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Part 1 of this article series demonstrates, using real-world process data, that the four fundamental assumptions underlying the classical Shewhart control charts—randomness, independence, constant average, and constant variation—are often not met.
The European Union (EU) good manufacturing practice (GMP) and FDA regulatory documents require manufacturers to monitor pharmaceutical and biopharmaceutical product quality to ensure that a “state of control” is maintained throughout the lifecycle of new and legacy products during the third process validation stage called “continued process verification (CPV)” or “ongoing process verification (OPV)”. Indeed, EU GMP Annex 15 clearly states that, “Manufacturers should monitor product quality to ensure that a state of control is maintained throughout the product lifecycle with the relevant process trends evaluated”. Thus, regulatory agencies expect manufacturers to implement a CPV/OPV plan.
The implementation of Stage 3 of the manufacturing process validation is translated into establishing an ongoing CPV/OPV program, which allows identification of CPV signals and defining types of responses to these signals. These CPV signals can in theory be detected by evaluating process data plotted on Shewhart charts, also called process-behavior charts, and scrutinizing them with Nelson rules, also referred to as detection rules or runs tests in StatGraphics software (Stagraphics Technologies Inc., USA) program. However, the validity of these rules holds when the fundamental assumptions underlying the classical Shewhart control charts are met. Otherwise, applying traditional statistical process control (SPC) rules on real-world process data would lead to excessive false signal alarms, which in turn would lead to futile investigations aimed supposedly at assigning causes to these apparent process deviations.
This series of papers will review and demonstrate examples of pharmaceutical process data that the SPC fundamental conditions are often not met; explain the regulatory expectations regarding “state of control”; and suggest practical SPC tools that minimize false alarm signals.
It is shown that collecting, charting, and evaluating product and process data under relaxed and adjusted SPC rules allow a practical and streamlined implementation of the CPV/OPV program.
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Submitted: March 3, 2021
Accepted: May 3, 2021
Raphael Bar, PhD, firstname.lastname@example.org, is a consultant at BR Consulting in Ness Ziona, Israel.
Vol. 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).