Quality Risk Management is a tool that in recent years has proved to be useful and extremely versatile to support choices and decisions with a rational and scientifically supported process.
Its application has often made it possible to construct solid arguments on which to base important decisions that condition often complex scenarios.
Quality Risk Management techniques, risk based approaches, root cause identification, risk ranking and filtering are all solutions that have simplified the management of complex production systems, allowing to identify the critical parameters of the processes and the correct control strategy, to establish priorities and methods of intervention and, ultimately, to make more effective and informed decisions.
At the same time, however, numerous critical issues have also emerged in the application of the various risk analysis models. It is not always easy to choose the most suitable one from the many tools available, the one that best designs the context in which you operate.
In certain circumstances, it is not immediate to identify what kind of risks to analyze: only those that the patient taking the medicine runs directly or even indirect ones, perhaps caused by damage that hinders business continuity and therefore the availability of the product on the market?
Operators have often found themselves in the position of having to attribute acceptance criteria and risk acceptability matrices with a strong sense of subjectivity of choices. Choices that would then have affected the final result of the analysis itself.
With what criteria should the severity be attributed in an FMEA analysis? And how do you assign a scale of detectability or frequency that can be rational?
Often it is not clear what level of formalization to give to the analyzes: a simple report is enough or complex tables must be built, which take into account the multiple factors that impact even indirectly on the analysis, with the risk of exaggerating the effort employed and the sustainability of the whole process of applying Quality Risk Management?
The new version of the ICHQ9 (R1)
Precisely to respond to these difficulties, the members of the Management Committee in November 2020 approved a concept paper with some clear proposals for harmonization of the ICHQ9.
And it is on the basis of these proposals that it was decided to revise the ICHQ9 guideline and release it for public consultation one year later (November 18, 2021).
So what are the main changes to this guideline and how can they guide a more rational, sustainable and effective application of QRM?
Identify the dangers
First of all, in the text of the document the expression “risk identification” is replaced with “hazard identification“, making the entire risk analysis process more rational which therefore provides, in the preliminary assessment phase, the identification of hazards (hazard) and the damage associated with them (harm) and only to follow the analysis of the consequent risks.
This small change sheds light on the need to keep separate the (often confused) concepts of harm (e.g. use of contaminated materials), danger (product contamination) and risk (patient death).
And it is precisely in the accurate analysis of the relationship between damage, danger and risk based on the real knowledge of the data and information that describe the analyzed context that the new version of the ICHQ9 finds the solution to the high subjectivity that too often risks accompanying the application of QRM templates (par. 4.1).
Effective knowledge management
Once again we talk about the importance of having robust, reliable information from secure and validated sources to have quality output (par 4.3 risk evaluation). Safe data is needed to build a deep understanding of pharmaceutical processes, identify the real sources of damage, the likelihood that they will occur, and the consequences they may have on the product and, therefore, on the patient.
How can the process fail? What are its sources of variability? How much and when am I able to detect the damage if it occurs? How many times has this damage already occurred in the same or similar situations?
These are some of the questions to be answered in order to carry out an effective and useful risk analysis. And to answer these questions it is necessary to know in depth the context under analysis.
The need to know all the aspects that characterize the context under analysis is also emphasized in the new paragraph 5.1 Formality in quality risk management, where the concept of uncertainty is introduced, which in this context is understood as a lack of knowledge of the scenario. of reference.
Uncertainty, as defined in this paragraph, is considered one of the three elements – together with the importance of the decisions to be made (“importance”) and the complexity of the context under analysis (“complexity”) – which determine the degree of formality and effort necessary to carry out an adequate and sustainable risk analysis.
The greater the knowledge of the process under analysis, the less uncertainty, effort and formalization required to make the risk analysis adequate and robust. The guideline requires systematic approaches to acquire, analyze, archive and disseminate scientific information essential to generate knowledge, which is fundamental in all quality risk management activities (paragraph 5.1).
Uncertainty can be reduced through effective knowledge management that allows for the accumulation and use of information (both internal and external) to support risk-based decisions throughout the entire life cycle. We need solutions that collect and organize data and information in a safe and usable way.
We need systems for collecting process data from the field, and systems that effectively and efficiently record anomalies, non-compliant events and their frequencies, so as to be able to study them even afterwards and identify any unexpected correlations. Only in this way is the knowledge necessary for the application of effective and valid QRM techniques produced.
New technologies for the benefit of patients
Automation and new technologies are also referred to as measures to contain the risk of unavailability of the product on the market.
This new version of the guideline highlights an important concept that has been too often overlooked until now: the indirect impact that the interruption of the business continuity of medicinal products and the consequent unavailability of the product on the market can have on the patient.
As elements that contribute to the risk of product unavailability, there are not only the complexity of the supply chain, but also the capability of the production processes and their risk of drifts and variability that could impact on the quality of the product and limit its availability.
It is also essential to carry out a robust risk analysis of the infrastructures necessary for production (machines, plants, premises, systems, etc.) which takes into account their obsolescence and which allows the definition of appropriate maintenance or replacement programs.
The use of modern technologies, such as digitization and automation, is expressly indicated among the risk mitigation measures.
The responsibility of professionals
This new edition of the ICHQ9 guideline, which is now in step 2 (public consultation), opens up a modern and sustainable application of the QRM without losing focus on the aspects of scientificity and robustness necessary for the life science world.
The guideline, however, warns against the blind and uncritical application of the QRM process and clearly requires an appropriate use of quality risk management, which must be understood as a tool to facilitate the application of regulatory requirements and not to justify practices that are not acceptable or that replace appropriate communications between industry and regulatory authorities.
Once again it is the factory professionals who must identify, analyze and assess risks with a critical spirit, profound competence and intellectual honesty.
Related articles:
Tag, blockchain and AI, here is the supply chain of the future
