By Ann Neuer 

January 31, 2017 | For the first time in twenty years, there is a major update to the international guideline for clinical trials. Released in November 2016, ICH-GCP E6(R2), the revised Good Clinical Practice (GCP) guideline from the International Conference on Harmonisation (ICH), emphasizes quality risk management, which depends on people and innovations in processes and technologies for success. This is a significant departure from the 1996 version, known as ICH-GCP E6(R1), which  created a unified standard for regulatory agencies from the European Union, Japan, and the United States to mutually accept clinical trial data, but it barely mentioned technology. It was a long ago paper-based world without smartphones, iPads, or Snapchat accounts, and the cloud was just a fantasy.

Linda Sullivan, Co-Founder and President of Metrics Champion Consortium, says, “R1 was very dated, so it did not reflect what has been happening in the industry in recent years. R2 acknowledges that technology has advanced to the point that it can now support processes and provide insightful data needed to adopt risk-based approaches to quality management.”

As Sullivan suggests, the new guideline, officially an Integrated Addendum to R1, aligns with a clinical trial world that has morphed dramatically since 1996. It confronts head-on the ongoing evolution in technology, whereby various solutions can now speak to each other and disparate datasets can be aggregated to provide actionable information. The resulting data enable the identification of risk that may be acceptable or may need to be mitigated. This capability was only hinted at in the first guideline.

But there is much more to it than simply embracing the latest technology. The intent is to implement solutions that change processes meant to avoid excessive protocol complexity and unnecessary data collection, leading to better data quality, and ultimately greater patient safety, which regulators expect. Specifically, R2’s many changes encourage more streamlined approaches to clinical trial design, conduct, oversight, recording, and reporting—a tall order.

Most of the high profile changes appear in Section 5—Quality Management—which describes steps needed to manage quality throughout all stages of the clinical trial continuum. They include:                

• Critical processes and data identification
• Risk identification
• Risk evaluation
• Risk control
• Risk communication
• Risk review
• Risk reporting 

A quick look at any of these sections shows the breadth of the process changes needed to achieve quality management. Critical process and data identification, for example, refers to sponsors using the protocol development phase to identify those processes and data that are critical to ensure subject protection and the reliability of trial results. Another factor, risk control, refers to the sponsor needing to determine which risks to reduce and which are acceptable. The section states that risk reduction activities may be incorporated into protocol design and implementation, monitoring plans, agreements between parties defining roles and responsibilities, systematic safeguards to ensure adherence to SOPs, and more.

Sullivan says that these changes can be implemented by determining which data are critical. “We are in an environment where nearly half the data collected in clinical trials are not critical for submission,” Sullivan commented. “R2 reinforces that you should focus on the quality of critical data you need to assess the efficacy and safety of your investigational product.” 

Looking at what matters becomes the framework for risk-based monitoring (RBM), which, according to section 5.18.3 of the guideline, should be developed by the sponsor, and emphasize monitoring of critical data and processes. The section notes further that RBM includes onsite as well as centralized monitoring, which can be used to analyze site characteristics and performance metrics. Essentially, the guideline harmonizes the RBM information from the FDA’s guidance on RBM and the Reflection Paper from the European Medicines Agency on risk-based quality management, both released in 2013, plus recommendations from various consortia and regulatory documents.

With so much emphasis on RBM, some stakeholders are assuming that it is synonymous with quality management, but that’s just the beginning. Kristin Mauri, Global Head, Risk-based Monitoring for Bioclinica, explains, “RBM in and of itself is not quality management. It’s just one way to help support overall quality management of a trial, which extends to all areas of trial execution. As the industry gains a better understand of Total Quality Management, I would expect these lines to become less blurred.”

Mauri adds further that the key challenge to adopting quality risk management is the degree of change management needed to revise how companies run their clinical trials. “Either they don’t have the right people to help manage the necessary process changes, or they can’t dedicate the time needed to do things differently. Larger companies have been trying to formalize their processes, but a lot of the smaller and mid-sized ones are hiring independent consultants to help with this shift, a step that documents the importance of the people component in implementing R2,” Mauri says.

Triumph Consultancy Services has designed a Quality Management System Maturity Model to determine organizational readiness for complying with the guideline. There are seven levels to the model, starting with being unaware of compliance, and ending with optimization. Duncan Hall, CEO of Triumph, says that aapproximately 60% of organizations are in the process of achieving compliance but have not yet done so, and less than 10% have actually achieved compliance. The final 30% are either aware of the requirements to become compliant and just haven’t started yet, or are unaware of the need in the first place.

The new guideline will have far-reaching consequences as risk management becomes the core of clinical trial operations, all in an effort to determine the critical processes and data as a clinical trial unfolds.