By Scott Tarpley, Consultant, Light Pharma, Inc.Socrates once wrote that the unexamined life is not worth living. The same holds true for manufacturing processes: the unexamined process may still make product, but it cannot be improved.Over the last 70 years, riding the “continuous quality improvement” wave set in motion by Walter Shewhart and W. Edwards Deming in the 1920s, many different industries have worked hard to better understand their processes in order to improve them.A critical tool for improving process understanding is “process capability analysis,” which has proven its worth in automotive, aerospace and semiconductor manufacturing. Used to gauge process performance, the technique is now being used outside of manufacturing, to analyze transactional and design processes. Process capability analysis is also making its way into the pharmaceutical industry, where drug manufacturers and regulatory agencies are using it to characterize processes. This article introduces a roadmap for conducting a pharmaceutical process capability analysis, and explains some of the basic metrics involved.“Process capability” can be defined as the comparison of the “Voice of the Customer” (VOC) with the “Voice of the Process” (VOP). VOC, based upon customer requirements, is defined by the specification limits of the process, which are fixed, while VOP is defined by control limits, which are based on performance data and vary over time.As any process is improved, its variability or “spread” tightens and it is more closely centered between specifications; as this occurs, process capability increases dramatically. A metric, Cpk, was developed several decades ago to compute this comparison between control and specification limits. However, simply computing the Cpk won’t provide any value. Several basic diagnostics must first be performed and analyzed to determine data quality.Defining upstream specificationsAny process capability road map must start with well-defined specifications. Definition can be challenging for upstream specifications, though. After all, specifications developed during the drug development process are based on science, and experimentation. For example, release parameter specifications are generally proven through physico-chemical studies in the laboratory.Upstream in the process, however, specifications often aren’t based on science. For example, process engineers involved in drug manufacturing are likely to have less confidence in the specifications set for key raw materials or in-process parameters than they do in the final test specifications designed to meet the consumer’s needs.If a process capability analysis is to be meaningful, the team carrying out the project must determine how much confidence they have in specifications for upstream parameters. Confidence is critical, because process capability metrics assume that specifications are targeted properly with associated tolerances. Examples would be the Specific Surface Area (SSA) or Particle Size specifications of excipients such as lactose. Are they targeted correctly? Are analysts comfortable with the tolerances set around that target? Further, have correlations been proven between these parameters and downstream quality attributes such as assay, dissolution, or content uniformity?Measuring the measurement systemThe next step in the road map is a “Measurement Systems Analysis” (MSA), designed to assure that the system is capable of measuring the process. The measurement system does not mean the analytical devices alone, but the entire measurement process.For instance, in a dissolution test, an MSA would not be limited to the analytical equipment in the lab used to perform the actual test. It would also consider other variables or contributing factors, including:
Cpk-U = (USL Mean)/3s Cpk-L = (Mean LSL)/3s Where s = standard deviation (of process data)If the VOP is within the VOC, or the control limits are within the specification limits, this means process capability is strong. If either control limit (UCL or LCL) is outside either specification limit (USL or LSL), then the process capability is weak. Therefore, a Cpk less than 1 indicates poor process capability. If Cpk-U = 1, thenâ¦ 1 = (USL Mean)/3s andâ¦ USL Mean = 3s andâ¦ USL = Mean + 3s when Cpk-U = 1 s = standard deviationThe right hand side of the equation, Mean + 3s, is also known as the Upper Control Limit (UCL). Therefore, when Cpk-U = 1, the UCL is located exactly at the USL. In effect, VOP data are manifesting themselves precisely at the borderline of what the customer will accept. If the Cpk-U is less than 1, the UCL is outside the USL. Of course, as the Cpk increases, the stronger the process capability.The same analysis could be performed for the Cpk-L.In summary, determining process capability provides far more insight into any pharmaceutical process performance than simply computing the percentage of batches that pass or fail each year. Remember that high process capability guarantees a high percentage of passing batches, but a high percentage of passing batches cannot guarantee high process capability.Process capability analysis is not the only technique available for improving process understanding. However, given FDA’s new science-based regulatory framework and the promise of “safe haven” for manufacturers that demonstrate process knowledge, the practice promises to become a more important tool for pharmaceutical manufacturing professionals in the future.About the AuthorJ. Scott Tarpley is a consultant with Light Pharma Inc. He has worked in quality engineering and manufacturing for 15 years across multiple industries, and has vast experience in Six Sigma, Total Quality Management, and other quality initiatives. He has a B.S. Management Science and an M.S. in Statistics, both from the Georgia Institute of Technology. He can be reached via email at [email protected]Editor's Note: All figures referred to in this story are contained in a .pdf file which may be obtained by clicking on the "Download Now" button below.
- the pH of the solution in the vessel where the tablet or capsule is being tested
- the length of time that the capsules have been absorbing moisture prior to testing
- the lab technician conducting the test -- his or her skill and other factors influencing performance.
- between gauges
- within measurements made by a single gauge
- between the gauge and the material being analyzed
- common cause
- special cause.
- have been a problem for a long time;
- have not been resolved, although many different tools have been applied; and
- elicit theories on causes and solutions from everyone in the plant — theories that cannot be proven.
- any data point is outside three standard deviations from the process mean
- seven consecutive data points are either increasing or decreasing (also called “trend”)
- seven consecutive data points are on the same side of the process mean (also known as “shift”).