The Drug Developer’s Double B(l)ind: Overcoming Barriers to Solve the Rare Disease Data Puzzle

Let’s kick off this blog with a confession: I am not, nor have I ever been, a drug developer. So, you might wonder, what qualifies me to speak about the perspective of someone navigating real-world data (RWD) for rare disease drug development in the pharmaceutical world? Fair question. However, after 20 years in this field and countless collaborations with clients facing a myriad of challenges, I’ve gathered a fair share of insights. My aim here is to synthesize those experiences and distil their implications. As for bias? I’ll let you, dear reader, be the judge.

In the world of rare diseases, drug development is notoriously challenging. Small patient populations and the complexity of these conditions make every stage, from discovery to commercialization, an uphill battle. For drug developers, generating meaningful RWD is not just a necessity; it's a strategic puzzle. And let’s face it, in rare diseases, where RWD is itself a rarity (pun intended), the challenge only grows.

In my earlier blog, I touched on how the scarcity of information in rare diseases has pushed the research community to be more open to non-RCT (randomized controlled trial) data. No data source is perfect—not even RCTs, which in rare diseases are fraught with their own limitations: recruiting enough patients is tough, inclusion criteria are often strict, and the controlled environment rarely reflects real-world conditions. This prolongs drug development and creates headaches for teams striving to bring new innovations to market. Enter RWD. When used effectively, it can help bridge evidence gaps, support regulatory approvals, and enhance market access.

The Ugly Truth: Why RWD Generation in Rare Diseases Is Tough for Drug Developers

As I described in my first blog of this series, the rare disease data puzzle is a complex web of obstacles. Patient dispersal, data fragmentation, and issues of data access and ownership are the usual suspects. Add to this two other challenges, which are closely linked.

First, there’s variability in clinical practices. Rare diseases often lack standardized treatment guidelines, resulting in significant differences in how patients are managed across healthcare settings. This inconsistency makes it difficult to aggregate data in a way that’s meaningful; patients may receive different treatments, dosing regimens, or care protocols depending on their location and the expertise of their healthcare providers.

Second, as discussed in my blogs on patient-generated health data and patient communities, traditional clinical endpoints often fail to capture the full scope of a patient's experience. Increasingly, drug developers need to rely on patient-reported outcomes, quality-of-life measures, and other patient-centric data. Yet gathering this information in a consistent, validated manner is no easy feat. It requires robust methodologies to ensure that the data is reliable and valuable for regulatory and reimbursement decisions.

But let’s get to the heart of the matter: the data puzzle in rare diseases is, at its core, a problem of time and cost. To unpack this further, we need to take a closer look at the reality of decision-making that drug developers face.

More of the Ugly Truth, But a Brighter Future?

As I’ve discussed in previous posts, recent advances in technology and infrastructure give us reason to be more optimistic about solving the rare disease data puzzle. The obstacles to generating RWD in rare diseases are becoming less daunting. However, the broader challenges remain and, in some cases, are growing. This reality underscores the importance of clear vision and meticulous planning when considering the use of RWD. There are three key elements that must always be kept in mind.

First, while drugs for rare diseases may benefit from regulatory incentives, such as orphan drug designations, the market size is inherently small. Limited revenue potential forces companies to manage costs with extreme caution. Delays in development directly eat into an already narrow market window. In today’s landscape, early drug development is rarely undertaken by large pharmaceutical firms; smaller biotech companies, under pressure from investors, are tasked with spending only when absolutely necessary. The runway is getting shorter, not longer.

The second challenge is the inherent risk of drug development itself. Most drug candidates do not make it through clinical trials successfully, so companies must be judicious in allocating resources, often spreading budgets across multiple projects to offset the risk of failure. Add to this the relentless pressure on drug pricing, especially for potentially curative treatments in rare diseases that can exceed $500,000 per course. This context forces companies to scrutinize every expenditure, and RWD is no exception. Confidence in the generated or available RWD must be high before committing resources.

The third and final element is internal: strategy in drug development is always in flux. Strategic planning in this environment must be dynamic, yet most companies adhere to traditional processes—setting annual budgets and evidence generation plans well in advance. This static approach often fails to keep pace with the need for quick adjustments in response to data readouts or regulatory feedback. It’s like trying to steer a speedboat with the slow, deliberate movements of a cargo ship.

So, in this rather bleak environment, is there hope for RWD to play a more useful role in drug development than it has in the past? The answer is an unequivocal yes. The developments I’ve covered in this series indicate that there is now a wider array of options for RWD than ever before. The challenge is to match these options to specific use cases across the drug development spectrum. Cheaper and faster methods, even those not stemming from rigorously designed studies, deserve a place alongside the more traditional, costly, and time-consuming approaches like decade-long natural history studies. The hierarchy of evidence needs to adapt.

On the flip side, we need an easier and faster way to evaluate the feasibility of each RWD generation approach for a given rare condition. This is not a ‘nice-to-have’ option; it’s essential. Too often, companies embark on multi-year RWD collection efforts, believing that the rigor of prospective data collection is the logical path. Yet, two years later, they find the data set contains just 10 individuals and lacks key metrics. Dismissing faster, cheaper data options—even if only to guide the protocol design—because they seem like a waste of time and money often proves to be a costly mistake. In reality, this decision should be made right from the start.

Conclusion: Turning the Data Puzzle into a Competitive Advantage

In rare diseases, the ability to generate high-quality RWD is not just a research necessity—it is a strategic advantage. Drug developers who can effectively navigate the challenges of RWD collection will be better positioned to demonstrate their therapies' real-world value, gain regulatory and market access, and ultimately improve patient outcomes.

By embracing innovative data generation methods like rAIre: Rare Disease Insights , building strong partnerships with patient communities, and investing in data integration technologies, drug developers can turn the RWD puzzle into a pathway for success. The road ahead is complex, but those who solve it stand to make a meaningful impact in the rare disease space. ?Stay tuned for the next post exploring the hidden gems of RWD in rare diseases!