A failure mode and effect analysis (FMEA)-based approach for risk assessment of scientific processes in non-regulated research laboratories
2020
Nowadays, Quality Management tools such as failure mode and effect analysis (FMEA) are widely used throughout the aeronautical, automotive, software, food services, health care and many other industries to sustain and improve quality and safety. The increasing complexity of scientific research makes it more difficult to maintain all activities under control, in order to guarantee validity and reproducibility of results. Even in non-regulated research, scientists need to be supported with management tools that maximize study performance and outcomes, while facilitating the research process. Frequently, steps that involve human intervention are the weak links in the process. Risk analysis therefore gives considerable benefit to analytical validation, assessing and avoiding failures due to human error, potential imprecision in applying protocols, uncertainty in equipment function and imperfect control of materials. This paper describes in detail how FMEA methodology can be applied as a performance improvement tool in the field of non-regulated research, specifically on a basic Life Sciences research process. We chose as “pilot process” the selection of oligonucleotide aptamers for therapeutic purposes, as an example of a complex and multi-step process, suitable for technology transfer. We applied FMEA methodology, seeking every opportunity for error and its impact on process output, and then, a set of improvement actions was generated covering most aspects of laboratory practice, such as equipment management and staff training. We also propose a useful tool supporting the risk assessment of research processes and its outputs and that we named “FMEA strip worksheet.” These tools can help scientists working in non-regulated research to approach Quality Management and to perform risk evaluation of key scientific procedures and processes with the final aim to increase and better control efficiency and efficacy of their research.
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