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Automation and digitalization work together in the digital plant.
Automation—using machines rather than people to perform tasks—offers benefits to biopharmaceutical manufacturers in multiple facets of plant operation, such as transporting materials, taking samples, and controlling the process. Digitalization—replacing manual records with digital ones and using data in a variety of digital tools and connected systems—works synergistically with automated processes. Data collected from equipment that is connected via the industrial Internet of things (IIoT) and analyzed with artificial intelligence (AI), for example, allow the control system to know when a process is trending out of specification, or when a piece of equipment needs maintenance. The digital or “intelligent” plant makes use of both automation and digitalization.
“With the advance of data science enabling factors such as easy access to scalable memory and computing resources; our growing competence in collecting, storing, and contextualizing data; advances in robotics; [and] the quickly evolving method landscape driven by the open-source community, the benefits of automation and simulation are becoming accessible in the notoriously complicated realm of biopharma manufacturing,” says Marcel von der Haar, head of product strategy data analytics at Sartorius.
Business benefits of the digital plant include getting new products to market faster; faster changeover of products, which enables flexible and multi-product manufacturing; improved operational integrity, which includes predicting and correcting potential problems before they occur; and real-time release, which makes production faster and minimizes inventory, says Nathan Pettus, president of process systems and solutions at Emerson. Digital tools accelerate the pipeline and help commercialize products faster by easing technology transfer. “For example, cloud-enabled knowledge management software that manages product and process specifications through the entire drug development lifecycle is enabling biopharma manufacturers to minimize errors in the tech transfer process,” says Pettus.
Automation leads to improved product quality and compliance, reduced risk, and improved productivity, says Heather Coglaiti, global industry strategy and marketing lead for life sciences, Rockwell Automation. An increase in right-first-time occurrences leads to fewer deviations and investigations. Predictive or adaptive mechanisms to prevent equipment failure, as well as real-time asset management, help reduce operational risk, she explains.
“Automation, even partially digitized, reduces the human errors associated with batch-to-batch variability,” adds Dennis Brandl, BRL Consulting. For example, automated dispensing systems have less variability than manual dispensing. Automation also eliminates human error associated with manual data entry.
Automation provides consistency in performance, quality, and process, agrees Dushyant Arora, life sciences industry manager for Siemens Pharma USA. He explains that automation and digitalization enable online quality testing and review-by-exception, which can enable real-time release.
Adoption of these technologies in biopharma is driven in part by the ongoing push for speed-to-market and the trend to flexible and multi-product manufacturing. Digital technologies are also a crucial part of continuous processing.
“As life sciences plants have moved away from manufacturing a single product and are focused more on developing a wider range of specialized treatments, the importance of flexible manufacturing has taken center stage,” explains Pettus. “Plug-and-play” capabilities of automation systems, which enable flexible manufacturing and faster technology transfer, are more important than ever, he says.
For scaled-down and distributed systems that use multiple, identical manufacturing lines located in one facility or at multiple sites, the ability to collect and share information across the enterprise is crucial, adds Pettus. “Today’s automation platforms leverage scalable ‘data aggregators,’ often based on software-as-a-service models and cloud-architectures. [This ability to scale allows] companies to use automation from the beginning, develop the right process, and then scale the solution as they build their business and infrastructure globally,” he explains. Pettus concludes, “At any scale, there is too much at stake to dismiss automation and digitalization, as they are truly critical pieces of the quality puzzle.”
Intensified and continuous processes rely on real-time testing for process control. “We are focusing our efforts for the ‘factory of the future’ on process intensification and connecting unit operations together, which is enabled by single-use technology, automation, and process analytical technology (PAT),” says Darren Verlenden, head of bioprocessing at MilliporeSigma, the US and Canada life science business of Merck KGaA.
MilliporeSigma’s good manufacturing practice (GMP) PAT platform for bioprocessing includes a Raman spectroscopy analyzer and software that can achieve in-line, real-time monitoring of process parameters and quality attributes. These data can be used to automate control of the bioreactor. In April 2022, the company acquired Lonza’s MAST platform, an automated, sterile bioreactor sampling system; upon closing, it will become part of MilliporeSigma’s BioContinuum platform. The GMP MAST system will help processors evolve from manual sampling to automated sampling, says Verlenden.
Digital transformation is an ongoing journey, says Verlenden. He adds that some customers may want to run a fully continuous, automated process, while others want to intensify and automate certain unit operations.
