RBM Contributions to Oncology Research
It’s Breast Cancer Awareness Month and - in the U.S. - everything from landmark buildings to NFL players are wrapped in pink, to raise both awareness and funding for research.
With the American Cancer Society estimating 1.68 million new cases of all cancers this year in the U.S. alone, it’s no wonder the federal government initiated the Cancer Moonshot 2020. That $1 billion effort seeks to “win the war on cancer” by unleashing the potential of combination immunotherapies to treat cancer patients.
The National Cancer Institute reports U.S. investment of $5.21 billion for fiscal year 2016, which supports investigator-initiated research, clinical trials, and other initiatives. But, NCI notes its funding has plateaued over the past decade, as research costs have increased and inflation eats away at its buying power.
Despite best efforts to raise sufficient funds to fight cancer, researchers know they must continually find ways to work more efficiently. Given that, for several years now, the FDA has encouraged the use of risk-based monitoring (RBM) in clinical trials.
Risk-Based Monitoring: Determining What Works Best
RBM has been described as a methodology using risk algorithms to determine the right level of monitoring. The FDA defines monitoring in general as “a quality control tool for determining whether study activities are being carried out as planned, so that deficiencies can be identified and corrected.” It covers a wide range of activities, but according to Applied Clinical Trials, RBM is meant to reduce the time-consuming, costly practice of onsite 100 percent source data verification (SDV), while refocusing efforts on improving data quality.
“There is a growing consensus that risk-based approaches to monitoring, focused on risks to the most critical data elements and processes necessary to achieve study objectives, are more likely than routine visits to all clinical sites and 100% data verification, to ensure subject protection and overall study quality,” the FDA asserts.
The FDA is not alone. ICH, EMA, the UK’s Medicines and Healthcare Products Regulatory Agency, the Clinical Trials Transformation Initiative and others have endorsed RBM as a means to improve clinical trial monitoring.
And, clinical trial monitoring needs improving: Studies have shown that trials are taking longer (up to 16 months more, on average), and that it’s harder to recruit and retain patients.
In oncology trials in particular, access to patients is a major challenge. There is significant competition among academic and industry needs, and trial design is more complex since trials do not involve healthy volunteers. Only oncology patients on standard-of-care treatments may participate.
Is RBM the solution?
Does the promise of RBM hold up when practitioners evaluate their efforts? A recent analysis of the literature on RBM finds it is working: “Reduced SDV combined with a centralized, risk-based approach may be the ideal solution to reduce monitoring costs while improving essential data quality.”
In addition, when Cancer Research UK (CRUK) piloted RBM at its Center for Drug Development, it saw 20 percent efficiency savings in monitoring of early phase oncology trials, and other measures indicated support for RBM from monitoring site staff.
More organizations are moving to implement RBM. Specifically, it’s being used in more oncology trials because of reduced source data verification, support from regulatory agencies, and improved integration with CRO activities.
One survey reported nearly 50% of respondents were using RBM across programs or in pilots, up from a benchmark of 33 percent. Another 10-30% indicated they would start RBM strategies within the coming 12 months. The growing use of electronic solutions and statistical assessments was cited.
RBM helps clinical trials in significant ways:
- Site selection: by tracking site feasibility and performance over time, in a specific therapeutic area such as oncology.
- Subject recruitment: by tracking issues based on a holistic view of data - for example, site readiness/training or geographic challenges, and bringing enrollment back on target.
- Trial oversight and subject safety: by integrating in-flight clinical outcomes data with operational data, tracking site performance is possible. This helps increase the efficiency of trials while protecting the safety of participants. Trends and outliers in safety data are identified faster, preventing costly and dangerous delays in the analysis of potential safety concerns.
- Integration with real-world evidence (RWE): addresses the trend toward personalized medicine in oncology research. This targets therapies to specific cohorts of subjects and identifies these subjects for research using data beyond that from controlled clinical trials, such as electronic health record and social media data.
FDA: Technology to Reduce Need for Onsite Monitoring
While FDA says it expects some amount of onsite monitoring to continue, “evolving monitoring methods and technological capabilities” will lead to its decrease.
Technology solutions designed for RBM can reduce the effort spent on complete SDV, while providing better data analysis and protection of trial subjects. This shifts clinical monitoring from frequency-based to risk-driven monitoring that is proactive and comprehensive.
Some of the advantages of using an RBM technology solution include:
- Speed to Actionable Insight – ensures clinical development users can act more quickly to answer risk monitoring questions.
- Visibility into the Unknown – reveals trends and patterns that present risk (or opportunity) through visualizations, dashboards, and applications. Disparate clinical, operational, and safety data are connected.
- Self-Service Discovery – allows users to drastically reduce time-consuming reliance on IT for data preparation, report building, and spreadsheet version control.
- Universal Adaptability – empowers the broadest spectrum of clinical development users.
Trial monitors interested in RBM solutions that achieve the above-stated benefits should look for the following:
- Closed-loop, actionable RBM that lets you follow up on recommended actions that have been triggered by risk signals directly within the RBM system
- Adaptive, flexible risk model that lets you assess the efficacy of your RBM strategy within the RBM system, and also adjust the risk model accordingly. Examples include: thresholds, weightings, algorithms, or recommended actions.
- Holistic data view that provides insights from data from multiple sources, all at once, even if the data is not standardized.
- Persona-driven workflow that lets you get to the data needed quickly - based on your persona in the system - and act on it. This eliminates the need to hunt for the data you require.
Are you using RBM to improve efficiency of your oncology clinical trials? Find out how PerkinElmer Informatics can deploy RBM and Trial Operations solutions to make the most of your clinical trials.