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.
Process validation provides the documented evidence that a given process, operated within established parameters, can effectively
and reproducibly produce an intermediate, active pharmaceutical ingredient (API), or drug product meeting predetermined criteria
and quality attributes. While final drug products and APIs must meet specifications based on standards mandated by safety
concerns and other factors, intermediate process steps do not have such mandated standards. However, they still must meet
a number of acceptance criteria to demonstrate process consistency and other required product quality attributes to meet final
Establishing appropriate validation acceptance criteria (VAC) is one of the greatest challenges in the development of a commercial
biopharmaceutical manufacturing process. Setting VAC that are too broad will not enable demonstration of adequate process
control. VAC that are too narrow can result in failed validation runs, even though the process may be performing adequately.
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.
Process characterization involves bench-scale studies performed to demonstrate process robustness and to help predict the
performance of the process within the constraints of the operational ranges that will be used in manufacturing. One approach
to process characterization has been described previously.1 Briefly, operating parameters (OPs) that are most likely to impact the process performance parameters (PPs) are identified
by a risk analysis (e.g., failure modes and effects analysis2,3 ). These parameters are then tested outside their normal operating range (OR) (typically 2–3X outside the OR) to determine
process robustness (ROB) and to eliminate from further study any OPs that have no effect on the process over this range. Those
parameters exhibiting significant effects in the robustness study are tested to the edge of their OR (EOR) in a second study
designed to identify two-way statistical interactions between OPs. From these studies, we can predict the expected performance
of the process within the constraints of all ORs. Table 1 provides definitions for process characterization.
Table 1. Process characterization definitions.
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
Tolerance intervals can be computed and used to set VAC under any of the following scenarios:
- When only data from large-scale runs are available, and there are no bench-scale data that adequately represent the full-scale
process. (Typically, this may be a relatively small data set.)
- When bench data derived from process characterization experimental design studies are combined with large-scale runs to compute
tolerance intervals at
a. setpoint conditions; or as
b. OPs move within the OR.
Examples of calculating tolerance intervals computed for each of these scenarios appear in the three scenarios that follow.