Crucial Considerations in Monitoring Process Performance and Product Quality

Routine, ongoing assessment of process performance and product quality is crucial to ensuring that high quality pharmaceuticals reach the patient in a timely fashion. In traditional solid-dosage pharmaceutical manufacturing, process data are routinely analyzed at two points in time to assess process stability and capability. During the production of each batch, process operators and quality-control departments collect data to ensure stability and capability and take appropriate remedial actions when needed. On a less frequent basis (i.e., monthly or quarterly), batch-to-batch variation is analyzed based on product parameters to assess the long-term stability and capability of the process. The author discusses this system, including key challenges, and describes a structured approach, including the role of quality by design (QbD), for operating the system effectively.

QbD considerations

Process monitoring and control. The industry and regulatory focus on QbD places an even greater emphasis on the quality of pharmaceutical products and the performance of the pharmaceutical-manufacturing processes. Process and product control are major building blocks of QbD (1). The strategic structure of the International Society for Pharmaceutical Engineering's (ISPE) Product Lifecycle Implementation Plan (PQLI) lists the "process performance and product quality monitoring system" as one of its critical elements (2).

Process stability and capability. Central to any system is the assessment of process stability and capability. Manufacturing processes that are stable and capable over time can be expected to consistently produce product that is within specifications and thereby cause no harm to patients due to nonconforming product. Stability and capability are described as follows (3, 4): A stable manufacturing process is a process that is in a state of statistical control as each batch of tablets is being produced and as batches of tablets are produced over time. A process in a state of statistical control consistently produces product that varies within the process control limits; typically set at the process average (X-Bar) plus and minus three standard deviations (SD) of the process variation for the parameter of interest. Separate control limits are set for each parameter (e.g., tablet thickness and hardness). Any sample value that falls outside of these limits indicates that the process may not be in a state of statistical control.

A capable process is one that consistently produces tablets that are within specifications for all tablet parameters (3, 4). A process-capability analysis compares the process variation to the lower and upper specification limits for the product. A broadly used measure of process capability is the Ppk index, or process performance index, which is discussed in greater detail later in this article.

A production process can include any one of the four combinations of stability and capability: stable and capable (desired state), stable and incapable, unstable and capable, and unstable and incapable (worst possible situation).

Process stability and capability are typically evaluated twice:

1) During the production of each batch to ensure that the process is in control and to identify when process adjustments are needed. Some key questions that need to be addressed during this analysis include:

• Is the batch production process stable during the production of the batch with no trends, shifts, or cycles present?

• Is the process capable of meeting specifications (i.e., are the process-capability indices acceptable)?

• Is the within-batch sampling variation small, indicating a stable batch production process?

2) Monthly or quarterly to ensure batch-to-batch control throughout a given year and between years. Some important questions that should be addressed during this analysis include:

• Is the batch-to-batch variation stable from year to year and within years with no shifts, trends, or cycles present?

• Is the batch-to-batch variation small?

These two analyses also help to assess the robustness of the process.

Control limit versus specification limit. Control limits are calculated from process data and applied to the process. Control limits are used to assess the stability of the process and to determine the need for process adjustments when out of control samples are detected. On the other hand, specification limits apply to the product. Specification limits are used to assess the capability of the process to produce a product that has the desired properties and characteristics.

A systems approach

It is generally agreed among industry that a systems-based approach enables operations to be more efficient and sustainable. Schematics of such systems are shown in Figure 1 for monitoring individual batches and in Figure 2 for monitoring batch-to-batch variation (5–7). The systems underlying Figures 1 and 2 have the following characteristics:

• Data are periodically collected from the process. Pharmaceutical manufacturing processes are often monitored using 30–60 minute samples.

• These data are used to monitor processes for stability and capability using control charts, process capability indices, analysis of variance, time plots, boxplots, and histograms.

• The analysis identifies when process adjustments are needed to get the process back on target.

• Records are kept on the types of problems identified. As significant problems are identified or problems begin to appear on a regular basis, the resulting issues and documentation are incorporated into process-improvement activities to develop permanent solutions.

Figure 1: Framework example for monitoring process stability and capability. ALL FIGURES ARE COURTESY OF THE AUTHOR

Process improvement can be effectively completed using the define, measure, analyze, improve, control (DMAIC) problem-solving and process-improvement framework (5, 6). The following section describes the tools typically used in this framework.

Figure 2: Framework example for monitoring batch-to-batch variation over time.

Assessment tools

As a general principle, it is rare that a manufacturing process that is stable and capable will produce a product that is out of specification. The primary purpose of a process monitoring system is to address the question: Is this process capable of consistently producing product that is within specifications over time? The statistical analyses conducted to answer this question are briefly described below. These methods are generally accepted and well documented in the literature (4).

Control-chart analysis. A control-chart analysis is used to assess the stability of a process over time. The Shewhart chart has been widely used to assess process stability since the 1930s. Other types of control charts are also useful for monitoring processes (4).

A stable process is a predictable process; a process whose product will vary within a stated set of limits. A stable process is sometimes referred to as being in "a state of statistical control" (3, 4). A stable process has no sources of special-cause variation—that is, effects of variables are outside the process but have an effect on the performance of the process (e.g., process operators, ambient temperature and humidity, raw material lot).

The most commonly used indicator of special-cause variation is a process that has product measurements outside of the control limits which are typically set at X-Bar plus and minus three SD of the process variation for the parameter of interest (see Equation 1).

For example a process may be producing tablets with an average hardness of 4.0 kp and a standard deviation of 0.3 kp. The control limits are thus 4.0 +/–3(0.3) for a range of 3.1–4.9. Any tablet sample outside of that range is an indication that the process average may have changed and a process adjustment may be needed. Separate control limits are set for each parameter.

