One goal of process characterization is establishing representative performance parameter ranges that can be used to set validation acceptance criteria (VAC). Characterization studies yield varying numbers of data points from multiple experiments, and may also include data generated at different scales (e.g., bench, pilot, and commercial), which add complexity to the analysis. Many statistical approaches can be used to set ranges from large data sets. As an example, we present the statistical considerations and techniques for setting validation acceptance ranges for a chromatography step used in purifying a recombinant protein. Performance parameter data from a combined data set consisting of 67 bench, six pilot, and three full-scale runs were analyzed using the statistical analysis software JMP (SAS Institute). The combined data set was used to compute tolerance intervals, so that sources such as scale and column feed material could be properly modeled. The resulting ranges were used to establish validation acceptance criteria.
If there are no representative bench-scale data from process characterization studies, the data set used for a statistical analysis to establish acceptance criteria may be quite small. Yet, if both process characterization data from bench scale studies, as well as data from large-scale runs are available, it may not be obvious how to combine these data sets in an appropriate way. In this article, we describe statistical methods in which bench-scale process characterization data are combined with a smaller, large-scale data set to establish validation acceptance criteria that are indicative of process consistency, yet are not unduly restrictive.
TOLERANCE INTERVALS AS PROCESS VALIDATION CRITERIA
A two-sided tolerance interval is an interval thought to contain 100p% of a population with 100(1 – α)% confidence. For example, if p = 0.99 and α = 0.05, then a two-sided tolerance interval will contain 99% of the population with 95% confidence. This means that the reported range is expected to include 99% of the PP values that will be generated by the process under consideration. Tolerance intervals are particularly useful for setting VAC because they describe the expected long-range behavior of the process.
Tolerance intervals can be computed and used to set VAC under any of the following scenarios:
a. setpoint conditions; or as
Examples of calculating tolerance intervals computed for each of these scenarios appear in the three scenarios that follow.