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Sep 1, 2012 (Vol. 32, No. 15)

Process Monitoring Growing in Sophistication

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    Sartorius Stedim Biotech has addressed data bottlenecks in real-time bioprocess monitoring by including a supervisory control package within its BIOSTAT Qplus parallel bioreactor system.

    Bioprocess monitoring and control have been around since the beginning of therapeutic biotechnology, borrowing methodologies from industrial biotech and chemical/pharmaceutical processing. During the 1980s and 1990s, new analyzers arose from medical instrumentation for measuring pH, dissolved oxygen, carbon dioxide, etc. This instrumentation, in one form or another, serves every bioreactor in use today, down to milliliter-sized scaledown vessels.

    This “traditional” bioprocess monitoring is of course still a very big thing, often involving some very substantial organizations.

    Sartorius Stedim Biotech has incorporated multiparameter monitoring and control in its BIOSTAT® Qplus parallel bioreactor system, which uses 0.5 and 1 L vessels. Intended for design of experiment (DOE) for process optimization, the system incorporates O2 and CO2 off-gas analyzers for monitoring, O2 supplementation capability, and BioPAT® software for supervisory control.

    Since DOE mimics conditions in much larger bioreactors, it demands the same analytics and control. Yet one of the knocks against continuous, real-time bioprocess monitoring is what to do with all the data. Since BIOSTAT runs up to 12 culture vessels in parallel, the data-handling system must be robust. Sartorius has incorporated a full MFCS SCADA supervisory control package with Qplus to go along with the monitoring functions.

  • From Diagnostics to PAT

    An internal collaboration between Roche Diagnostics and Roche Pharma arose from the need to improve process monitoring and control during drug substance manufacturing. “Roche Pharma was struggling with the gold standard technology at the time,” says Andreas Schneider, vp for life science alliances at Roche Diagnostics. Instrumentation consisted of three analyzers from a U.S.-based international vendor that had begun as a manufacturer of clinical analyzers.

    Schneider and his colleagues realized that Roche Diagnostics already possessed the technology to duplicate the performance of the three process analyzers they were using at the time. Like their vendor’s system, this technology originally served medical diagnostics and monitoring markets. “We asked ourselves, is there a possibility of modifying an analyzer designed for clinical use into a process monitor?”

    After considerable development work, they came up with the Cedex Bio Bioprocess Analyzer, a continuous, random-access, multichannel bioprocess analyzer. Cedex measures up to 14 parameters simultaneously through a combination of electrolyte testing (similar to standard process monitors) and “photometric” analysis through a built-in, 12-wavelength spectrophotometer. Roche is targeting cell culture processing from cell-line development to large-scale manufacturing.

    Reducing the number of process analyzers, Schneider says, will improve consistency, minimize human intervention and maintenance, and reduce technical support.

    Cedex Bio hits all the standard analytes: glucose, lactate, LDH, ammonia, potassium, sodium, glutamine, and glutamate. A version with IgG measurement, and one for microbial cultures, will be available in future upgrades. Roche claims time-saving and greater accuracy compared with existing bioprocess monitors. “And this plays to QbD,” Schneider says, “where you need accurate data to design studies.”

    Roche is collaborating with Bayer Technology Services, the engineering arm of Bayer Healthcare, on an autosampler that feeds Cedex Bio with process fluid, allowing real-time analytics and feedback control. The two companies are investigating how best to integrate the two systems to make measurements more accurate and fully automated, providing data around the clock.

  • PAT and QbD

    The FDA’s Process Analytic Technology (PAT) initiative, promulgated in 2004, had the immediate effect of opening up the panoply of chemical analysis tools to bioprocessors. Vendors operating in traditional spectroscopy markets—near-infrared, Raman, and UV/visible spectroscopy—were breaking into biopharmaceutical markets, as medical device companies had done a decade earlier.

    A scan of the literature shows how radically new sensing modalities depart from traditional quantitation of pH and glutamate. PAT also inspired development of novel sensor and monitoring technologies for “old” parameters, particularly for instrumentation and data handling.

    From the perspective of operations, PAT’s major industry-wide success has been bringing process monitoring closer to the process. FDA defines analytics as being offline (far away), at-line (in the same room), and inline, which roughly correspond to cycle times of days/hours, hours/minutes, or more-or-less real time.

    Bioprocessors now have a better appreciation for shorter analysis times and greater proximity to where the action is. Bioprocess monitors that measure routine parameters (pH, DO, etc.) are most often deployed in-line, but other technologies are moving toward the bioreactor at a snail’s pace. One often-cited reason is that production suites were designed for manufacturing, not analytics, and that space can be tight.

    Regardless, eight years after PAT, analyzers have become more sophisticated and robust, while biomanufacturers have a much more refined sense of monitoring, quality, and risk minimization. This is due in no small part to FDA’s well-reasoned connection of PAT, process understanding, and quality by design (QbD). These three factors have come to form the predominant business case for advanced process monitoring and control.

    Their connectedness look great on paper, and sound great in theory. Building a strong business case for higher quality, products that are safer and more effective, and fewer batch failures is easy. Nevertheless, the uptake of advanced sensing and monitoring schemes has been painfully slow.

    PAT’s promise to deliver “process understanding” and enable QbD is based on the premise that analytics will provide an actionable (or predictive) understanding of critical process parameters affecting quality, which will lead to higher-quality product and/or less waste.


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