QualityGlobal competition fueled by the power of information technology has forced the pharmaceutical and biotechnology industries to seek new ways to compete. The US Food and Drug Administration (FDA) has promoted quality by design (QbD) as an effective approach to speed up product and process development and create manufacturing processes that produce high-quality products that are safe and effective (1–3). Statistical design of experiments (DoE) is a tool that is central to QbD and the development of product and process “design space” (a combination of raw material and process variables that provide assurance that a quality product will be produced) (4). Much has been written about using DoE to create process design spaces. Here, I address factors that make it successful.
Experimentation Strategy
The first step in selecting a statistical design is creating a strategy for using DoE. You need a strategy based on a theory about experimentation. Experience over many decades in many different subject matters has identified some critical elements of an effective theory.
First is the Pareto principle, which states that only a small number of the many variables that can have an effect actually do have major effects. Experience has shown that typically three to six variables have big effects. As Juran has emphasized, they become the vital few variables (5).
The next important piece of the theory is that experimentation is sequential; you don’t have to collect all the data at once. A better strategy is to collect the right data in the right amount at the right time (6).
The third element is to keep your focus on process and product understanding and summarize this knowledge and understanding in a process model that can be used to develop the design space as well as enable process control, another critical element of QbD. In recent decades, the pharmaceutical industry has adopted innovative cutting-edge technologies to conduct research and develop new drugs that can bring great value to society. In contrast, the emphasis on the manufacturing process is relatively low, so many such products are still usually produced using empirically developed and relatively inefficient batch processes. The U.S. Food and Drug Administration (FDA) introduced programs such as QbD and PAT, which encouraged the industry to use its wealth of skills to solve this imbalance, thereby actively changing process operations and improving processes. The process model is represented conceptually as Y = f (X), where Y is the process output variables predicted by the levels of X values (the process inputs and controlled variables).
Another element of the theory is that the experimental environment determines experimental strategy and designs to be used. Diagnosis of this environment includes agreeing on goals and objectives, number, type, and ranges of the variables (X values) to be studied; desired information regarding the variables; resource constraints; and available scientific theory regarding the effects of the variables (7).
Screening–Characterization–Optimization (SCO) Strategy: Table 1 summarizes an operational definition of the SCO theory of experimentation (7). This strategy identifies three experimental environments: screening, characterization, and optimization. Table 1 summarizes the desired information of those three phases.
Yan and Le-he illustrate SCO in their work (8), where they describe a fermentation optimization study that uses screening followed by optimization. In their investigation, 12 process variables were optimized. The first experiment used a 16-run Plackett–Burman screening design to study the effects of those variables. The four variables with the largest effects were studied subsequently in a 16-run optimization experiment. The optimized conditions produced an enzyme activity that was 54% higher than the operations produced at the beginning of the experimentation.
(All Rights Reserved)