P240: Impact of ICH E6(R3) on Data Managers: Changes in Risk-Based Quality Management for Clinical Trials
Poster Presenter
Cristiano Rocha Silva
Data Manager
Albert Einstein Israelite Hospital Brazil
Objectives
The ICH E6(R3) revision of Good Clinical Practice (GCP) guidelines have evolved significantly and marks a breakthrough in clinical trial oversight, emphasizing Risk-Based Quality Management (RBQM). This abstract outlines how the data manager's activities could be impacted by these changes
Method
A review addressing the evolution of GCP guidelines and RBQM principles was conducted. Examples from the literature were verified to illustrate how data managers could use Key Risk Indicators (KRIs) from RBQM to enhance data quality overview in clinical trials
Results
The concepts of risk management are not entirely new in clinical studies. The 2005 publication of ICH Q9 “Quality Risk Management” describes for the first time a systematic approach to quality risk management applicable to pharmaceutical manufacturing and quality assurance processes. Over the years, this idea has expanded to other fields related to clinical trials. In 2011, the FDA published a draft proposing a shift from traditional on-site monitoring to a risk-based approach, finalized in 2013. Similarly, the EMA released in 2013 a guidance focused on risk-based approaches to ensure data integrity and patient safety. After these guidance releases, the concept of RBQM gained prominence in 2016 with ICH E6(R2), which introduced the principles of risk-based monitoring in clinical trials. While R2 updated practices to accommodate technological advancements, the recent ICH E6(R3) revision brings a fundamental redesign, expanding risk-based quality management across all trial processes. All these fundamental changes are most likely to impact data management routines.
Throughout the conduct of a clinical trial, it is not possible to eliminate all protocol-related risks. However, the inherent risks can be mitigated, and the key risk indicators (KRIs) associated with the study can be monitored. While many risks would be assessed for in the overall risk mitigation plans from the multidisciplinary study team, some risks such as the complexity of the data, data flows, and planned technologies used for data collection, among others, could be one of the focus of the data management team. Several studies have shown that the use of well-developed KRIs during the study could minimize data discrepancies through centralized monitoring and reduce data entry errors in electronic data capture, among other improvements. These findings reinforce that in Clinical Data Management the KRIs must serve as critical tools for identifying and mitigating data quality risks
Conclusion
The whole purpose of RBQM is to ensure data quality, reliability, and participant safety. During the last couple of years, Clinical Data Management communities such as the Society for Clinical Data Management and the Association for Clinical Data Management have been fostering debates about the future of data management professionals and the potential subspecialization to the field of Clinical Data Science or Analytics in this ever-evolving role. Through the ICH E6(R3) it is possible to realize that the skill set of data management, data processing and data visualization has become increasingly essential to proactively identify, assess, and mitigate risks that could impact data quality. Although the RBQM adoption, as a framework, is a multidisciplinary endeavor, clinical data managers have the potential for a valuable contribution to the processes comprising risk-based approaches, such as the design of KRIs related to critical to quality data in clinical studies. Even though the DM is not usually responsible for leading RBQM activities, it is necessary to emphasize the contribution that the DM can provide in this context, be it as part of the risk design and specification work force or by assisting to build data evaluation tools, such as centralized monitoring reports. Finally, it is important to highlight the relevance of a close work with the DM teams, when using data from the study data collection tools to assess KRIs related to critical variables, revisiting the parameters ever so often when the protocol is updated and/or the case report form is modified, being fundamental to the strategy of maintaining risk-indicators and data oversight tools trustworthy
