OR WAIT null SECS
As market factors drive the rapid growth of decentralized clinical trials, organizations should embrace risk-based quality management to evaluate the new dimensions of risk and ensure effective oversight of disparate data sources, writes John Hall.
Virtual or decentralized clinical trials present a far more patient-centric approach to conducting clinical research, building convenience and flexibility into the process, and allowing participants to engage in trials from the comfort of their day-to-day environment.
This new clinical research paradigm presents a significant opportunity to alleviate many of the considerable bottlenecks in traditional site-based clinical trials. Adopting a decentralized approach can improve the speed of recruitment and the diversity of study populations, removing the geographical boundaries of conventional site-based recruitment. The use of digital technologies can facilitate more frequent data collection, allowing the researchers to capture fluctuations in disease signs and symptoms often missed between traditional periodic site visits. And the DCT model is also inherently flexible, offering multiple models for deployment (Figure 1), including both fully decentralized and hybrid models.
The DCT trend shows no sign of slowing down, with the model’s adoption boosted by the COVID-19 pandemic, the proliferation of new digital technologies for data collection and trial conduct, and the drive to deliver a more patient-centric approach to conducting clinical research.
According to a 2020 survey by Oracle and Informa Pharma Intelligence published at the height of the pandemic, more than three-quarters of researchers had implemented decentralized approaches, with 38% confirming that more than half of their studies are now decentralized.1 Furthermore, a recent analysis published in Clinical Trials Arena by GlobalData2 highlights the recent rapid adoption of decentralized trial components. In 2020, 673 interventional drug trials mentioned decentralized and virtual components in clinical registry protocols, and in 2021 the number had increased to 1,011. The uptake of DCTs is also forecasted to gain further momentum in 2022. Some 1,300 interventional trials are expected to be initiated with a virtual and/or decentralized component, representing a 93% increase from 2020.
Although there has been a rapid acceleration in the adoption of DCTs, barriers remain to fully adopt the decentralized model. Patient care and ensuring reliable data quality were the key challenges to adoption cited by 50% and 59% of respondents in the Oracle survey,3 respectively. Challenges in ensuring patient safety and data quality in decentralized trials could arise as we engage new independent entities (such as home healthcare providers and patients themselves) to be sources of study activity and devolve the accountability for trial conduct outside the control of a conventional clinical site.
Risk-based quality management (RBQM) supports the centralized remote monitoring of clinical trial data. It is an approach to proactively identify, assess, control, and review risks (see Figure 2) to ensure data integrity and patient safety in clinical research. It has been formalized in the ICH Guideline for Good Clinical Practice E6(R2), released in 2016.4 RBQM includes components such as risk assessment, quality tolerance limits (QTLs), key risk indicators (KRIs), and centralized statistical monitoring, to mention a few. Rather than reviewing every data point collected in a clinical trial, it provides a framework for study teams to focus on the most critical data.
The adoption of RBQM delivers many benefits to study execution, including improved regulatory compliance and inspection readiness, reduced drug approval timelines, lower study execution costs through reduced on-site monitoring activities, and improved data quality through a more effective monitoring approach.
Like DCTs, the COVID-19 pandemic has equally impacted the adoption of RBQM. In an industry survey of 650 industry representatives in 2021, 81% of surveyed representatives said they were either implementing or have already fully adopted RBQM.5 Enacting COVID-specific guidance from the regulators and addressing the additional risks the pandemic introduced to clinical trials was a daunting task. Yet where organizations had already embraced RBQM before the onset of the COVID pandemic, they were better positioned to respond to the challenges it presented. Now, as monitoring visits continue to be conducted remotely, several pharma companies and CROs are making RBQM the default mode, thanks to its impact on improved trial execution resilience.
It may seem contradictory to combine the approaches of decentralized clinical trials and centralized monitoring, but these two approaches are very complementary.6 The RBQM framework can ensure that risks to participant safety or data integrity, because of adopting new digital technologies or decentralized processes, are effectively identified and evaluated. Furthermore, DCTs collect large quantities of data from multiple disparate sources and thus require a more centralized monitoring approach to ensure effective oversight of all data sources and associated processes. These opportunities are further explored below.
