Moving Quality Control From the Lab to the Line
An expert reflects on the practical steps required to implement real-time QC into biopharmaceutical manufacturing lines.
Bioprocessing underpins the manufacture of many modern biopharmaceuticals, using living cells, microorganisms, and biological systems to produce therapies ranging from recombinant proteins and monoclonal antibodies to cell-based therapies.
As biopharmaceutical manufacturers face growing pressure to accelerate timelines while maintaining product quality, the need to shift from traditional quality control (QC) models to real-time QC, which continuously monitors and assesses quality during manufacturing, is becoming increasingly evident.
Historically, quality was verified post-production, creating delays between product manufacture and evaluation. Today, advances in process analytical technology (PAT) are enabling quality measurement within the process itself.
Few professionals have a better understanding of this transition than Shailesh Karavadra, business development and applications manager of vibrational spectroscopy at Thermo Fisher Scientific. Karavadra works closely with pharmaceutical manufacturing teams, focusing on technologies that support raw material verification, quality decision-making, and operational efficiency.
In this interview with Technology Networks, he discussed the technical requirements and organizational shifts required to embed real-time QC and process analytics into manufacturing lines successfully.
Building quality into biomanufacturing lines
What does it take for companies to move QC from the lab to the line successfully?
Transferring QC from the laboratory to the line requires more than simply installing analytical instruments on equipment. According to Karavadra, organizations must fundamentally rethink how they monitor and manage quality.
“Successfully moving QC from the laboratory to the production line requires a shift in mindset from testing quality after production to building quality into the process as it happens.” — Shailesh Karavadra.
Karavadra highlighted three elements that are crucial for success in this shift:
- Understanding of critical quality attributes (CQAs) and critical process parameters (CPPs): what needs to be measured, why, and how those measurements will be used.
- Implementing validated PATs that can operate in production environments. Tools include in-line Raman spectroscopy, process near-infrared (NIR) spectroscopy, process mass spectrometry, and more.
- Alignment between production, quality, engineering, automation, and regulatory teams when implementing and maintaining real-time QC processes.
CQAs and CPPs
To assess biopharmaceutical quality, it is essential to measure CQAs and CPPs. In this context, CQAs are the physical, chemical, biological, or microbiological characteristics that determine whether a product meets regulatory and performance requirements. CPPs are process variables that can affect those attributes and, therefore, require monitoring and control.
Karavadra emphasized that successful implementation doesn’t depend only on implementing the technologies and retrieving the data, but also on whether the data will be used to guide decisions proactively.
"The most successful companies do not simply replace a lab test with an in-line analyzer,” he noted. “They redesign the workflow so real-time data can support faster decisions, reduced deviations, improved batch consistency, and ultimately a more proactive quality culture."
Transformation beyond technology is needed:
- Moving QC in-line requires a shift from endpoint testing to continuous quality assessment.
- Technologies are available to support real-time QC, but actively using the resulting data to support decision-making is critical in order to reap the rewards.
Challenges in real-time QC
What technical challenges arise when moving QC from the lab to the line?
Once the technology, teams, and strategy are in place, the practical realities of moving QC methods from a controlled laboratory environment into the complexity of the production line must be confronted.
Karavadra noted that manufacturing environments pose far greater analytical challenges than laboratory environments.
“In the lab, samples are prepared, controlled, and analyzed under stable conditions,” he said. “In production, the analyzer must deal with changing temperatures, pressures, flow rates, bubbles, foaming, turbidity, fouling, biological variability, and sometimes sterile or hazardous environments.”
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To add to these challenges, Karavadra highlighted biopharmaceutical-specific considerations: “Cell culture and fermentation processes are dynamic living systems. The matrix changes over time; cells consume nutrients, metabolites accumulate, and gases evolve.”
“Product quality can be affected by subtle shifts in the process.” — Shailesh Karavadra.
He highlighted the additional difficulties that biological fluctuations create, from complicating probe placement and calibration strategies to compromising the long-term dependability that prompted manufacturers to adopt these methods in the first place.
To address these challenges, real-time analytical technologies must deliver reliable measurements while adapting to process variability. Karavadra highlighted three different techniques, which bring distinct capabilities:
- Raman spectroscopy primarily measures nutrients and metabolites via an in-line probe, requiring little or no sample preparation.
