Recognize that development and end-product testing have a common goal.
Development and end-product testing have a common goal: to ensure a manufacturing system produces product with characteristics
within the safety and efficacy requirements. Testing must be viewed not as a risky activity required for compliance but as
part of a scientific decision-making process.
Development activities support this goal by identifying the optimal settings and allowable variation around these (i.e., the
design space) for various manufacturing process parameters. In-process control tests are developed to ensure that the process
will remain robust to variation of incoming materials and manufacturing conditions. End-product test data can be used in a
control system to holistically monitor and guarantee system performance and to control the consumer's risk.
Link sample size and acceptance criteria to manage risk.
If sample size is not properly addressed in acceptance criteria, the increased information carries with it increased risk.
The quality of information is directly related to the amount of data and the precision of the measurement tool. Acceptance
criteria should acknowledge the risks to the manufacturer and to the customer. Consequently, they should be adjusted whenever
there is a change to the amount of data being collected or the precision of the measurement device.
End-product testing is a type of acceptance sampling. Acceptance sampling is an established quality control tool with a firm
statistical basis (14). A key premise of acceptance sampling is that the risk of not meeting an acceptance criterion should
depend upon the batch quality and not be based upon the sample size evaluated. With traditional statistical acceptance sampling
plans, the acceptance criteria vary as a function of sample size or of the number of tests conducted on a given batch. This
is done to maintain established producer- and consumer-risk levels. Similarly, the acceptance limits for end-product tests
should depend on sample size. Such situations may arise when nontraditional methods are applied to batch monitoring such as
PAT. These methods may use sample sizes that are much larger than those of traditional release tests, thus providing much
better estimates of true batch characteristics. For example, a statistically based approach that better characterizes the
batch quality, while adequately controlling the risks and allowing for varying sample sizes, has been proposed (15).
Recognize the value of additional testing.
In many cases, data collected for continuous product development, improvement, and investigation should not be subjected
to the same acceptance criteria applied to end-product testing. Additional data improve the precision in estimating the true
level of a parameter, resulting in more informed decisions. Testing larger numbers of samples generally provides additional
knowledge of batch parameters such as average and RSD, or measures of stratification and trends. However, multiplicity leads
to a penalty for companies that attempt to use larger sample sizes for testing that must meet end-product testing criteria.
As part of an OOS investigation, it may be desirable to obtain additional test results from the batch in question or from
other batches made using the process in an attempt to gain further insight about the batch. Furthermore, as part of a larger
continuous process-improvement effort, additional data collection may be considered an extension of process-development activities.
Additional data are typically required to make a better informed risk-based decision about a batch or an overall process.
Although it has sometimes been said that additional data may be used to "test into compliance," manufacturers should not be
discouraged to acquire such additional data in the interest of continuous process understanding. The knowledge obtained from
these data may lead to improved processes with increased quality of produced batches as the ultimate goal.
Use averages where appropriate.
It is important to understand that data-driven decisions surrounding traditional CMC business objectives such as stability
testing, validation, and analytical investigations can be improved by the appropriate use of averaging rather than comparing
each replicate with a specification. Additional test results should be used to obtain better estimates of the true batch characteristics
by averaging or other data summarizing techniques. Although each individual test result estimates the true batch potency,
the average of the results is a better estimator because the uncertainty in the estimate is reduced as the number of samples
increases. Therefore, the average should be used to assess the batch's fitness for use and is the most appropriate quantity
to compare against the specification. Thus, when the goal is estimation of batch characteristics, averaging will facilitate
more informed and risk-based assessments of the true value for an analytical property.
Make effective use of data through proper statistical analysis.
Inappropriate interpretation of the data can result in increased risk to both manufacturers and customers. A proper statistical
analysis of data relies on a clear statement of the objective and a statistical design that addresses the quality goal with
appropriate attention to risk.