Department of Clinical Immunology, Amgen, Thousand Oaks, California 91320, USA.
AAPS J. 2010 Mar;12(1):79-86. doi: 10.1208/s12248-009-9166-4. Epub 2009 Dec 10.
The immunogenicity immunoassay validation process ensures development of a robust, reproducible method. However, no matter how well developed, validated, and maintained a method is, in the course of running a large number of samples over time, it is not uncommon to see bad reagents, poorly calibrated equipment, personnel errors, or other unknown and unpredictable factors that have an impact in the performance of the method and quality of the sample results. The immunogenicity immunoassay thus needs to be closely monitored with an internal statistical quality control process overtime to ensure a consistent and reliable output. The statistical process control has been widely applied to monitor manufacturing processes and in clinical laboratories. Its application to immunogenicity immunoassays is relatively novel. Limited guidance is available to implement the process to monitor semiquantitative immunogenicity immunoassay performance. Here, we have performed a suitability evaluation for process control charts with actual laboratory data from three immunogenicity immunoassay methods each utilizing a different technology platform. Additionally, a panel of prepared samples designed to assess long-term method performance were periodically evaluated for over a year. Finally, we make recommendations for an internal quality control process based on the results of these evaluations.
免疫原性分析验证过程可确保方法的稳健性和重现性。然而，无论方法开发、验证和维护得多么完善，随着时间的推移，在处理大量样本的过程中，也可能会出现试剂质量差、仪器校准不良、人员失误或其他未知和不可预测的因素，这些因素会影响方法的性能和样本结果的质量。因此，需要通过内部统计质量控制过程来密切监测免疫原性分析，以确保始终如一、可靠的输出。统计过程控制已广泛应用于监测制造过程和临床实验室。将其应用于免疫原性分析相对较新。目前，可用的实施该过程以监测半定量免疫原性分析性能的指导有限。在这里，我们使用来自三种免疫原性分析方法的实际实验室数据，对过程控制图进行了适用性评估，这些方法各自利用不同的技术平台。此外，还定期评估了设计用于评估长期方法性能的一系列备检样本，为期一年多。最后，我们根据这些评估结果为内部质量控制过程提出了建议。
