风险分析(工程)
污染控制
控制(管理)
风险评估
质量(理念)
计算机科学
关键控制点
运营管理
可靠性工程
危害分析
污染
业务
工程类
计算机安全
生物
认识论
哲学
人工智能
生态学
作者
R. van der Galiën,A. L. Langen,Loe Jacobs,P. Sawant Raschdorf,Xing Ai,M. C. van Amsterdam
标识
DOI:10.5731/pdajpst.2024-003018
摘要
A Contamination Control Strategy (CCS) is a strategy that focuses on how to prevent contaminations with microorganisms, particles and pyrogens and manages risks to medicinal product quality and patient safety within a GMP facility. A CCS should reflect on all proactive and retrospective data within a sterile and/or aseptic and/or non-sterile manufacturing environment, to identify all sources of contamination and associated hazards and/or control measures. It should outline associated Quality Risk Assessments (QRAs), Critical Control Points (CCPs) and suggest necessary control measures. An effective way to perform QRA for CCS is adopting the Hazard Analysis Critical Control Point (HACCP) methodology, a proactive risk assessment tool. This tool can be ideally used to monitor all CCPs associated with various sources of contamination. To keep the CCS effective, it is highly recommended to have the CCS reviewed by a multidisciplinary team and updated periodically, and to re-review it as necessary in the event particular issues arise. An annual review of the CCS effectiveness can be performed by constructing an assessment tool in the form of a report. The report provides an overview of all proactive and retrospective Quality Management System (QMS) related data that may have an impact on the CCS. This report then acts as a tool to drive continual improvement of the manufacturing and control methods, as required per EudraLex Volume 4 GMP guidelines Annex 1. This article describes a practical application of setting up an annual CCS assessment within a pharmaceutical manufacturing company to keep the CCS effective. This assessment will facilitate the process of reviewing the effectiveness of all control measures over time and facilitates escalation of issues that are not under control.
科研通智能强力驱动
Strongly Powered by AbleSci AI