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 368
OP 376
DO 10.5731/pdajpst.2019.010843
VO 74
IS 4
A1 Anthony Bucci
A1 Le Ho
A1 Lauren Orme
A1 Qingyu Zeng
YR 2020
UL http://journal.pda.org/content/74/4/368.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 achieving CCS visual acceptance. CCI is essential for parenteral packaging and must be maintained throughout the entire sealed drug product life. In order 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 consideration of 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 paper, we demonstrated a comprehensive approach utilizing real CCS component dimensional data as a statistical input for CCS dimension stack-up calculations to calculate the actual CCS end performance window and the CCS's quantitative failure risk to determine the CCS's optimal sealing performance and visual acceptance under different stopper compression percentages. We examined two vial CCSs differing by the stopper as a case study. Each component was measured and included in comprehensive dimensional stack-up calculations. The resulting statistical distributions were used to examine component variability and stack-up assemblies at multiple stopper compressions and to identify the optimal CCS based on the performance window generated from the real data. Using this data-driven approach, we quantitatively identified that as little as 5% stopper compression difference could impact the CCS chosen. More importantly, comprehensive dimensional stack-up calculations can assist in selecting the best vial CCS and appropriate stopper compression, as well as troubleshoot processing concerns and ensure operation within the optimal CCS performance window.