The rarity of these diseases means information about them is scarce, as there are often too few people in a geographic location to inform experts fully or to complete a clinical trial. In turn, this may mean more patients will be under- or misdiagnosed.
While initiatives like the US’ Orphan Drug Act of 1983 have provided incentives for progressing the research and development of treatments for these diseases, a deficit still remains in the number of existing treatments. This is due to several barriers that still stand between research and the results needed to develop further treatments.
Poor understanding of the pathophysiology of rare diseases remains a significant one, and this is not only a challenge when formulating molecules to combat the disease: it also has the potential to create inaccuracies or errors in diagnosis, endangering patient health and causing downstream issues for clinical trial patient selection.
More data is needed. To better treat these diseases, we must understand their lifecycle, the impact of various treatments on them, and how to develop targeted plans to combat them. But given the difficulty of recruiting enough people with these diseases to complete a clinical trial, how can we gather this information?
Real-world data (RWD) is the answer.
RWD can come from a number of sources: healthcare databases such as electronic health records (EHRs); patient registries, which are valuable sources for showing the disease lifecycle, patterns of a disease, and variability of clinical progression; and even ‘unstructured’ sources like mobile devices and social media.
It also shows overall treatment effectiveness across a wider timescale than a trial otherwise would. Ultimately, the patient is at the centre of our work and RWE is one way to determine exactly how we are helping. For people with often-misunderstood diseases like pulmonary arterial hypertension, this is critical for development of medicines that can improve both their own prognosis and everyday quality of life. Many people also participate in follow-up studies to help advance the science further, to support the improvement of treatment and care for future patients.
In turn, these benefits mean that we are able to evaluate new molecules in real-world situations and better shape trial design to gather higher-quality data, as well as replicate real-world instances and measure the long-term safety and efficacy of medicines.
RWE is also central to the advancement of artificial intelligence (AI) and machine learning (ML). These complex, sophisticated algorithms rely on significant amounts of data to formulate accurate analyses – the more data fed in, the better the results. RWE is important when training AI and ML on rare diseases and allow it to generate data visualisations to assist clinicians in patient care.
Additionally, RWE can support traditional randomised clinical trials (RCTs) through the creation of external control arms (ECAs). Using previous trial and RWE data, a historic group of people can be found whose conditions closely matched trial patients but who did not receive the trial’s treatment.
Mining RWE data in this way means no peer control group need be found, and no time is spent managing them. This is a significant resource burden lifted from RCTs, but can occur only with well-studied conditions where standard of care has changed little over time. With regulatory-grade ECAs, however, regulatory decision-making becomes easier.
While RWD can provide valuable and complementary information to RCTs, there are limitations to its use. As RWD sources are not designed for clinical research, there is a risk that potentially unobserved factors, such as a patient request, could influence a physician’s decided course of treatment. This could prevent a direct comparison of outcomes between treatment arms or to RCT findings.
In addition, depending on the method used to collect it, intake of RWD can be complex. A survey will naturally be a faster and easier method of collecting data than, for instance, creating a patient registry or accessing EHRs. Overall, however, the collection of RWD is generally an easy and cost-effective way of gathering data. Generating RWE is significantly more challenging, and involves multiple bodies including patients, regulatory agencies, and clinicians.
Also, given the vast amounts of data which must be handled, it is critical that RWD processes are planned well in advance to ensure the right data is being gathered about the right populations, reducing ‘noisy’ data and establishing targeted, standardised approaches to RWD datasets.
The Observational Medical Outcomes Partnership (OMOP) common data model (CDM), pioneered by stakeholders across the industry from regulators to data scientists, is a standardised data format for RWD assets that outperforms many other formats in terms of integrity, flexibility and coverage. Reformatting registries and clinical databases to the OMOP CDM format allows for standardisation with limited data loss. In addition, establishing OMOP CDM as a data standard will enable efficient evidence generation through federated data networks, like Janssen’s own rare disease network, PHederation.
Another crucial element of RWD is collaboration. For RWE to reach its potential, the healthcare industry needs to harness its collective wisdom, reduce organisational silos and maximise the value of RWD for healthcare. This applies to research centres as well as pharmaceutical companies; it is vital that data is dispersed, not hoarded, and that authorities should support and lead these efforts within the community.
At Janssen we are spearheading this rare disease collaboration with the RWE Navigator, which supports and encourages knowledge-sharing across companies and academia while providing us with a holistic view of quality of life of those living with rare diseases. Because while industry collaboration is important, it is equally necessary to work alongside people living with rare diseases and patient advocacy groups. With their early input and collected healthcare data, unique insights can be gained on how to most efficiently gather important data.
This in turn can increase process efficiency and genuinely reflect the lives of people with rare diseases throughout the treatment lifecycle. Luckily we are also starting to see this now with the advances health authorities, such as the US Food and Drug Administration, are making by including RWD in regulatory decision making.
We are optimistic about the future of RWD and the potential it can bring to people that are living with a rare disease. As well as how it will support the healthcare industry to develop treatments and diagnose these diseases even earlier. Though challenges exist, the benefits of this method of generation far outweigh the risks.
With the knowledge that RWE insights bring, we have the opportunity to better understand not only the diseases we treat, but the journeys of the people we seek to help. We are excited to be part of the journey that is creating value from the next generation of RWE.
Neil Davie is Janssen’s global head of R&D and External Innovation, Pulmonary Hypertension.
Emmanuelle Quiles is worldwide vice president Cardiovascular, Metabolism and Pulmonary Hypertension at Janssen.