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How Process Analytical Technology Moves Quality into Real Time Dr. Stanislav Kasakov
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Driving Global Quality in Viral Vector Manufacturing [Melanie Kearney] Melanie Kearney
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Agility in Pharma: Transforming Challenges into Opportunities [Elizabeth Hickman] Elizabeth Hickman
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How Process Analytical Technology Moves Quality into Real Time Dr. Stanislav Kasakov
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Process Analytical Technology (PAT) used to be a niche tool for specialists. It meant bulky spectrometers, hard‑to‑manage optics, and feasibility studies that lived at the edge of process engineering. That picture is outdated. PAT has shifted from “nice to have” to core strategy. It helps teams build quality into the process, reduce uncertainty, and move from testing outcomes to understanding and control.
This article summarizes key insights from the discussion between Yan Kugel and Dr. Stanislav Kazakov (Senior Business Developer at Thermo Fisher Scientific). It covers what PAT really means, why you should start early, how PAT helps scale lab recipes to manufacturing, where the technology is heading, and what the human role will be as automation and AI take on a larger share of the heavy lifting.
This episode is supported by Thermo Fisher Scientific, a partner across the drug lifecycle to accelerate discovery, scale manufacturing, and ensure compliant release. Our analytical and process technologies support pharmaceutical and biotech teams from research and formulation through commercial manufacturing and quality control. With integrated instrumentation, workflow solutions, and expert service, we help protect yield, ensure data integrity, and deliver therapies to patients with confidence.
Learn More About Thermo Fisher Scientific
PAT can be understood as the sum of the information needed to characterize, understand, and develop a process across the molecule journey. That includes:
Put plainly: PAT brings visibility into the “black box” of a process. It increases the density of information so decisions are based on process behavior rather than occasional lab samples.
Two shifts made PAT strategic:
When to introduce PAT: The earlier the better. Needs to start on a lab scale. That’s the single most practical piece of advice from the episode.
Late adoption is possible, but harder. If PAT is introduced after filing and validation, the regulatory and business hurdles rise. Retrofitting sensors and changing documentation require a strong business case. If deviations and scrap rates are high, PAT can justify the investment. But the path is easier and cheaper when PAT is built into development from the beginning.
Traditional workflows rely on sparse sampling. Typical labs analyze samples hours or days apart. As a result, it misses transient events and their links to process settings. PAT increases temporal resolution to minutes or even seconds. This allows teams to:
Over time, deep process understanding can reduce the need for permanent, dense sensing in manufacturing. Once a process is well characterized, you may only need a small set of simple sensors for control. PAT creates that understanding.
Several practical mistakes are repeated in scale-up:
PAT should be applied to existing processes when it delivers clear value. If an existing production line shows frequent deviations or high scrap, PAT can definitely help.
But they should also expect:
PAT already supports real‑time release testing in some cases. It works in two modes:
Both approaches reduce time to release and give better control of quality attributes.
PAT is usable across many domains:
PAT produces dense data, and AI helps translate that into actionable insights.
Key roles for AI:
AI won’t eliminate the need for human judgment. It will shift human roles toward oversight, strategy, and cross‑functional coordination.
PAT scientists will move from manual model building to:
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Introduction of AI in the equation offers these changes:
The future trends are pretty clear:
The strong vision is a future in which PAT data, AI models, and automation accelerate R&D, reduce time to market, and support safer, more consistent manufacturing.
PAT is no longer an add-on—it is a strategic approach to improving process understanding, enabling more reliable scale-up, and ensuring consistent quality.
As automation and AI continue to lower technical barriers, more teams will be able to adopt PAT and focus their efforts where it matters most: interpretation, decision-making, and implementation.
Stanislav Kasakov is a biopharma process analytics professional at Thermo Fisher Scientific, focused on helping scientists improve process understanding through practical use of Process Analytical Technology (PAT). Based in Zurich, he works at the intersection of development, scale-up, and manufacturing, supporting both small-molecule and biopharmaceutical workflows. His work centers on Raman, NIR, and complementary inline and at-line analytical tools applied to real-world pharma and bioprocessing challenges, including real-time release testing, fill-finish, and continuous manufacturing. Stanislav is actively involved in industry collaboration and knowledge sharing, contributing to conferences, workshops, and publications such as PAT Connect, where he regularly presents applied case studies and implementation lessons. With a strong link to academia through Technische Universität München and close engagement with the PAT community, he is known for connecting advanced analytics with practical, compliant manufacturing needs—moving PAT from isolated pilots into routine use.
Melanie Kearney draws on 30+ years across R&D, QC, radiopharma and viral vectors to show how cross-functional experience, visible leadership, and scalable QMS practices keep advanced therapies inspection-ready and patient-focused.
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