The ICH E6(R3) Expert Working Group (EWG) is currently working to revise the E6(R2) Good Clinical Practice (GCP) guidelines with a focus on the growing inclusion of diverse trial types and data sources in clinical trials. This includes the increasing use of new digital technologies in trials. The new guidelines will offer flexibility, when appropriate, to enable the use of technological innovations in trials and leverage it to identify and manage risks.
Xtalks also spoke with the team at Medidata to get their perspectives and insights on preparing for the new ICH E6(R3) rules.
In their webinar, Medidata discussed how the international guidance could affect clinical operations and how sponsors, CROs and sites can prepare their clinical development programs so they are aligned with new compliance requirements. They also talked about leveraging critical to quality factors to create focused indicators of quality that can be used to identify and manage risks to support robust decision-making while protecting patients.
They offered insight into how sponsors, CROs and sites can focus on quality factors at the time of trial design and prepare for the new rules.
Given the potentially significant impact of the changes, a work-in-progress draft of the guidelines was released in April 2021. The draft was released to foster transparency and a general understanding so that sites and CROs can prepare for the changes. The ICH says the principles are interdependent and should be considered in their entirety to assure ethical trial conduct, participant safety and reliable trial results.
Impact of ICH E6(R3) on Clinical Trial Operations: Technology and Quality By Design
Medidata says one of the most important impacts of ICH E6(R3) is the alignment with ICH E8(R1) to Quality by Design (QbD) principles, and the inclusion of technology to support enhanced efficiency in clinical development activities.
The impetus of the new revision 3 (R3) is about modernizing and renovating in line with today’s scientific and technological innovations, explains Fiona Maini, Principal of Global Compliance and Strategy at Medidata.
ICH E6(R3) emphasizes a QbD approach where quality should be proactively designed into a trial’s study protocols and processes. It is important that these elements be incorporated in the early planning phases of trial design, and across trial operations.
As such, in their webinar, the experts from Medidata discussed how sponsors, CROs and sites can focus on quality factors at the time of trial design and prepare for the new rules.
Maini says ICH E6(R3) remains aligned with R2 ICH E6(R2) with respect to establishing a culture of quality within organizations.
Quality in clinical trials can be thought about as the absence of errors that matter, describes Brian Barnes, Senior Director of Product Management at Medidata. He says, “Trial quality ultimately rests on having a well-articulated investigational plan.” This means a trial should have clearly defined objectives and associated outcome measures.
Prospective attention that can prevent essential errors, which may hinder the collection of important information, can significantly improve trial quality. These elements are best addressed during risk assessment activities prior to protocol development.
Medidata told Xtalks that as the industry witnessed during the COVID-19 pandemic, nearly every global regulatory body that released COVID-19–specific guidance addressed the importance of performing risk assessment activities.
Key ICH E6(R3) Changes
There are three main areas of consideration that will need to be addressed as per the latest round of revisions to ICH E6. These are:
- Data Management
All three areas are linked to the increasing adoption of digital technologies in trials. For example, data management systems will have to be updated to consider the digitization of trial settings and data ecosystems. This involves incorporation of digital technologies for remote monitoring, data collection and data sharing.
As new technologies are introduced, there will be new responsibilities involved with potentially more different types of roles. Diverse knowledge necessitates tapping into potentially new and varied pools of talent; for example, handling large amounts of digital data may require an array of data scientists.
Delegation of tasks and building clarity around those tasks is key for all parties involved in trial processes. For example, investigators and sponsors must both be clear on their responsibilities amidst changing trial environments and roles.
With the increasing dependence on existing technologies, and incorporation of new ones, there is increasing variety in the types of monitoring approaches as well as sponsor oversight. While aspects of decentralized trial models were beginning to be implemented slowly prior to the COVID-19 pandemic, the pandemic spurred the need for off-site monitoring and decentralization. For example, virtual monitoring or at-home visits became the new norms.
Handling the logistics involved in remote monitoring required rapid adoption of customized, off-site trial processes, which made tailored risk-based approaches that much more important.
Quality control can be implemented by having different sponsor representatives conduct quality and sponsor oversight in changing trial models driven by technology. This includes details around IT security and data protection, and who has the responsibility to ensure that data is fit for purpose.
In general, Barnes says, “In addition to the strong emphasis on QbD, the introduction of technology to ICH E6(R3) into the principles supports an improved and more efficient approach to trial design and conduct.”
He says technology has had a significant impact on clinical trial operations, and some of the best examples come from the development of COVID-19 vaccines using decentralized trial strategies.
“Without the contribution of technology, patient participation in clinical trials and oversight activities outside the clinical investigative site would not have been possible.”
The speed with which the therapies reached the market relied on remote data capture linked to real-time data insights that enabled fast decision-making and issue resolution aligned with the principles of ICH E6(R3).
And with this, there is recognition of the importance of performing risk assessment activities.
Overall, Maini says the ICH E6(R3) is a progressive step forward.
COVID-19 accelerated the changes that were happening pre-pandemic in the clinical trial space. It sped up the adoption of decentralized trial models with increased reliance on technology. Technology proved to be essential to moving to fully virtual or hybrid trial models as it allowed for the integration of systems to offer a holistic approach and to minimize loss of connections and data.
