlocking the full potential of Raman spectroscopy on the high-throughput ambr multiparallel bioreactor systems.” This improvement is coming with the launch of a new spectroscopy platform. The platform, Aresenio notes, can be integrated into bioprocessing lines.
Keeping better track of a process enables process optimization. “PAT and software technologies allow us to accurately predict process parameters and apply automated process control strategies, improving both performance and product quality,” Arsenio explains.
Changes in the markets must also be considered. “The fast growth of biopharmaceutical markets, and evolving market needs such as personalized medicine or intensified processing, demand a significant increase of flexibility for production processes,” Arsenio says. “This requires modularization of process technology, instruments, and automation software. Smart, modular units with the new Sartorius automation platform will run prequalified and tested recipes, enabling simpler integration of manufacturers’ single-use technology in bioprocess operations.”
The need for engineering speed
Certainly, speed lies at the heart of Bioprocessing 4.0, and few know more about that than Robert Hariri, MD, PhD, founder and CEO of Celularity. Hariri is a jet-rated commercial pilot who has flown more than 60 military and civilian aircraft. He carries that need for speed to Celularity’s IMPACT select Immuno-Modulatory Placenta-derived Allogeneic Cell Therapy) platform. With this technology, Celularity needs only 24 months to take a new discovery to an Investigational New Drug select IND) application. Traditionally, that stage of drug development takes an average of 60 months.
To build a company that can develop drugs so quickly, it is necessary, Hariri says, to “control the design and engineering of the process.” Such control, he adds, “ultimately translates into the value in the product that companies are using to differentiate themselves from one another.” If you’re a biopharma and in the business of producing living cells as medicines, you must, Hariri says, understand the release specifications such medicines require. If you do so, he continues, you “can create the most streamlined, efficient procedure and protocols to get you there—it’s an engineering effort.”
A first in Framingham
In 2019, Sanofi opened a digitally enabled biomanufacturing facility in Framingham, MA. Franqui Jimenez, PhD, head of second-generation process development, global manufacturing science and technology at Sanofi, describes the facility as “one of the world’s first to use continuous biologics production technology.” He adds, “Bioprocessing 4.0 is digitally built into the multiproduct facility, which will manufacture biologics from our specialty care portfolio amongst others.”
This facility already provides Sanofi with big benefits. “The advanced, paperless, and data-driven manufacturing technologies enable achievement of higher levels of productivity, agility, and flexibility, which reduces the time needed for products to get from the development labs to the manufacturing plant,” Jimenez asserts. “The new way of processing results in significant reductions in energy usage, carbon dioxide emissions, and chemical and water usage versus traditional technologies.” In fact, Sanofi is enjoying up to an 80% reduction in energy usage and carbon dioxide emissions, and reductions of up to 90% or more of water and chemical usage.
Some of the advantages might not be as easy to quantify, but they improve a process. One such advantage, Jimenez suggests, is the development of an innovation mindset. “Combining the integrated continuous biomanufacturing platform with the digital aspects of the facility creates a powerful infrastructure,” he says, “and it fosters innovation and progress.”
Such a transformation of biomanufacturing doesn’t happen easily. “As with all significant changes or undertakings, logistical implementation hurdles are considerable, especially given the fact that there are few, if any, industry examples,” Jimenez says. Another challenge is the ongoing effort to change industry practices. Nonetheless, it is possible, Jimenez suggests, to transcend accepted practices and realize a vision beyond familiar boundaries.”
Sanofi’s U.S. Bioprocessing 4.0 facility is just a start. Jimenez says that it “paves the way for the transformation of our global industrial network.” Each year over the past five years, this network has benefitted from company investments of $1.11 billion. The company hopes that in the next three to five years, it will roll out its digital transformation around the world.
A bright idea
To improve processes that support research—processes such as workflow automation and data analysis—a biotech company’s laboratory may need to procure sophisticated instruments and other system components. It may also need to forge connections between components, thereby creating an integrated, functioning whole. For help with connections, laboratories may want to consult with MilliporeSigma, which recently introduced BrightLab, a cloud-based inventory management and instrument connectivity platform.
“This platform enables greater operational efficiency for bioprocessing teams by connecting lab equipment, sensors, and other devices to a central data lake for monitoring and automating workflows,” says Klaus-Reinhard Bischoff, head of research solutions at MilliporeSigma. “Rather than manually transcribing the results of an individual batch record onto paper, BrightLab instrument connectivity allows scientists and engineers to collect immense amounts of data throughout all points in the experimentation process—accurately and in real time.”
