In the world of rare disease research, data is everything, but diversity within that data is what makes it meaningful. Across the U.S., rare diseases collectively impact around 30 million people, or roughly 1 in 10 Americans. For conditions like Myasthenia Gravis (MG), an autoimmune disorder affecting approximately 60,000 people in the U.S., the importance of racially, ethnically, and geographically inclusive datasets cannot be overstated. In a space where patient populations are already limited, ensuring broad representation is a clinical necessity.
It's a timely reminder that rare disease trials demand more than scientific innovation; they require operational agility. This is where the story shifts from challenge to opportunity: a tech-enabled, data-diverse approach can drive not only compliance but faster, more inclusive, and more effective clinical outcomes. As we observe Myasthenia Gravis Awareness Month, this focus on MG prompts us to revisit a rapidly growing area: Immunology diseases, a complex and booming market. For deeper insights on how automation is reshaping immune disorder trials, check out our detailed Immunology Spotlight blog.
Diversity in clinical trials is a growing area of focus for regulatory bodies and the industry as a whole. Yet, many rare disease trials across the U.S. continue to face a critical shortfall in representation. When it comes to rare disease trial activity, oncology leads the way, accounting for 72% of rare disease clinical trials, while immunology, covering autoimmune and inflammatory conditions, represents the second-largest area at 10%. According to the FDA, less than 20% of participants in rare disease trials are from racially and ethnically diverse backgrounds. The consequences of this are significant such as skewed clinical insights, reduced generalizability of findings, and potential delays in regulatory approval.
Over the past five years, orphan drugs have accounted for over 50% of new active substance approvals in the U.S. and 45% in Europe. Of 268 drug launches in the U.S., 143 (53%) were for orphan-designated drugs. Rare diseases accounted for 44% of global clinical trial activity as of 2024, yet 95% of the 7,000–10,000 known rare diseases still lack approved treatments, underscoring a significant unmet need and ongoing focus for innovation.
This growing momentum has reshaped dealmaking dynamics in rare diseases. Large pharma companies have not just acquired entire rare disease portfolios, they also selectively targeted high-potential pipeline assets and marketed products from small-to-mid-sized biotechs (SMIDs) or specialized CROs. This strategic diversification reflected a clear industry signal: rare disease programs have become increasingly valuable across all stages of development.
For instance, AstraZeneca’s Alexion acquired a portfolio of preclinical gene therapies for rare diseases from Pfizer in 2023, in a deal worth up to $1 billion, showcasing confidence in early-stage innovation. On the other hand, Amgen’s $27.8 billion acquisition of Horizon Therapeutics brought in a mix of marketed products such as Tepezza (thyroid eye disease), Uplizna (neuromyelitis optica spectrum disorder), and Krystexxa, affirming the growing commercial value of mature rare disease assets.
These strategic moves reflect a broader industry realization: rare diseases offer not only therapeutic promise but also strong commercial potential. However, tapping into this potential is no easy task. The rare disease market remains deeply complex and fragmented, with thousands of conditions affecting small, dispersed populations. This makes standardized trial designs difficult and safety signal detection more challenging.
To address this, Real-World Data (RWD) plays a pivotal role by enabling access to diverse, decentralized data sources. It helps identify rare patient populations, uncover infrequent adverse events, and assess treatment outcomes in real-world settings. In diseases where clinical trial participation is often limited, RWD strengthens safety monitoring and improves regulatory readiness.
Making Rare Disease Trials More Inclusive: What’s Standing in the Way?
In the race to develop treatments for rare diseases, ensuring data diversity in clinical trials isn't just a regulatory box to check; it's a scientific and ethical necessity. Without inclusive representation, we risk creating therapies that don’t work for everyone or, worse, that exclude the very patients who need them most. Yet, the road to equitable participation in rare disease research is riddled with challenges.
Here are the key factors holding us back:
Data Source and Quality Issues
Recruitment and analysis are often hindered by poor data quality and fragmented sources. Datasets may lack completeness, standardization, or privacy safeguards and frequently fail to reflect target populations. Additionally, data silos across institutions and countries limit collaboration and aggregation essential for diverse rare disease trials.Regulatory and Protocol Constraints
While regulatory agencies stress the need for participant diversity, practical implementation remains difficult due to strict protocols. Eligibility criteria, aimed at controlling variability, can unintentionally exclude diverse patients, creating a paradox where data integrity efforts conflict with inclusive research goals in rare, complex conditions.Small and Heterogeneous Populations
Rare diseases affect small patient populations, making clinical trial recruitment inherently challenging. Enrolling a sufficiently large cohort is difficult, and achieving demographic diversity is even harder. Disease heterogeneity variations in symptom presentation and progression add complexity to recruitment and hamper reliable data interpretation.Underrepresentation of Minority Groups
Rare disease clinical trials often underrepresent minority groups, including racial and ethnic populations, older adults, people with disabilities, and those from lower socioeconomic backgrounds. This underrepresentation leads to inequitable access to new therapies and creates gaps in understanding treatment efficacy and safety across diverse populations.Logistical and Socioeconomic Barriers
Participants from underrepresented backgrounds often face logistical and socioeconomic barriers to trial participation, including limited access to trial sites, financial challenges, low awareness, and inadequate outreach. Additionally, geographic distance and centralized research at specialized centers further restrict inclusion from remote or underserved communities.
Cloud Concinnity: Centralizing Data for Equitable and Agile Rare Diseases Trials
Cloud Concinnity isn’t just a platform; it’s a purpose-built engine for transforming how rare disease trials are run. In a space where patient populations are small, site networks are dispersed, and timelines are unforgiving, Cloud Concinnity delivers the precision, visibility, and speed that today’s sponsors demand.
What sets Cloud Concinnity apart?
True Centralization, Not Just Integration: Our platform brings all critical oversight functions, diversity tracking, protocol changes, and risk logs into a single source of truth. No more toggling between systems or waiting on outdated reports.
Built for Cross-Functional Agility: Cloud Concinnity is uniquely designed to align sponsors, CROs, investigators, and oversight boards in one collaborative environment, enabling rapid decisions without compromising compliance.
Regulatory Intelligence Built In: With automated audit trails, protocol deviation tracking, and inspection-ready documentation, teams stay aligned with evolving FDA and EMA expectations without scrambling before submissions.
In rare disease R&D, there’s no room for rework. Cloud Concinnity gives sponsors the oversight they need and the foresight they’ve never had, ensuring every patient, site, and data point contributes to progress.
Discover how Cloud Concinnity can help make your next rare disease trial more inclusive, efficient, and successful.