Barnes says, “Technology is a mechanism that can drive efficiencies in standard quality management systems and quality by design processes.”
ICH E6(R3) continues to emphasize following ICH E8(R1) with respect to the importance of driving efficiency across trial development, starting with building quality in trial design early, and moving those principles into ongoing study conduct as well.
Critical to quality factors help translate broad customer needs into defined, actionable, quantifiable performance requirements based on process parameters that determine the end parameters.
ICH E6(R3) also focuses on critical to quality factors to help the clinical trial industry understand what elements are critical and of priority. Moreover, ICH E6(R3) outlines that technology should be customized and fit-for-purpose for each trial design.
COVID-19 served as a catalyst to taking the next step towards enhancing clinical operations, explains Barnes. “We’re beginning to see some strong use cases with the vaccines that were being developed during the pandemic and really focusing on how technology can help support those fit-for-purpose activities.”
The implementation of technology must be thoughtful and really used to address specific issues, according to Barnes.
Hence digital oversight is important in the context of ICH E6(R3). With evolving technologies driving greater efficiencies and enhanced subject participation and engagement, digital oversight becomes key. Decentralization is certainly not a fragmentation of processes, but rather, it’s the convergence of concepts.
Execution of QbD elements involves the coming together of different concepts that need new forms of tailored and sophisticated oversight.
With the continuous accumulation and aggregation of large amounts of data, including the monitoring of subject safety in real time, AI and ML-based analytics can be leveraged to take real-time proactive measures.
Efficiencies also come from limiting potential handoffs that take place, explains Barnes. For example, you will want to limit data drops or data lags when conducting technology integration. Therefore, building collaborative workflows that optimize the connections between virtual and physical interactions between the sites and participants is key to efficiency and loss prevention.
Barnes says, “We shouldn’t be thinking about decentralization in terms of just the patient data collection but thinking of it more holistically. How does the sponsor or a CRO engage with both the investigative site and the participant really in one environment?” This is where digital oversight starts to answer that technology convergence question, he says.
Critical to Quality Factors
Designing quality into studies starts at the beginning with identification of key risk indicators (KRIs) and quality tolerance limits (QTL) that affect critical to quality factors. The configuration of KRIs and QTLs is critical in managing and supporting the site, country and even study level risks for a given trial.
A risk management system should capture critical data and critical processes, allowing for the flexibility to monitor data quality and trial process issues.
Josh Rochotte, Product Manager, Digital Oversight at Medidata explains that while risks already exist, using a tailored system built into the study allows you to have risks that are defined, described and categorized in order to aid in the review of data and findings.
“This will support a more holistic review process and allow better oversight for the trial,” he says.
In the webinar, Rochotte showed demos of risk assessment systems with QTL and KRI indicator setups.
It’s important to collect the probability of a risk, the impact it would have on results and the ease with which it can be detected in the system. These will ultimately support and inform the review process and categorization of criticality, explains Rochotte.
The study team should also understand what potential mitigations may need to be taken and their relevance to the risk in question. A control may be as simple as central monitors reviewing specific data, or it could be stronger like pausing recruitment.
KRI is a key feature of the system and should allow for endpoint justification and aid in the prevention of major findings. This can be achieved with cross-platform data sharing and system integrations. This allows a user to remain in one system and get data delivered on demand.
On the other hand, QTLs are thresholds or values associated with a parameter that is critical to quality and informed by the primary endpoints.
These indicators are used in analyses for defining upper and lower values for a threshold; this is then used to configure an algorithm to analyze input data, ask questions about the data and identify issues.
Digitized data flow in reporting is needed for trials of the future, says Rochotte. And while KRIs are indications that a risk may be occurring at a site, QTLs are identified at the trial level and if exceeded, may indicate systematic issues that can impact participant safety and would trigger an evaluation. Integration of data allows for its centralization, preventing data losses.
No One-Size-Fits-All Approach
The ICH (E6)R3 revision addresses the evolving landscape of clinical trials with respect to decentralized models, increased use of digital technologies and quality by design approaches.
While digital workflow solutions are generally advantageous and support strong cross-functional collaboration activities, they can become complicated if their configuration or design are overcomplicated. This is where fit-for-purpose principles are key in determining designs that optimize your workflow.
Merging existing technologies and workflows with new ones must be adapted to fit individual participant characteristics and the particular trial design, Barnes told Xtalks. “This highlights the core interaction between the application of technology with quality by design principles. Therefore, it comes as no surprise that the best way to address this interaction is to conduct a risk assessment during the protocol design phases and throughout trial operations.”
Customized, functional approaches that foster more efficient trial processes involve the integration of new technologies and associated digital risk oversight systems. There is no one-size-fits-all approach, as each trial must be designed around benefiting patients and minimizing risks.
Watch the webinar, Preparing for ICH E6 (R3) Good Clinical Practice Changes, for more information and insights on the latest ICH E6 revision.
This article was created in collaboration with the sponsoring company and the Xtalks editorial team.