A second example involving the characterization of a crystallization step in a classical pharmaceutical process is shown in
Figure 5. Initially, the active pharmaceutical ingredient (API) recovery process was stressed one variable at a time. First,
the crystallization operation was tested at 35 °C and found to produce acceptable potency values at pH levels of 5.75, 6.25,
and 7.0. In a subsequent experimental series, the crystallization at a pH level of 6.25 was found to be acceptable at temperatures
of 33 °C, 35 °C, and 38 °C. Although the data suggested acceptable ranges for pH and temperature, little knowledge was gained
about whether the two parameters interact. For example, what happens at a high pH and a high temperature (Figure 5a)? Hence,
the process was further evaluated using a DOE approach that showed that potency was indeed an interactive function of temperature
and pH, as well as stirring speed and time. It was determined that certain combinations of low temperature and low pH, or
high temperature and high pH, affected the level of impurities. Ultimately, the unit operation design space was defined and
optimized using this knowledge to produce an API of high purity.
Figure 5. Illustration of outcome of an experimental study examining performance of a crystallization step in a pharmaceutical
process using A) univariate experimentation and B) design of experiments (DOE). In the univariate baseline method (A), acceptable
potency levels resulted from crystallization at 33, 35, and 38 °C, and separately at pH values of 5.75, 6.25, and 7.0, but
no interactions were studied. A DOE approach was then used to study the effect of interactions of pH and temperature on potency
(shown in B).
Figure 5b illustrates the effect of temperature and pH on potency. This example highlights the need to use statistical tools
in designing process characterization studies and analyzing the resulting data. Using univariate approaches can often result
in wrong or misinformed conclusions.
The biotech and traditional small-molecule pharmaceutical industry has been working actively on applying the concepts of Quality
by Design to the development and manufacture of drug products. Case studies such as those presented here will serve as useful
tools in establishing common ground about how to develop and define a design space. They provide examples of how to carry
out three key steps in process characterization: 1) performing a risk analysis to identify parameters for process characterization;
2) developing studies based on a design-of-experiments approach to study those parameters and their interactions; and 3) executing
those studies and analyzing them to determine which parameters are critical and how the design space should be defined. Part
2 of this article will present a stepwise approach to validating, filing, and monitoring the design space. It will also discuss
how to implement QbD for legacy products and how to integrate QbD with process analytical technology (PAT).
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, email@example.com
He is also a member of BioPharm International's editorial advisory board. Stephen H. Montgomery is a law clerk at McDonnell Boehnen Hulbert and Berghoff, LLP, Azita Saleki-Gerhardt 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.