Decode real-world data opportunities in rare disease
Better use of anonymised real-world data (RWD), and a willingness to collaborate broadly with regulators, payers and patients will be key to speeding new cures for rare disease
Approximately 25 million Americans, and 30 million people in the European Union, live with one of 7,000 rare diseases, many of which do not yet have a therapy.
“It's not like we have forgotten about these diseases or the patients and their families,” says Dr. Omar Dabbous, MD, MPH, of Global Geneconomics and Outcomes Research at Novartis. “It's just that technologies haven’t been available to deliver therapies that would make a difference in their lives. I'm driven by the opportunity to bring treatments for conditions that don’t yet have any. Some are fatal, some are debilitating, and many are costly.”
The unmet need here is enormous, but pharma is racing to address it with innovative approaches to data and a new spirit of collaboration with all stakeholders from regulators to patients to peers. A collaborative approach is often key for a number of reasons. It is important to define early on what is important to patients, and what is important to regulators, payers, and HCPs, bringing them all into the same room early on, if possible.
Be clear on the objectives of why you want access to that data and how you can work with respect to data constraints, says Dr. Dabbous. “Studies have to be designed in such ways that regulators, payers and scientists can support regulatory submissions, payer submissions and story development.”
Key to making progress in rare diseases is having the right data. Typically, trial data may offer an incomplete picture due to small sample sizes and it is becoming crucial to know how to supplement and complement trial data with other sources, such as RWD.
Creativity required
When starting to investigate new therapies, the first step typically involves carrying out a systematic review of the literature. Since this is often not possible in rare disease, where there are few studies or comparable medicines, it’s necessary to get creative, says Jacki Lyons, Senior Director of Epidemiology at Alexion. “You have to be very careful and very thoughtful right from the beginning, because in a lot of ways, you're creating the database for a particular rare disease upon which patients and caregivers rely as a means of getting these drugs approved.”
Essentially, in diseases where there is a lack of published literature, researchers are developing that literature for the future. “I think we're seeing regulators start to accept and understand that we have to create these data and these data analytics for ourselves,” explains Lyons.
Pharma teams also need to consider the future use of all trial data, says Dr. Sonal Bhatia, Rare Disease Chief Medical Officer at Pfizer, who is part of the 
rare disease business unit. “The assumption is that a clinical trial succeeds and we're garnering the RWD to help support utility. But it's also very important to think about a failed trial, because from a patient's lens, getting to the end of a trial that fails or doesn't meet its endpoint, the data you've collected is so important for the disease space.”
If a trial fails and a team moves on to the next disease or trial, the data should not simply be put aside or forgotten about as those data may hold valuable clues that could help in subsequent studies. RWD can inform how to design the next trial and could potentially be “the Holy Grail for the one study that meets the need of the patient,” says Bhatia.
Overcoming the data challenges
The volume of RWD collected from various sources can be overwhelming and distracting, underlining the need to define research questions before delving into the data. “It all comes back to a very careful and a very thoughtful analysis plan and an understanding of what questions you're trying to answer,” says Lyons.
Another issue to address is the problem of small sample sizes that can make statistical rigor and anonymization in small populations challenging. How can you keep a patient case study anonymous if there are only a small number of patients in a country?
Varying data rules are a further barrier to overcome since access to patient data varies depending on local data regulation. “It’s always a balancing act between the precision expected by a researcher and the data protection required by the law,” says Gilles Paubert, Senior Vice President, and Global Head of Cegedim Health Data. “At the end of a study we have to aggregate the data and the result is to lose precision.”
In order to combat this reduction in precision, Cegedim decided to improve the world of RWD capture by working with primary care physicians and enrolling and involving HCPs and patients. Currently, almost 10,000 physicians are active within the network, with the idea being to not only meet the number of patients needed for anonymization criteria required by the law, but also to improve the coding of data and the structuring of the database so that data is consistent.
This not only helps to demonstrate value for patients, but also prevents data being lost when working country by country, which can happen when the number of patients falls below the legal requirements for anonymization. In Belgium, for example, collecting secondary data for rare disease is forbidden, making the case for larger, more easily anonymized, European databases.
Engage with patients early
Another vital aspect in deciding which data are important lies with patients. Finding out what types of measures matter most to them in terms of improvement and outcomes from a therapy should be understood as early as possible, because if patients are brought in when an investigational therapy is nearing submission, the opportunity to understand what patients need may have been lost. “I think bringing patients along for as much of the journey as possible makes the most sense in these populations,” says Lyons.
Understanding patients in such depth can help researchers build trust with them so that they are more likely to want to give access to their data or take part in a study that they feel will help them or others like them in the future. Knowing more about the diagnostic journey of patients can also help identify more, undiagnosed patients to bring into limited data pools.
According to Paubert, it’s not only important to ask the question “when” to involve patients, but this is also linked to the question” where” to involve patients. “When we are thinking about a treatment, the patient pathway is very important to improve detection at primary care levels, to help the physician to detect symptoms and refer to a specialist, so for me, “when” and” where” are really linked.”
What Next?
The use of RWE in the drug approval process could be sped up further by using machine learning and artificial intelligence, as well as by closer industry collaboration. “I expect that over the next decade, or perhaps less, a lot more is going to move forward,” says Bhatia. “We know where we want to go and we're all trying to do it in different ways. The more we share with each other, the faster we're going to get to that solution.”
Applications for RWE in rare diseases may well open the door for applications in non-rare disease areas as well. “Rare diseases are really paving the pathway [for other areas] on how we interact with regulators and what is needed from a RWE perspective,” says Bhatia.
Last year, the FDA published new guidelines concerning data quality and the type of data that can be submitted, along with how to submit it, and its impact on the regulatory pathway. The EMA is taking similar actions.
“If you think about the last seven to eight years, the regulators have been starting to look at RWE as something very critical, as part of the dossier,” says Bhatia. “This wasn't the case if you think about ten or 15 years ago, there's a lot of change that's happening now."