Although BrightLab is a powerful product, it is anything but intimidating. “The BrightLab platform’s core modules can be activated and set up with relative ease, including the electronic lab notebook, inventory and requests, and asset management modules,” Bischoff explains. “Most users are able to set these up on their own, although we do provide video tutorials, training documentation, and live technical support.”
For more advanced uses, most companies will need help. “To connect your lab equipment for data flow automation, we recommend a consultation,” Bischoff says. “MilliporeSigma’s software works with existing hardware, and our engineers have a variety of methods for cloud-enabling benchtop devices.” Plus, the BrightLab platform works with any data format.
For a user perspective, we talked with Cenk Sumen, PhD, chief technology officer at Stemson Therapeutics. “We have been using BrightLab to document experiments and provide a common repository of data for several experiment streams that lead to our final product,” he relates. “Additionally, as our small startup grows, we’ve been using the inventory features for streamlining the ordering process and for keeping continuity of products in the lab.”
Already, Sumen sees the value in using BrightLab. “The major benefit has been the increased organization and efficient workflow for capturing the details of our experiments,” he says. “Another major benefit has been the increased collaboration and accountability between group members—every member of the lab accessing the experimental reports and seeing the workflow helps us to be more efficient as a group.”
Building a team
As Sumen indicates, bioprocessing at an advanced level—approaching and realizing the Bioprocessing 4.0 concept—takes more than technology. It takes a high degree of coordination among people. Other experts in bioprocessing agree.
“A lot of thought needs to go into building the cross-functional, highly knowledgeable, skilled team that is required to successfully bring the concept to fruition,” says Jimenez.
Hariri agrees. To engineer a fast and efficient bioprocess that is well engineered and makes the most of today’s technology, it requires more than the right platforms. “You’ve got to have the right expertise on your team to understand what it takes to get from start to finish,” he says.
Not all companies take Hariri’s advice. Combining so many levels of expertise daunting. “That’s why most companies working in cellular therapy are relying on third parties,” Hariri notes. “Big third-party providers do it because the cellular therapy companies lack the internal expertise. But if you lack that expertise and end up relying on a third party, you don’t own what is perhaps the most proprietary part of the product.”
So, creating a strong basis in Bioprocessing 4.0 takes a skilled and experienced team. “We’ve always built expertise around the bioprocessing and manufacturing of our products,” Hariri stresses. “I’ve always felt that the process is as much the product as the cellular platform we start with.”
Optimizing Yield and Improving Overall Culture Performance with Metabolomics
Metabolomics, the study of small molecules select metabolites union illuminates active biology through comprehensive assessments of cellular biochemistry. In other words, it can generate actionable insights by analyzing data about metabolites such as amino acids, carbohydrates, energetic pathway intermediates, lipids, vitamins, and cofactors.
Brian Keppler, PhD
Director of Discovery and Translational Sciences, Metabolon
Metabolomics can elevate bioprocessing through the analysis of metabolite data streams. In fact, these data streams are critical to the Bioprocess 4.0 approach to biomanufacturing. They can be used to maintain process control and achieve optimal performance in bioprocessing applications.
To access high-quality data streams, biomanufacturers that embrace Bioprocess 4.0 need enabling technology, such as that provided by Metabolon in the areas of analytical chemistry, quality control, bioinformatics/machine learning, and expert biochemical interpretation.
Traditional bioprocess methods measure individual molecules such as lactate, glucose, and ammonia to monitor culture performance. Metabolon’s untargeted approach to metabolomics includes more than 5200 metabolites, setting the standard for coverage in the industry. This wide-angle view provides an extensive, broad, accurate, and informative assessment of biochemical pathways that are ideal inputs into advanced Bioprocess 4.0 methods, driving performance, predictability, and optimization of systems.
Untargeted metabolite profiling of both cell pellets and supernatants/media/feeds will increase process understanding and provide actionable biological data and insight within various areas of a bioprocess:
- Development—media/feed optimization, scale-up
- Improvement—variability reduction, production/quality optimization
- Characterization—cell line selection, batch/process fingerprinting
- Monitoring—assessment of performance/quality predictors
Metabolon’s technology, which offers comprehensive coverage and top-tier competence with respect to data quality, is applicable in many areas across the bioprocess lifecycle, and it can help reduce time-to-market and program expense. By seizing these advantages, biomanufacturers can improve the development, implementation, and execution of robust and reproducible bioprocesses.