Quality risk analysis in a cGMP environment: multiple models for comprehensive failure mode identification during the computer system lifecycle

Abstract

Pharmaceutical quality systems use various inputs to ensure product quality and prevent failures that might have patient consequences. These inputs are generally data from failures that have already occurred, for example process deviations or customer complaints. Risk analysis techniques are well-established in certain other industries and have become of interest to pharmaceutical manufacturers because they allow potential quality failures to be predicted and mitigating action taken in advance of their occurring. Failure mode and effects analysis (FMEA) is one such technique, and in this study it was applied to implement a computerized manufacturing execution system in a pharmaceutical manufacturing environment. After introduction, the system was monitored to detect failures that did occur and these were analyzed to determine why the risk analysis method failed to predict them. Application of FMEA in other industries has identified weaknesses in predicting certain error types, specifically its dependence on other techniques to model risk situations and its poor analysis of non-hardware risks, such as human error, and this was confirmed in this study. Hierarchical holographic modeling (HHM), a technique for identifying risk scenarios in wide-scope analyses, was applied subsequently and identified additional potential failure modes. The technique for human error rate prediction (THERP) has previously been used for the quantitative analysis of human error risk and the event tree from this technique was adapted and identified further human error scenarios. These were input to the FMEA for prioritization and mitigation, thereby strengthening the risk analysis in terms of failure modes considered.

• Computer system
• failure modes and effects analysis
• hierarchical holographic modeling
• Pareto analysis
• risk analysis
• technique for human error rate prediction

Acknowledgements

The lead author wishes to thank the personnel of Rottapharm Limited for their assistance with this study, in particular Àine Tobin, Christine O’Sullivan and Padraig McCrum and the members of the MES project team who helped generate much of the data; to Niall Horton and Paul Ludorf for providing the opportunity to work on this project and to Patrick Garrahy, under whose direction the MES project was initiated and successfully concluded.