In a conventional clinical trial, the RBQM framework can effectively oversee risk across different dimensions of study activity, such as at the patient, site, and study level (see Figure 3).
In a decentralized trial setting, where trial activity is no longer centered around a physical clinical site, we engage new entities (such as home healthcare providers, caregivers, local pharmacies, and the patient themselves) to be sources for key study activity and data collection. However, these entities arguably have less structure and oversight than conventional clinical sites. Therefore, there is perhaps a more significant opportunity for increased variability to arise in collecting our data. RBQM and centralized monitoring tools can effectively identify and monitor such dimensions of risk. For example, the risk assessment process can be used to identify and categorize such risks, and central monitoring tools, such as KRIs and Central Statistical Analysis, can be used to monitor entities (e.g., home health care providers) and identify individual entities that are not performing at expected or observed levels in the trial.
Additionally, one of the key benefits of decentralized trials is offering trial participants optionality and empowering them to choose the route through which they engage with a clinical trial, whether through a physical clinical site or different methods of remote engagement (see Figure 4).
However, clinical trials can be unfamiliar experiences for many patients, and there can be considerable variations in their comfort and access to technology. As we provide that choice to patients within trials, there is the potential to add bias to critical data across these different modes of data capture, which should be considered. Again, the RBQM framework and centralized monitoring tools can be used to compare various methods of data collection (e.g., on-site vs. telemedicine vs. home health) or different types of sites (traditional brick and mortar sites vs. virtual sites) to identify emerging risks and differences across these modes of data capture and patient engagement.
The adoption of decentralized approaches, such as sensors to capture vital signs or ePRO to measure outcomes or the use of local labs, pharmacies, and imaging facilities, can result in the collection of clinical data from many disparate sources. With these approaches, the utility of traditional on-site monitoring techniques to confirm the quality and integrity of trial data at a site becomes less effective, as such data sources are not typically available in the patient’s on-site or medical record.7
Centralized monitoring, on the other hand, by its nature, has access to all the disparate sources of clinical trial data. It can aggregate and analyze all study data together to identify trends and associations between data sources and detect anomalies that may impact patient safety or data quality. This is perhaps more significant with some DCTs collecting large volumes of data frequently from trial participants, requiring oversight of millions if not billions of data points. A further significant advantage of centralized monitoring is that it also allows real-time data evaluation, making early interventions possible to address data anomalies.
In addition, digital technologies to collect data may have modality-specific failure scenarios (such as miscalibration) that on-site monitoring approaches can’t detect. Centralized monitoring technologies can access and analyze audit trail information to help evaluate the quality of the data by assessing the integrity of the processes and technology used to collect the data.
Critical work is being done to introduce industry-wide practices to standardize DCTs, enabling the model's adoption at an unprecedented rate within a sector that has traditionally been slow to support emerging trends. Several industry associations, working groups, and public-private partnerships such as ACRO, CTTI, DTRA, and DiMe are working to develop standard definitions, tools, and templates for the design and conduct of DCTs, share examples of best practices and evidence of value, and address gaps in global regulatory guidance.
Similarly, such organizations such as TransCelerate, ACRO, CTTI, and MCC have had a similar impact on the standardization of the approach to adopting RBQM across the industry.
The adoption of DCT goes hand in hand with the adoption of RBQM and a more centralized monitoring strategy. Together they can improve patient engagement, diversity, and retention, boost trial resilience, and enhance data quality, all at lower development costs and shorter timescales.However, to seize the opportunities a combined DCT & RBQM approach offers, there is still work to be done. As the pace of DCT adoption continues to grow, there is a pressing need for greater use of centralized monitoring, enabling the effective aggregation and oversight of disparate data sources, coupled with greater adoption of RBQM to ensure successful implementation of DCT components.
John Hall, SVP, EMEA & APAC, CluePoints