From Retrospective Testing to Continuous Process Intelligence

As biopharmaceutical manufacturing continues to evolve, the industry's approach to process monitoring is also changing. Traditional analytical methods have long relied on collecting samples at specific time points, analyzing them offline, and using those results to guide manufacturing decisions. While this approach has supported the production of biologic therapies for decades, it can introduce delays between what is happening in a manufacturing process and when operators receive actionable information.

How will real-time analytics change biopharmaceutical manufacturing?

According to Bryan Hassell, founder and CEO of Nirrin Technologies, the future of biomanufacturing will depend on replacing these retrospective measurements with continuous process intelligence.

"I think we are moving from retrospective measurements toward more continuous process intelligence," Hassell said during an interview with BioPharm International®. "Historically, protein concentration has been measured at discrete points. A sample is collected, taken to an instrument, it's analyzed, and then that result is used to make a decision. That model has worked well, but it inherently creates delays in what's happening in the process and what operators actually know is happening in the process."

As manufacturers adopt greater automation and data-driven workflows, Hassell believes analytical measurements will increasingly become an integral part of process control rather than serving as standalone data points.

"As we move toward automation and data-driven decision making, manufacturers will increasingly expect this type of data to be available in real time and integrated directly into process control systems," he said. "Analytical instruments will become inputs to decision making rather than a tangential data set that's more isolated."

Supporting that transition, however, requires analytical technologies capable of operating reliably within manufacturing environments. Speed alone, Hassell noted, is not enough. The measurements themselves must also be trusted.

"If you're going to use an analytical instrument to guide a process, make a process decision, or automate anything, it has to be consistent. It has to be reliable," he said.

That consistency becomes increasingly important as manufacturers seek continuous visibility into both process parameters and critical product attributes. Rather than focusing solely on obtaining faster measurements, Hassell said the greater opportunity lies in generating an uninterrupted stream of reliable process data that supports better operational decisions.

"It's not necessarily just about speed or the real-time nature of it," he explained. "It's a continuous stream of trusted process data because that's where you can make informed, smarter manufacturing decisions. You'll have better process understanding and ultimately greater efficiency across the board."

Hassell views real-time analytics as a foundational technology for advanced process control, where manufacturing systems automatically adjust process conditions based on continuous analytical feedback.

One challenge, however, is ensuring that measurements remain consistent regardless of where they are collected. Manufacturers may use at-line instruments, inline sensors, or in situ probes from different vendors that rely on different analytical principles. Variability among those systems can complicate data interpretation and reduce confidence in automated decision making.

"Manufacturers don't want one answer from an at-line instrument, another from an inline sensor, or a third from an in situ probe," Hassell said. "The real opportunity comes when data generated across locations and form factors can be interpreted in truly the same way."

Creating that consistency, he added, establishes a common analytical framework that allows manufacturers to trust measurements throughout development, scale-up, and commercial manufacturing.

"In a lot of ways, the different form factors speak to transferability," Hassell said. "If measurements can remain consistent through scale-up and move between development and manufacturing environments, that's where pharma will really leverage real-time analytics without sacrificing continuity or process understanding."

As the biopharmaceutical industry continues advancing toward digital manufacturing, the ability to generate continuous, reliable analytical data may become increasingly important for enabling automation, strengthening process understanding, and improving manufacturing efficiency. For Hassell, the goal extends beyond measuring proteins more quickly—it is creating trusted analytical information that can drive smarter manufacturing decisions across the entire product lifecycle.

Check out part one of this three-part interview here.