In a manner of speaking, knowledge is the currency of managing risk and an organization that is risk-focused will want to apply the best of what it knows to assess those risks, identify appropriate risk controls and evaluate the performance of those controls. An organization that effectively manages knowledge should be able to recognize and proactively apply new learnings to better anticipate risks. This is particularly important in the manufacture of medicinal products. Since the publication of ICH Q10 in 2010, QRM and KM have been positioned as coenablers to the Pharmaceutical Quality System. However, in practice these two disciplines have remained largely distinct and disconnected. This paper presents a novel way to consider Quality Risk Management (QRM) and Knowledge Management (KM) which represents their true interdependencies, and which has the potential to deliver more effective and risk-based control strategies in a more synergistic and effective manner. This paper advocates for the need to strengthen the relationship between QRM and KM. In order to better understand the synergistic relationship between QRM and KM, a Knowledge Management process model is first proposed to envision KM akin to the familiar representation of QRM in ICH Q9 . Following this model, a framework is presented in the form of a Risk-Knowledge Infinity Cycle which serves to visualise and understand the QRM-KM relationship. It is the authors belief that treating QRM and KM in this way has a variety of potential benefits for biopharmaceutical companies, including improved risk-based decision making, facilitating evidence-based risk reduction and increased process knowledge, leading to less uncertainty and subjectivity in QRM outputs. This should ultimately result in more effective risk-based control strategies and more reliable manufacturing processes, which potentially lead to increased protection – and other benefits including product availability and value – for patients.



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Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.