Each rare disease research breakthrough offers the promise of a longer, better life for patients who otherwise may have no meaningful options. Because of this, patients grappling with rare diseases are unique within the world of clinical trials—they’re often highly motivated and eager to take part in research. Typically, they are more-informed about their disease than the average patient and are actively seeking out possible treatments. Fortunately, decentralized clinical trials (DCT) and FAIR data principles are well-poised to meet their needs and push research further, faster.
The eager willingness of rare disease patients to enroll in clinical trials can greatly alter the recruitment process. But first, they need to find out about the trial’s existence. Since traditional clinical trials relied on physicians or other healthcare professionals to spread the word, eligible patients didn’t always hear about current or upcoming clinical trials. Too often, clinical researchers had to rely on a wing and a prayer that the right patient would walk into the right doctor’s office and learn about the trial. This slows down or even stalls out life saving research.
DCT changes the recruitment process entirely since it supports the screening and enrollment of patients from anywhere. Decentralized methods can also help rare disease researchers identify possible boluses of rare disease participants in certain areas, allowing them to decide where a research site might be most useful, if one is required. Alternatively, researchers may rely on remote nursing and nearby plasma centers to move research forward from afar. Regardless of the exact methods employed, decentralized trials are well-poised to increase access to rare disease trials for the participants who need it most.
Rare disease researchers have another trick up their sleeve to speed up recruitment: searchable data. When researchers can easily search high-quality data, they can find the people who can benefit most from a potential cure. One way to find rare disease patients is to comb through combined data from multiple sources. For example, searching disease registries can be highly effective. Using a standardized script to query across different registries in one move can simplify the process and accelerate recruitment exponentially. This can allow rare disease researchers to find an adequate sample size, despite the limited number of potential candidates.
Once possible participants are identified, the next hurdle is enrollment. One of the biggest challenges for clinical trial participants is the burden of travel to the site. For patients living in rural areas or with high-risk health conditions, this can make the difference of whether or not they will participate in a trial.
Fortunately, eConsent eliminates the need for patients to travel to sites to complete pre-screening questionnaires or informed consent. Instead, participants can complete these activities from their homes with the help of eConsent features such as video chat and e-signature. Language barriers or cultural issues can likewise be overcome by offering virtual support, including live translators via video chat.
Importantly, eConsent also allows for dynamic consent, so participants can retract or add new layers of consent over time. This paves the way for researchers to reuse data collected from various sources or even contact patients involved in other research projects through the applicable sponsors for research purposes.
Embracing FAIR data
Patient data registries that are findable, accessible, interoperable, and reusable (FAIR) for both humans and computers support research across multiple resources. This is especially relevant in rare disease research since data is too often scarce and scattered. When rare disease registries are created according to FAIR standards, then specific research questions can be asked across multiple registries without physically combining data.
Recently, a small group of researchers wrote about searching European Reference Networks (ERNs), made up of 24 virtual networks to search for a sample group of patients for a hypothetical vascular anomaly study. Their project showed how researchers can combine data from various places to create an adequate sample size, regardless of a potentially small affected patient population.
Day-to-day data collection can be used for other rare disease research—but first it must be machine-readable to be valuable. For health systems in the European Union, it’s mandatory to collect data on certain rare or low-prevalence complex diseases—after all, when combined these affect the daily lives of around 30 million EU citizens. Every registry collects common data elements (e.g., gender, age of onset, diagnosis of disease, consent info), thereby creating huge machine-readable cross-registries for researchers to mine in their quest for cures. Other nations looking to create this success must prioritize interoperability and FAIR data principles in data collection to ensure precious data can be reused to accelerate future rare disease research.
The work done within the pharma industry for rare disease shines especially brightly. It promises the hope of a better life for those who need it most. Decentralized methods, such as eConsent, can support a completely remote prescreening, enrollment, and consent process. At the same time, reusable data speeds up the process of both finding potential participants and can even give researchers a jumpstart on their research.