Statistical Tools for Process Validation, Stage 1: Process Design

Process validation is vital to the success of companies that manufacture drugs and biological products for people and animals. According to the FDA guidelines published by the U.S. Department of Health and Human Services:

“Process validation is defined as the collection and evaluation of data, from the process design state through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product.”
— Food and Drug Administration

The FDA recommends three stages for process validation. In this 3-part series, we will briefly explore the stage goals and the types of activities and statistical techniques typically conducted within each. For complete FDA guidelines, see www.fda.gov. 

The goal of this stage is to design a process suitable for routine commercial manufacturing that can consistently deliver a product that meets its quality attributes. It is important to demonstrate an understanding of the process and characterize how it responds to various inputs within Process Design.

Suppose you need to identify the critical process parameters for an immediate-release tablet. There are three process input variables that you want to examine: filler%, disintegrant%, and particle size. You want to find which inputs and input settings will maximize the dissolution percentage at 30 minutes.

To conduct this analysis, you can use design of experiments (DOE). DOE provides an efficient data collection strategy, during which inputs are simultaneously adjusted, to identify if relationships exist between inputs and output(s). Once you collect the data and analyze it to identify important inputs, you can then use DOE to pinpoint optimal settings.

The first step in DOE is to identify the inputs and corresponding input ranges you want to explore. The next step is to use statistical software, such as Minitab, to create an experimental design that serves as your data collection plan.

According to the design shown below, we first want to use a particle size of 10, disintegrant of 1%, and MCC at 33.3%, and then record the corresponding average dissolution% using six tablets from a batch:

Using Minitab’s DOE analysis and p-values, we are ready to identify which X's are critical. Based on the bars that cross the red significance line, we can conclude that particle size and disintegrant% significantly affect the dissolution%, as does the interaction between these two factors. Filler% is not significant.

Optimizing Product Quality

Now that we've identified the critical X's, we're ready to determine the optimal settings for those inputs. Using a contour plot, we can easily identify the process window for the particle size and disintegrant% settings needed to achieve a percent dissolution of 80% or greater.

And that's how you can use design of experiments to conduct the Process Design stage. Next in this series, we'll look at the statistical tools and techniques commonly used for Process Qualification!