The digital biopharmaceutical manufacturing plant is the “facility of the future,” but it is also a reality today. Sanofi’s digital and paperless facility in Framingham, Mass., for example, won the 2020 overall Facility of the Year Award (FOYA) from the International Society for Pharmaceutical Engineering (ISPE). The company says that the manufacturing facility is 80 times more productive than a traditional facility and has a smaller environmental footprint (1). ISPE’s 2022 FOYA category award for innovation went to another digital plant located in Framingham, Mass.: CRISPR Therapeutics’ facility, which uses digital systems and automation for production and filling operations (2). Digital tools allow this facility to manage multiple production suites and products at different stages of development (2). Takeda Pharmaceuticals International’s vaccine facility in Singen, Germany won ISPE’s 2022 Pharma 4.0 category award for its use of digital technologies, which include an electronic batch record system, an embedded warehouse management system, and autonomous mobile robots to transport materials (3). ISPE’s Honorable Mention-award winner, Iovance Biotherapeutics’ autologous cell therapy facility in Philadelphia, uses digital tools, including electronic batch records and centralized equipment monitoring (4).
Walvax Biotech’s new COVID-19 messenger RNA (mRNA) vaccine plant in China is another example of an intelligent and digital plant; it uses Honeywell’s batch process control, building and energy management solution systems, and digital twins to monitor assets (5).
“As the need for speed and agility increases, biopharma production facilities need some way to better visualize and integrate batch processing across various equipment and systems,” says Shawn Opatka, vice-president and general manager, life sciences at Honeywell. He explains that the data capture, recording, and reporting systems used by Walvax will help maintain auditability as well as optimize production. Opatka says that data from the live process will be sent to the digital twin, where operators can proactively watch for problems and fix them before they occur.
“The application of AI is paramount and will continue to grow as many organizations progress in their digitalization journey,” says Opatka. “We see more biopharma customers interested in having plants with planning and production executed automatically and using advanced analytics.”
Using AI in predictive analytics to anticipate manufacturing problems—the predictive plant—is the next level of digital maturity after a connected plant, says Yvonne Duckworth, Industry 4.0 specialist, director of digital technology, and senior automation engineer at CRB. “Predictive analytics can reduce downtime and promote quality,” says Duckworth. She points to a real-life example of a vibrating agitator that unthreaded itself before the operators noticed; if a vibration sensor had been attached to the agitator, the control system would have flagged the vibration as a concern before it could have been seen by a human eye. “Some equipment vendors don’t yet offer this capability; some have vibration or temperature sensors available as options, and others offer a fully integrated predictive analytics platform,” says Duckworth. “Vendors have been challenged over the past year to have an answer to the question of predictive analytics capability.”
Another step forward on the digital journey is the use of digital twins, which are digital models of a physical system. Brandl says that digital twins have been used for the past 30 years in process industries where the chemistry of the processes is well understood. Only recently, however, enabled by AI and data availability, have digital twins been developed for biological processes, where there is no ‘first principle’ development possible, he says. “Automation brings in the data for machine learning to model the dynamic processes of cell growth and map it against the multiple dimensions provided by advanced sensors,” explains Brandl.
Digital twins are being used today in biopharma process development to optimize the process in a simulated environment. They can also be used for collaborating in real-time across the value chain and validating new lines and processes virtually, says Coglaiti.
“We will likely see biopharma manufacturers moving from using the digital twin primarily as a simulation and training component to making it a key part of the overall dynamic control of the process,” suggests Pettus. “Manufacturing models are becoming more well defined and well understood, and measurements that were virtually impossible to understand in the past are now enabled with various technologies such as spectral-based techniques. As the industry becomes more comfortable with this more complex, closed-loop control and includes the digital process response predictions with advanced control techniques, we will see the digital twin become even more relevant than it already is today.”
“Today, digital process twins based on statistical models, such as multivariate statistical process control, are increasingly widely applied to obtain predictive and even prescriptive insights into biological process development as well as GMP manufacturing,” adds Mark Demesmaeker, head of data analytics at Sartorius. “We are now moving toward the next step, which will combine data-driven methods and mechanistic concepts—such as growth kinetics and specific metabolite consumption rates in upstream processing, and fluid dynamics or neural networks in downstream processing—to obtain enhanced observability of highly complex cell culture processes or separation and purification steps. This will allow the industry to advance to a closed-loop model predictive control strategy in the coming years.”
Experts agree that there are some regulatory challenges to the concept of applying AI-based models to closed-loop controls. “The use of AI, or even machine learning in a regulated environment, still needs a more elaborated validation framework to manifest [the] great promise [of these technologies],” says Dirk Wollaert, digitalization lead at Siemens Pharma headquarters.
Pettus says that Emerson and others are working together in the BioPhorum consortium to develop strategies
to meet this challenge. “We are optimistic that a path forward for these remaining regulatory hurdles is already underway, particularly as we continue to work with BioPhorum, other similar organizations, and companies that are already engaging regulatory bodies around the world,” he explains.
