Raphael Bar, PhD, rbar@netvision.net.il, is a consultant at BR Consulting in Ness Ziona, Israel.

Raphael Bar

This paper demonstrates how assay data sets of a formulated liquid product, drug substance, and an impurity, obtained from combined accuracy-precision studies, are evaluated to calculate statistical intervals (prediction and tolerance intervals) and to graphically display the total analytical error (TAE) to account for the systematic and random errors. Inclusion of either the statistical intervals or the TAE within the allowed accuracy limits is an indication of a successful validation of the accuracy and precision method characteristics. This paper subsequently evaluates the capability of common procedures of pharmaceutical analysis to accommodate acceptable TAE and statistical intervals. While considering typical values of standard deviations (0.5–2.5% relative standard deviation) of chromatographic tests and typical specifications (±2, ±5, or ±10%) of pharmaceuticals, it is shown that a substantial portion of specification tolerance is taken up by either the statistical intervals or by the TAE, particularly in cases of narrow specifications and imprecise procedures.

Raphael Bar, is a Consultant at BR Consulting, Israel, 


Vol. 47, No. 5
May 2023
Pages: 32–41, 45

When referring to this article, please cite it as Bar, R. Total Analytical Error in Validation of Analytical Procedures of Pharmaceuticals. 

Articles

This paper demonstrates how assay data sets of a formulated liquid product, drug substance, and an impurity, obtained from combined accuracy-precision studies, are evaluated to calculate statistical intervals (prediction and tolerance intervals) and to graphically display the total analytical error (TAE) to account for the systematic and random errors. 

Total Analytical Error in Validation of Analytical Procedures of Pharmaceuticals

Part two of this article series shows how traditional statistical process control rules can be relaxed or adjusted to allow charting and evaluation of real-life data of pharmaceutical processes with a reduced number of false alarms. Ten practical tools or measures are suggested for minimizing false alarms among process data plotted as is on charts, provided concurrent process performance and product quality are demonstrated. This pragmatic approach reduces the number of futile investigations of apparent innocuous process deviations and streamlines the implementation of the third process validation stage of continued process verification or ongoing process verification.

Submitted: March 3, 2021
Accepted: May 3, 2021


Volume 45, Number 11
November 2021
Pages: 46–53

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 2. Practical SPC Rules to Apply on Pharmaceutical Process,” 

Part two of this article series shows how traditional statistical process control rules can be relaxed or adjusted to allow charting and evaluation of real-life data of pharmaceutical processes with a reduced number of false alarms. 

Practical SPC Rules in the Real World of an Ongoing Process Verification Plan: Part 2. Practical SPC Rules to Apply on Pharmaceutical Process Data

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.


Vol. 45, No. 10
October 2021
Pages: 40–47

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,” 

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.