A characteristic of good science is good data. Quality data are arguably more important today than ever before. Data are used to develop products and processes, control manufacturing processes (Snee 2010) and improve products and processes. Quality data also reduces the risk of poor process performance and defective pharmaceuticals reaching patients.

Measurement is a process that is developed, controlled and improved just like a manufacturing process. Indeed, quality data are the product of measurement processes (Snee 2005). Quality by Design (QbD), introduced by the FDA in 2005, is focused on the development, control and improvement of processes. Data are central to QbD and in turn QbD concepts, methods and tools can be used to develop, control and improve measurement processes (Borman, etal 2007: Schweitzer, etal 2010. As a result QbD and test methods have a complementary relationship; each can be used to improve the other.

This article discusses the concepts, methods and tools of QbD that have been successfully used to design, control and improve measurement systems. The specific approaches are summarized in Table 1 and discussed in the following paragraphs. The concepts and methods involved will be introduced and illustrated with pharma and biotech case studies and examples.

Design of Experiments, an effective QbD tool is used in the development of test methods to create the operability region for the method by first running a screening design to test the effects of various candidate test method variables. There are typically a large number of variables tested in the screening design to reduce the risk of missing any important variables. The variables found to have the largest effects (both positive and negative) are studied in a subsequent optimization experiment, the output of which is operating window for the method which serves the same function as the Design Space for a product or process. As a result we refer to this as the “Test Method Design Space”.

In a recent test method development project 11 variables were studied in 24 runs using a Plackett-Burman screening design. The four variables with the largest effects were evaluated further in a 28-run optimization experiment producing the design space for the method. The next step in the development was to assess the effects of raw material variations

Test Method Repeatability and Reproducibility is an important assessment once the method has been developed initially. Thus is done with using a Gage Repeatability and Reproducibility study referred to as a Gage R&R Study.

In the study 5-10 samples are evaluated by 2-4 analysts using 2-4 repeat tests sometimes involving 2-4 test instruments. Output from such a study produces quantitative measures of repeatability, reproducibility and measurement resolution. These statistics are then used to evaluate the value of the method to be used for product release and process improvement. The variance estimates obtained are also often used to design sampling plans to monitor the performance of the process going forward.

Test Method Ruggedness. Sometimes we find that as a test method is used the observed variation in the test results becomes too large. What do I do now you ask? One possibility is to evaluate the measurement process/procedure for ruggedness. Measurement method is “rugged,” if it is immune to modest (and inevitable) departures from the conditions specified in the method (Youden 1961). Ruggedness (sometimes called robustness) tests study the effects of small variations in the how the method is used. There are other sources of variation in a measurement method in addition to instruments and analysts which are typically the subject of Gage R&R studies. Such variables include raw material sources and method variables such as time and temperature. Ruggedness can be evaluated using two-level fractional-factorial designs including Plackett-Burman designs (Box, etal 2005: Montgomery 2013).

A test method is said to be rugged if none of the variables studied have a significant effect. When significant effects are found a common fix is to rewrite the SOP to restrict the variation in the variables to a range over which the variable will not have a great effect on the performance of the test method.

Process Variation Studies. Sometimes when the process variation is perceived to be too high it is not uncommon to think that the measurement is the root cause. Sometimes this is the case but often it is not. In such situations there are typically three source of variation that may contribution to the problem: the manufacturing process and the sampling process as well as the test method (Snee 1983).

In two instances that I’m aware of the sampling method was the issue. In one case the variation was too high because the sampling procedure was not followed. When the correct method was used the sampling variance dropped by 30%. In another case each batch was sampled 3 times. When the process variance study was run sampling contributed only 6% of the total variance. The Standard Operating Procedures were changed immediately to reduce the samples to 2 per batch; thereby cutting sampling and testing costs by one-third. A study was also initiated to see if one sample per batch would be sufficient.

Method Continued Verification. The FDA Process Validation Guidance calls for Continued Process Verification which includes the test methods. An effective way to assess the long-term stability of a test method is to periodically submit “blind control” samples (also referred to as reference samples) from a common source for analysis along with routine production samples in a way that the analyst cannot determine the difference between the production samples and the control samples. Nunnally and McConnell (2007) conclude “…there is no better way to understand the true variability of the analytical method”.

The control samples are typically tested 2-3 times (depending on the test method) at a given point in time. The sample averages are plotted on a control chart to evaluate the stability (reproducibility) of the method. The standard deviations of the repeat tests done on the samples are plotted on a control chart to assess the stability of the repeatability of the test method.

Another useful analysis to perform is to do an analysis of variance of the control sample data and compute the % long-term variation which measures the stability of the test method over time. Long term variation variance components < 30% are generally considered good with larger values suggesting the method may be having reproducibility issues (Snee and Hoerl 2012). 

It is concluded that using QbD concepts, methods and tools improves test method performance and reduces the risk of poor manufacturing process performance and defective pharmaceuticals reaching patients. Risk is reduced as the accuracy, repeatability and reproducibility increases. Reduced variation is a critical characteristic of good data quality as reduced variation results in reduced risk.

Screening experiments followed by optimization studies is an effective way to design effective test methods. Measurement processes can be controlled using control samples and control charts and analysis of variance techniques. Measurement quality can be improved using Gage Repeatability and Reproducibility studies. Robust measurement systems can be created using statistical design of experiments. Product variation studies that separate sampling and process variation from test method variation is an effective way to determine the root cause of process variation problems.

Borman, P., M., etal (2007) “Application of Quality by Design to Analytical Methods”, Pharmaceutical Technology, October 2007, 142-152.
Box, G. E. P. , J. S. Hunter and W. G. Hunter (2005), Statistics for Experimenters,  2nd Edition, John Wiley and Sons, New York, NY, 345-353
Montgomery, D. C. (2013), Design and Analysis of Experiments, 8th Edition, John Wiley and Sons, New York, NY, Chapter 13.
Nunnally, B. K. and J. S. McConnell (2007) Six Sigma in the Pharmaceutical Industry: Understanding, Reducing, and Controlling Variation in Pharmaceuticals and Biologics, CRC Press, Boca Raton, FL
Schweitzer, M., etal (2010) “Implications and Opportunities of Applying QbD Principles to Analytical Measurements”, Pharma Tech, Feb 2010, 52-59.
Snee, R. D. (1983), “Graphical Analysis of Process Variation Studies”, J. Quality Technology, 15, 76-88.
Snee, R. D. (2005) “Are We Making Decisions in a Fog? The Measurement Process Must Be Continually Measured, Monitored and Improved”, Quality Progress, December 2005, 75-77.
Snee, R. D. (2010) “Crucial Considerations in Monitoring Process Performance and Product Quality”, Pharmaceutical Technology, October 2010, 38-40.
Snee, R. D. and R. W. Hoerl (2012) “Going on Feel: Monitor and Improve Process Stability to Make Customers Happy”, Quality Progress, May 2012, 39-41
Youden, J. (1961) “Systematic errors in physical constants”. Physics Today, 14, No.9, 32-42.

Ronald D. Snee, PhD is founder and president of Snee Associates, a firm dedicated to the successful implementation of process and organizational improvement initiatives. He can be reached at