Despite these many potential benefits, one of the obstacles to automation is the difficulty of calculating the return on investment of a project. Identifying the value is key, says Verlenden.
Duckworth agrees that cost can be a barrier. “Identifying the problem you’re trying to solve makes it easier to identify the savings,” she explains. Some are concerned about the cost and effort of revalidating an existing process, she adds.
“It is important for companies putting in a new facility to think about what approach they want to take from a digital technologies perspective,” adds Duckworth. “A lighthouse approach incorporates all the new technologies at the start. A phased approach, however, can be more beneficial if there are limited resources or a need for speed to market. In this case, we can design a robust network infrastructure which will allow for more advanced digital technologies to be added in later phases.”
Development-stage biotech companies that are moving to commercial manufacturing may not be as familiar with automation and the requirements for using computer systems in GMP environments, such as computer system validation, says Michel Claes, biopharma portfolio lead at Siemens. System integrators can provide guidance, he suggests.
In the past, connecting different types of equipment was a big challenge for automating bioprocesses, but this difficulty has begun to be addressed by modular systems and open-source module type package (MTP) communication protocols that standardize the connections.
“With MTP, organizations can easily add new equipment without having to worry about compatibility and need not spend weeks or months integrating equipment when moving from bench-scale to production-scale,” says Pettus.
Standard interfaces make it easier to integrate PAT sensors into automation systems, says Brandl. “Adding a new sensor for pH, oxygen, or carbon dioxide, for example, can be done at 10% of the cost that it was a few years ago,” he says. Another key development provides the ability to port automation logic across platforms, which is important for biopharma companies that have production in different parts of the world, says Brandl. “PLCOpen has provided the ability to use the same machine control strategies in different facilities using locally supported automation systems,” he explains.
Industry members have been working with the BioPhorum consortium to further pave the way for plug-and-play automation. A current issue is a lack of standardization for data integrity requirements, such as audit trails. In a 2021 paper, BioPhorum proposed a harmonized audit trail model, which is intended to stimulate development of a formal international standard (6).
Maintaining data integrity faces multiple challenges. “Organizations must centralize their data because traditional on-premises historians often lack the metadata and contextualization required to uphold data integrity,” suggests Petter Mörée, managing director for EMEA, Seeq. In addition, he says, data must be reliably stored and managed and available throughout its lifecycle.
Having the right workforce with the suitable skills necessary to incorporate and maintain digital technologies can be another challenge, says Duckworth. Recognition of the need for workers to have digital skills is part of the European Commission’s “Industry 5.0” vision, which seeks a human-centric approach to digital technologies (7) and incorporates sustainability into the way industries are moving forward.
Technology is driving rapid and significant change in biopharmaceutical manufacturing, with more to come, experts suggest.
“Most industry players have merely scratched the surface of what could become a fully automated and digitally twinned manufacturing process,” says von der Haar. “At Sartorius, we are convinced that the companies that adopt and master these methods will disrupt and guide the biopharmaceutical industry in the coming years, generating benefits ranging from drastically reduced time-to-market, lower process variability and failure rates, [and] resource efficiency gains.”
“Real time release, one-click tech transfer, autonomous plants, and self-adjusting processes are going to be important in our future,” says Pettus. “I would be very surprised if these four areas are not significantly developed and advanced in the coming three to five years. The life science industry is going to impact all of humankind over the coming two decades in ways similar to how the semiconductor industry has over the past 50 years.”
1. Sanofi, “Factory of the Future,” Sanofi.com (April 8, 2020).
2. ISPE, “2022 Category Winner for Innovation,” ispe.org (April 26, 2022).
3. ISPE, “2022 Category Winner for Pharma 4.0,” ispe.org (April 26, 2022).
4. ISPE, “2022 Honorable Mention Winner,” ispe.org (April 26, 2022).
5. Honeywell, “Honeywell Collaborates With Walvax Biotech To Automate China’s First Digital mRNA Covid-19 Vaccine Plant,” Press Release, Feb. 1, 2022.
6. Biophorum, “On the Plug-and-Play Audit Trail to Connect Intelligent Pieces of Equipment,” Press Release, July 22, 2021.
7. EC, “Industry 5.0,” https://ec.europa.eu/info/research-and-innovation/research-area/industrial-research-and-innovation/industry-50, accessed May 19, 2022.
Jennifer Markarian is manufacturing reporter for BioPharm International.
Vol. 35, No. 7
When referring to this article, please cite it as J. Markarian, “Automating Biopharma Manufacturing,” BioPharm International 35 (7) 10-13 (2022).