Figure 3 shows a control chart for a total weight of 10 tablets manufactured by two tablet presses. The graphic demonstrates that both presses are stable and in control and producing tablets with the same average weight and variation.

Figure 3: Control chart showing batch tablet weight produced using two presses (A and B). UCL is upper control limit and LCL is lower control limit.

Figure 4 shows a control chart for assay values for batches produced over a 3-year period. The process is stable through the middle of Year 2 and begins to decrease in Year 3. When a process adjustment is made, the batch assay values return close to the values observed in Year 1. This example is interesting because a process shift is shown, but none of the batch assay values are close to the assay specifications of 90–110%.

Figure 4: Control chart showing assay values of batches produced over three years. UCL is upper control limit and LCL is lower control limit.

Out-of-specification (OOS) and out-of-control (OOC) values require investigation. These values are not always caused by manufacturing problems, and may be caused by sampling errors, testing errors, or human administration errors such as recording or data keying. The causes of OOS and OOC measurements should be carefully considered when interpreting the OOC and OOS values and deciding on appropriate action.

Process-capability analysis. A process-capability analysis is conducted to determine the ability of the process to meet product specifications. The Ppk value represents the ratio of the difference between the process average and the nearest specification divided by three times the process SD (see Equation 2).

Where A is the upper specification minus the process average and B is the process average minus the lower specification. Two main statistics are used to measure process capability: percent of the measurements OOS and the process Ppk value. The interpretation of the Ppk value is summarized in Table I.

Table I: Summary of process performance index (Ppk value) interpretation.

Process capability indices of Ppk of 2.0 and higher are consistent with high-performance processes or robust processes. Figure 4 shows an example of process capability for tablet weight. In this case, the Ppk value is 1.91 (an excellent category) which is based on the distribution of tablet weights being a considerable distance from the lower and upper specifications for tablet weight.

Figure 5: Process capability for a tablet weight of Ppk = 1.91. LSL is lower specification limit and USL is upper specification limit.

When a process is robust, small process upsets will not create OOS product. Accordingly, small OOC signals do not result in OSS product. A process is said to be "robust" if its performance is not significantly uninfluenced by variations in process inputs (e.g., raw material lot), process variables (e.g., press force and speed), and environmental variables (e.g., ambient temperature and humidity).

Variance analysis. Another way to assess process stability is to study the variation in process performance that is caused by potential special-cause variation (e.g., tablet presses, raw-material lots, process operating teams). Analysis of variance (ANOVA) enables one to identify variables that can increase variation in tablet parameters and that may produce OOS product (see Figure 6).

Figure 6: Boxplots showing tablet hardness for two tablet presses (X and Y).

The boxplot in Figure 6 shows the distribution of tablet hardness values for a batch of tablets produced by two different tablet presses (X and Y). Press X has a wider hardness distribution than Press Y, yet none of the hardness values are outside the hardness specification of 1–6 kp. With this data in mind, the process operators can determine whether to make process adjustments.

After statistical significance of a comparison (e.g., average of Tablet Press X versus average of Tablet Press Y) is established, the practical significance of the difference in average values must be considered. This assessment is frequently carried out by expressing the observed difference in average values as a percentage of the overall process average. Subject-matter expertise is used to evaluate the practical importance of the observed percent difference.

Nested analysis of variance is another form of ANOVA used to assess process stability. Nested ANOVA can estimate the portion of the total variation in the data attributed to various sources of variation. Typically, the larger the percent of the total variation attributed to a source of variation, the more important is the source of variation. Low amounts (< 30%) of long-term variation as determined by a nested ANOVA indicate a stable process.

Process monitoring systems

The author's experience has shown that there are important considerations to take into account when designing, implementing, and operating process monitoring systems. The first of these is process understanding—that is, a deep knowledge of the variables that drive the process, which enables the accurate prediction of process performance. Effective application of QbD will result in better process understanding.

It is also crucial to understand the sources and magnitudes of measurement variation, in particular the repeatability and reducibility of the measurement of the process parameters. Gage R&R studies are an effective method for measuring the repeatability and reproducibility of the measurement methods used (6). Ruggedness studies are effective for determining method robustness for measuring typical variations that occur during the routine use of the method (8).

A systematic method is necessary to keep track of special-cause variation and to determine whether a systemic problem exists. This information can then be used to improve the process.

Two problems often observed related to monitoring systems include data not being analyzed routinely, and not taking action when significant sources of variation are identified. Regular management review and accountability can address both problems.

When a systematic approach is used, including regular review and action, the result is effective process monitoring. More importantly, high-quality pharmaceuticals are provided to the patient.

Ronald D. Snee, PhD, is founder and president of Snee Associates, 10 Creek Crossing, Newark, DE 19711, Ron@SneeAssociates.com

References

1. R.D. Snee, Pharm. Technol. 33 (10), web exclusive, (2009).

2. J.C. Berridge, Pharm. Engin. 36–39 (2009).

3. J. Oakland, Statistical Process Control (Elsevier, New York, NY 2008).

4. D.C. Montgomery, Introduction to Statistical Quality Control, 6th Edition (John Wiley and Sons, New York, NY, 2009).

5. R.D. Snee and R.W. Hoerl, Leading Six Sigma—A Step by Step Guide (FT Press, Prentice Hall, New York, NY, 2003).

6. R.D. Snee and R.W. Hoerl, Six Sigma Beyond the Factory Floor: Deployment Strategies for Financial Services, Health Care and the Rest of the Real Economy (FT Pearson, Prentice Hall, New York, NY, 2005).

7. R.D. Snee and E. C. Gardner, Quality Progress 56–59 (2008).

8 M. Schweitzer et al., Pharm. Technol. 34 (2) 52–59 (2010).