RT Journal Article
SR Electronic
T1 A Vial Container Closure System Performance Optimization Case Study Using Comprehensive Dimensional Stack-Up Analyses
JF PDA Journal of Pharmaceutical Science and Technology
JO PDA J Pharm Sci Technol
FD Parenteral Drug Association (PDA)
SP pdajpst.2019.010843
DO 10.5731/pdajpst.2019.010843
A1 Anthony Bucci
A1 Le Ho
A1 Lauren Orme
A1 Qingyu Zeng
YR 2020
UL http://journal.pda.org/content/early/2020/02/14/pdajpst.2019.010843.abstract
AB Compatible vial container closure system (CCS) components in combination with a proper capping process are crucial to ensuring reliable performance, maintaining container closure integrity (CCI), and CCS visual acceptance. CCI is essential for parenteral packaging and must be maintained throughout the entire sealed drug product life. To build the most robust CCS performance, many variables, including component selection, fit, function and capping processes must be set according to the actual dimensions of the CCS components used. However, conventional CCS stack-up calculations are based on dimensional engineering data and its tolerance from CCS component drawings without considering the real statistical distributions and their resultant impact on the risk of CCS end performance. CCS dimensional variations may lead to capping failure, resulting in CCS visual defects, CCI failure, and potentially costly destruction of an entire CCS production batch. In this work, we demonstrated a comprehensive approach utilizing real CCS component dimensional data as statistic input for CCS dimension stack up calculations to calculate the actual CCS end performance window and its quantitative failure risk to determine its optimal sealing performance and visual acceptance under different stopper compression percentages. We examined two vial container closure systems as a case study differing by a stopper. Each component was measured and included in comprehensive dimensional stack up calculations. The resulting statistical distributions were used to examine component variability, stack up assemblies at multiple stopper compressions, and how to identify the optimal CCS based on the performance window generated from real data. Using this data-driven approach, we quantitatively identify as little as five percent stopper compression difference can impact the chosen CCS. More importantly, comprehensive dimensional stack up calculations can assist in selecting the best vial CCS, appropriate stopper compression, as well as troubleshoot processing concerns and ensure operation within the optimal CCS performance window.