Since current analytics and controls do not necessarily indicate product quality, interest in deploying PAT tools closer to the production environment will continue, believes Sam Watts, Ph.D., business development officer at Stratophase. “But the complexity of analytical methods, with respect to data-gathering and subsequent analysis and interpretation, leads to significant challenges in applying these techniques to multipoint, real-time, and inline applications.”
Stratophase produces optical microchip sensors that monitor both process status and product quality in real time, or which serve as a gateway for triggering process sampling and slower, but more detailed off-line analysis. Stratophase has worked closely with a number of process experts in a range of industries on PAT-type initiatives, including manufacturers of pharmaceuticals and precursors. “A number of our demonstration systems are now being deployed with key end users, which will be critical for our product-development activities.”
Dr. Watts believes there is a general trend toward accepting “novel monitoring solutions” that present a compromise between the high chemical specificity associated with historical analysis and rapid, scalable methods. There is the potential for a combined solution as well, employing both full-featured analysis and real-time multipoint monitoring to trigger the more in-depth analysis.
Stratophase has worked with GlaxoSmithKline using Stratophase’s temperature compensated refractive index measurement to determine deviation from ideal feedstock concentration within a continuous flow reactor. It has also conducted trials monitoring fermentation processes for alcoholic beverages, as well as in the manufacture of biofuels, biopolymers, and biopharmaceuticals.
The firm is building an empirical model for E. coli fermentation that will compare real-time process status against a model. “The validation of this model-building process is a key stage in our development of PAT-type bioprocess applications,” says Dr. Watts.
Lyophilization is a classic example of a process for which quality has traditionally been “tested in,” and where PAT could help considerably.
“Smart” lyophilization technology, licensed from the University of Connecticut and commercialized by SP Scientific is slowly helping to change the optimization of primary freeze-drying, that is the removal of all free ice.
“Over the years, our understanding of lyophilization technology has evolved from an art, or black box activity, to a science,” says Leslie Mather, director of pilot lyophilizers at SP Scientific’s Stone Ridge, NY, facility.
The technique, which incorporates process analytics during development and optimization, involves doing some preliminary work to understand the thermal properties of the formulation through differential scanning calorimetry and freeze-drying microscopy. This leads to an understanding of how the drug freezes and melts, and helps establish boundaries such as minimum product surface area.
Then, during the first—and only—experimental run, the equipment takes very rapid pressure measurements during drying. “The valve between the product chamber and condenser must close in less than one second,” Mather says. A pressure reading is then recorded over the next 25 seconds, after which the valve is re-opened.
According to Mather achieving the optimal lyophilization process takes just one experimental run.
Proprietary algorithms translate pressure data into values for product resistance, temperature at the ice surface, shelf temperature, heat flow, mass transfer, and ice thickness. The resulting optimization cycle enables operators to conduct lyophilization in walk-away mode. Although intended only for optimization, the technique may be employed during manufacturing and as an add-on.
Engineers always model biomanufacturing processes to identify bottlenecks and perform general streamlining before any equipment is assembled. Rafiqul Gani, Ph.D., professor of chemical and biochemical engineering at the Technical University of Denmark, goes a step further and models the process from the perspective of suitable analytics.
Theoretically a PAT model can be as useful as the process model itself. Armed with a product and process, Dr. Gani’s model analyzes the steps and equipment required, identifies variables that are most critical for monitoring product quality, and recognizes which of these should be measured and monitored as well as equipment, cost, and time for deployment.
Thus far, working through an industrial consortium, he has applied his PAT model to fermentations, milk pasteurization, tablet manufacturing, and crystallization processes.
“We think that, as in other situations, a PAT model will not directly replace experiments, but supplement it,” Dr. Gani says. “The more information one has, the better the analysis and decision making.”