As technology progresses, it opens up new opportunities for streamlining processes to drive increased reliability and operational excellence. This trend is as true in the life sciences as it is in any other process manufacturing industry. In fact, it might be more relevant, as more and more life sciences manufacturers are rolling out born-digital facilities, leveraging new technologies to capture innovation as quickly as possible.
However, as Michalle Adkins shares in her recent collaboration in Biopharm International, technology can be a double-edged sword. It can bring great value, as long as it does not also add new complexity.
“If new technologies add uncertainty or additional steps, they’re unlikely to be widely adopted. Historically, that has been a challenge, but we’re starting to get over those complications as the technologies themselves mature. Today’s solutions are far more robust and designed for high-performance environments.”
In-line analysis
One area where innovation has recently driven significant improvements is in quality. Process analytic technology (PAT) can do wonders for speeding the treatment development process. However,
“Historically, PAT has been used to define processes and build control strategies, but its integration into manufacturing has often been limited.”
Consequently, one of the key technologies that is driving innovation today is DeltaV™ Spectral PAT. DeltaV Spectral PAT software runs chemometric models directly in the control system, allowing real-time prediction of critical quality attributes. This eliminates the need for product to sit in work-in-progress for days, weeks, or even months, waiting on lab testing for quality validation, ultimately helping unlock fully-automated manufacturing and driving improved speed to market.
More acceptance of AI
It’s hard to explore new technologies in manufacturing without talking about AI. Today, AI software is everywhere, and the life sciences industry is no exception. However, validation and regulation continue to be hurdles to adoption. Those barriers, however, are unlikely to last forever. Michalle explains,
“Regulatory bodies are increasingly open to and encouraging the use of these more automated approaches, particularly as companies are demonstrating success in both process development and targeted projects. With early successes demonstrated in bulk drug substance processes such as fermentation and purification, along with pilot-scale manufacturing, broader adoption is expected across the manufacturing lifecycle.”
That means teams are going to need to start preparing for AI as they look toward the future. One way they can start doing so is by considering how the organization handles data. AI software consumes a lot of data to do its job, but it’s not only the data itself that is important, but also the context.
“One of the most critical—and often underestimated—elements of effective data analytics in pharma manufacturing is context. Teams must be empowered to generate and preserve context as data flows across the enterprise. Whenever context needs to be reconstructed, the value of that data diminishes. It becomes harder to use, more expensive to manage, and slower for driving meaningful action. That’s not just a technical challenge—it’s a strategic one.”
The solution is an automation environment built around software that delivers seamless data mobility from the intelligent field through the edge and into the cloud like the Enterprise Operations Platform (EOP). The EOP is engineered to deliver seamless integration across the enterprise not just to move data, but to preserve its context at every stage to make that data more valuable to the people and applications that consume it. The EOP will create,
“a common data platform that distributes consistent equipment information across all solutions via a shared data fabric. This not only simplifies data management but also strengthens the foundation for more autonomous and scalable operations. This approach enables scalable, AI-ready data environments that support both operational efficiency and innovation.”
The next generation automation system delivered via the EOP will fundamentally shift the way life sciences manufacturers approach their automation strategy. In fact, one of the key benefits of the EOP is that elements available today bring significant operational improvements while simultaneously preparing organizations for easier implementation of emerging technologies.
The next generation of control is just over the horizon, and life sciences manufacturers can start laying the foundation today.
