RT Journal Article
SR Electronic
T1 Statistical Quality and Process Control in Biopharmaceutical Manufacturing - Practical Issues and Remedies
JF PDA Journal of Pharmaceutical Science and Technology
JO PDA J Pharm Sci Technol
FD Parenteral Drug Association (PDA)
SP pdajpst.2020.011676
DO 10.5731/pdajpst.2020.011676
A1 Nicolas Heigl
A1 Bernhard Schmelzer
A1 Franz Innerbichler
A1 Mahesh Shivhare
YR 2021
UL http://journal.pda.org/content/early/2021/03/15/pdajpst.2020.011676.abstract
AB Statistical quality and process controls (SQC and SPC) are used for monitoring, trending and ultimately improving biopharmaceutical manufacturing processes and operations. The purpose of this paper is to highlight characteristic features of bioprocess data, their impact on typical SQC and SPC applications, specifically control charts for individual observations (I-chart) and provide guidance on practical issues faced during application of SQC and SPC. Simulated data were used in an attempt to mimic bioprocess data by inducing inhomogeneity, non-stationarity, auto-correlation, and outliers. The first part of the paper highlights the role of within and overall standard deviation (SD) estimates for 3-sigma limits, consequences of autocorrelation and their impacts on frequently applied sensitizing rules for control charts, i.e. Nelson′s rules 1 - 4. The second part deals with the often asked question of how many observations are required for estimation of robust 3-sigma limits. In the third part five popular approaches for treating censored data (results below or equal to limit of quantification, ≤ LOQ) were compared and their impact on 3-sigma limits and Ppk estimates were assessed. Finally addressing the less mathematical needs of quality managers, the last section summarizes the typical issues faced by the practitioner in the application of SQC and SPC and provides remedies for setting up robust and efficient control charts for biopharmaceutical process monitoring. Overall, this study shows that process monitoring and subsequent assessment without taking into consideration this atypical nature of biopharmaceutical process can lead to increased false alarm rates thus impacting the batch release or even possibility of rejecting good batches.