The first part of this series of articles demonstrates, using real-world process data, that the four fundamental assumptions underlying classic Shewhart control charts (randomness, independence, constant mean and constant variation) do not are often not met.
European Union (EU) Good Manufacturing Practices (GMP) and FDA regulatory documents require manufacturers to monitor the quality of pharmaceuticals and biopharmaceuticals to ensure a “state of control” is maintained throughout the life cycle of new and old products during the third validation step of the process. called “Continuous Process Verification (CPV)” or “Continuous Process Verification (OPV)”. Indeed, Annex 15 of the EU GMP clearly states that “Manufacturers must monitor product quality to ensure that a state of control is maintained throughout the product life cycle with trends in relevant processes evaluated ”. Thus, regulators expect manufacturers to implement a CPV / OPV plan.
The implementation of step 3 of the validation of the manufacturing process results in the establishment of an ongoing CPV / OPV program, which allows the identification of the CPV signals and the definition of the types of responses to these signals. . These CPV signals can in theory be detected by evaluating process data plotted on Shewhart maps, also called process behavior maps, and examining them with Nelson rules, also called detection rules, or by running tests in StatGraphics software (Stagraphics Technologies Inc., USA) program. However, the validity of these rules holds when the fundamental assumptions underlying classic Shewhart control charts are satisfied. Otherwise, applying traditional Statistical Process Control (SPC) rules to real-world process data would lead to an excessive number of false red flags, which in turn would lead to futile investigations purportedly aimed at attribute causes to these apparent process deviations.
This series of articles will examine and demonstrate examples of pharmaceutical process data that the basic conditions of SPC are often not met; explain regulatory expectations regarding “state of control”; and suggest practical SPC tools that minimize false alarms.
Collecting, mapping and evaluating product and process data according to relaxed and adjusted SPC rules has been shown to enable practical and streamlined implementation of the CPV / OPV program.
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Submitted: March 3, 2021
Accepted: May 3, 2021
About the Author
Raphael Bar, PhD, firstname.lastname@example.org, is a consultant at BR Consulting in Ness Ziona, Israel.
Flight. 45, no.10
When referring to this article, please cite it as R. Bar, “Practical SPC Rules in the Real World of an Ongoing Process Verification Plan: Part 1. Conventional SPC Rules and Pharmaceutical Process Data” Pharmaceutical technology 45 (10) 40-47 (2021).