Yu Zhao, Changqin Hu, Shangchen Yao, Lihui Yin, Xiaomei Ling. A strategy for population pharmaceutical quality assessment based on quality by design[J]. Journal of Pharmaceutical Analysis, 2021, 11(5): 588-595. doi: 10.1016/j.jpha.2020.11.001
Citation:
Yu Zhao, Changqin Hu, Shangchen Yao, Lihui Yin, Xiaomei Ling. A strategy for population pharmaceutical quality assessment based on quality by design[J]. Journal of Pharmaceutical Analysis, 2021, 11(5): 588-595. doi: 10.1016/j.jpha.2020.11.001
Yu Zhao, Changqin Hu, Shangchen Yao, Lihui Yin, Xiaomei Ling. A strategy for population pharmaceutical quality assessment based on quality by design[J]. Journal of Pharmaceutical Analysis, 2021, 11(5): 588-595. doi: 10.1016/j.jpha.2020.11.001
Citation:
Yu Zhao, Changqin Hu, Shangchen Yao, Lihui Yin, Xiaomei Ling. A strategy for population pharmaceutical quality assessment based on quality by design[J]. Journal of Pharmaceutical Analysis, 2021, 11(5): 588-595. doi: 10.1016/j.jpha.2020.11.001
1.NMPA Key Laboratory for Quality Research and Evaluation of Chemical Drugs, National Institutes for Food and Drug Control, Bejing, 102629, China;
2.Acedemy for Advanced Interdisciplinary Studies, Peking University, Bejing, 100088, China;
3.Peking University Health Science Center, Peking University, Beijing, 100191, China
Funds:
The National Major Scientific and Technological Special Project for ‘Significant New Drugs Development’ (Grant No.: 2017ZX0901001-008) provides support for this study.
From a regulatory perspective, drug quality consistency evaluation must concern different processes used for the same drug. In this study, an assessment strategy based on quality by design (QbD) was developed for population pharmaceutical quality evaluation. A descriptive analysis method based on QbD concept was first established to characterize the process by critical evaluation attributes (CEAs). Then quantitative analysis method based on an improved statistical process control (SPC) method was established to investigate the process indicators (PIs) in the process population, such as mean distribution, batch-to-batch difference and abnormal quality probability. After that rules for risk assessment were established based on the SPC limitations and parameters. Both the SPC parameters of the CEAs and the risk of PIs were visualized according to the interaction test results to obtain a better understanding of the population pharmaceutical quality. Finally, an assessment strategy was built and applied to generic drug consistency assessment, process risk assessment and quality trend tracking. The strategy demonstrated in this study could help reveal quality consistency from the perspective of process control and process risk, and further show the recent development status of domestic pharmaceutical production processes. In addition, a process risk assessment and population quality trend tracking provide data-based information for approval. Not only can this information serve as a further basis for decision-making by the regulatory authority regarding early warnings, but it can also reduce some avoidable adverse reactions. With continuous addition of data, dynamic population pharmaceutical quality is meaningful for emergencies and decision-making regarding drug regulation.