Zhao Yu, Hu Changqin, Yao Shangchen, Yin Lihui, Ling Xiaomei
NMPA Key Laboratory for Quality Research and Evaluation of Chemical Drugs, National Institutes for Food and Drug Control, Beijing, 102629, China.
Acedemy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100088, China.
J Pharm Anal. 2021 Oct;11(5):588-595. doi: 10.1016/j.jpha.2020.11.001. Epub 2020 Nov 4.
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.
从监管角度来看，药品质量一致性评价必须关注同一药品所采用的不同生产工艺。在本研究中，开发了一种基于质量源于设计（QbD）的评估策略，用于总体药品质量评价。首先建立了一种基于QbD概念的描述性分析方法，通过关键评价属性（CEA）来表征生产工艺。然后建立了基于改进的统计过程控制（SPC）方法的定量分析方法，以研究工艺总体中的过程指标（PI），如均值分布、批次间差异和异常质量概率。之后，基于SPC的局限性和参数建立了风险评估规则。根据交互试验结果，将CEA的SPC参数和PI的风险进行可视化，以更好地了解总体药品质量。最后，构建了一种评估策略，并将其应用于仿制药一致性评价、工艺风险评估和质量趋势跟踪。本研究中展示的策略有助于从过程控制和过程风险的角度揭示质量一致性，并进一步展示国内药品生产工艺的发展现状。此外，工艺风险评估和总体质量趋势跟踪为批准提供了基于数据的信息。这些信息不仅可以作为监管机构进行早期预警决策的进一步依据，还可以减少一些可避免的不良反应。随着数据的不断增加，动态的总体药品质量对于药品监管中的突发事件和决策具有重要意义。