- NIR spectroscopy primarily measures process variables such as water content and product concentration via an in-line probe.
- Process mass spectrometry is conducted via on-line sampling. It primarily measures gas composition to assess culture performance.
Karavadra stressed the ultimate goal: "The technical goal is not just to collect more data, but to generate reliable, validated, decision-ready data that can be trusted by operators, quality teams, and regulators."
What real-time QC means for manufacturers:
- Manufacturing environments have more analytical variability to overcome compared to laboratories.
- Real-time QC in bioprocessing, such as in biopharmaceutical manufacturing, requires tools capable of monitoring highly dynamic biological systems.
- Solutions must provide data that is actionable and can withstand regulatory scrutiny.
Changing organizational culture is crucial
What cultural barriers emerge when embedding QC into production lines, and how can they be addressed?
Karavadra shared his insights on why cultural barriers have emerged: “Traditional QC is often seen as the responsibility of the laboratory, while production is responsible for throughput.”
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“When QC moves to the line, those boundaries change,” he continued. “Quality becomes part of daily manufacturing decisions, not a separate function at the end of the process.”
“The biggest cultural barrier is ownership.” — Shailesh Karavadra.
Real-time analytics blurs the boundaries between teams that previously operated largely as separate entities.
“That can create understandable concerns,” noted Karavadra. “QC teams may worry about loss of control or method integrity. Production teams may worry that real-time analytics will slow them down or create more deviations.”
He added: “Operators may be uncomfortable acting on multivariate data or model-based outputs. Regulatory and validation teams may ask how these systems will be qualified, maintained, and defended during audits.”
Karavadra noted that early cross-functional engagement can help address these concerns and foster a collaborative culture from the outset: "Quality, manufacturing, automation, engineering, and process development need to define the measurement strategy together. Operators need training not just on the instrument, but on what the data means and what action should follow.”
“QC teams should remain central, but their role evolves from end-point testing to method governance, model lifecycle management, and process assurance,” he continued, emphasizing that real-time analytics will not diminish the role of QC teams, but instead broaden their scope.
Practical takeaways for success in real-time QC integration:
- Understanding and addressing concerns early is essential to help teams embrace evolving responsibilities.
- Cross-functional planning can improve real-time QC implementation success.
- Operator training should focus on data interpretation and actionable insights as well as instrument operation.
- Real-time QC does not diminish individual responsibilities but can actually broaden scope.
Bioprocessing QC: From reactive to proactive
How do you see real-time process analytics reshaping QC over the next 5–10 years?
Karavadra predicted that real-time analytics will fundamentally change how manufacturers define and achieve quality over the next decade.
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Although many rely on traditional, laboratory-based QC today, he sees this changing: “In the future, more QC decisions will be supported by continuous process data … Instead of waiting for a lab result to confirm whether a batch is within specification, manufacturers will increasingly know the state of the process as it is happening.”
"Over the next 5–10 years, real-time analytics will reshape QC from a reactive testing model into a predictive and preventive quality model." — Shailesh Karavadra.
This vision aligns with the US Food and Drug Administration (FDA) PAT framework and industry momentum toward real-time QC.
Karavadra described what this shift will look like practically: “In practical terms, QC will become more distributed, more digital, and more integrated. Labs will still be essential, especially for release, stability, reference methods, investigations, and complex assays, but routine quality monitoring will increasingly move closer to the process line.”
The reality of real-time QC today vs the potential tomorrow:
- Manufacturers continue to rely heavily on traditional QC methods, but the move toward continuous, on-the-line process analytics is palpable.
- FDA guidance is available to support the shift from lab to line.
- Laboratories will remain essential but will focus on specific, high-complexity testing.
"The companies that benefit most will be those that treat real-time analytics not as an instrument purchase, but as a strategic quality transformation,” concluded Karavadra.
Key takeaways:
- Successful implementation of real-time QC depends on robust PAT tools, effective control strategies, and cross-functional collaboration.
- Cultural adoption is just as important as analytical capability, particularly when defining ownership of quality decisions.
- Real-time QC and PAT will enable pharmaceutical manufacturing to be less reactive and more proactive.
This content includes text that has been created with the assistance of generative AI and has undergone editorial review before publishing. Technology Networks' AI policy can be found here.
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