If instead, near-infrared (NIR) sensors are used, the fluid bed drying process can be monitored continuously and moisture
content end point determined in real time for each drying cycle. Risks from sampling errors and from raw material and environmental
variability that contribute to process variability are reduced if not eliminated. Process development studies based on time,
temperature, airflow and dew point were carried out to evaluate their effect on the dried material's physical properties,
including particle size and residual solvent content. The commercial process was simulated at laboratory, pilot, and commercial
scale. Because of equipment design differences (e.g., the bowl, agitation, and bag design) at different scales of manufacturing,
specific and some redundant trials at each step were deemed necessary. Figure 4A shows a schematic of an NIR detector attached
through the wall of the dryer bowl. Figure 4B shows several examples of real time data generated throughout the drying cycle.
NIR sensors monitored and enabled control of the drying cycle through a control algorithm. As the results were obtained during
each run, it was possible for the process parameters to be adjusted to produce dried material with consistent quality attributes.
Applying PAT in this manner led to a process with lower variability.
Case Study 5: Detecting Raw Material Variability
Figure 5 illustrates another case study involving a potential PAT application that is currently under study. In this application,
raw materials are identified by NIR to gain the tangible benefits of speed, accuracy, and cost savings that NIR offers over
traditional wet chemistries. NIR analysis allows for trending of raw material lot quality in real time and early detection
of any shifts in quality. A subsequent manufacturing step involves an extrusion unit operation which can be monitored continuously
inline for temperature and active ingredient concentration. An ultra-performance liquid chromatography test is performed offline
to test the material for presence of a degradation product. Particle size distribution is continuously monitored during milling
for process consistency and controlled by feedback or feed-forward control for manufacturability or compression performance
as a function of particle size. Finally, the tablet's weight, thickness, potency, and hardness are tested at line at the tablet
press for continuous quality verification and feedback control of compression. These additional data sources reduce quality
risk and variability while increasing process understanding.
Case Study 6: Monitoring Multiple Unit Operations
It is possible to combine and coordinate process knowledge from multiple unit operations to achieve a holistic picture of
the entire manufacturing process for a given product. Consider the process train for manufacturing a solid oral dosage form
illustrated in Figure 5. Several unit operations are required to combine the right quantity of active pharmaceutical ingredient
(API) and excipients under appropriate processing conditions and in a controlled environment to produce a drug product that
consistently meets quality attributes. Through the use of appropriate sensor technology, the real time profile for the manufacturing
process at each step or unit operation could be generated. During commercial manufacturing, material is moved through each
manufacturing step only if the real-time profile is consistent with expected historical data. At the end of a manufacturing
cycle, a review of the real-time profiles for all unit operations throughout the process could determine conformance and verify
that the product meets quality attributes. If the reported profile is consistent with historical data, based on population
analysis, real time release of product can be considered. Fundamentally, only those lots that fall outside the known population
of data would require additional off-line testing or be rejected.
Successful implementation of QbD in the pharmaceutical industry requires a concerted effort between the regulators and the
industry. Case studies such as those presented here will serve as useful tools in gaining the common ground and defining best
practices in the various functions: Research and Development, Quality, Manufacturing and Regulatory. Ultimately, the implementation
of QbD is likely to result in increased efficiencies both for regulatory reviews and for pharmaceutical manufacturing.
This article summarizes the presentations and discussions that occurred in the plenary session titled "How do you sell Quality
by Design (QbD)?" at the PDA–FDA Joint Regulatory Conference held on September 24–28, 2007, in Washington, DC. The objective
of the session was to discuss the challenges that are encountered when implementing the QbD paradigm.
Anurag S. Rathore, PhD, is a director of process development at Amgen, 805.447.4491, firstname.lastname@example.org
He is also a member of BioPharm International's editorial advisory board. Stephen H. Montgomery, PhD, is a law clerk at McDonnell Boehnen Hulbert and Berghoff, LLP, Azita Saleki-Gerhardt, PhD, is division vice-president for quality at Abbott, North Chicago, IL, and Stephen M. Tyler is director of strategic quality and technical operations at Abbott.