Overcoming Orphan Drug Development Challenges with Real-World Data and Evidence

How real-world evidence can address persistent challenges in rare disease therapy development

While individual rare diseases affect populations that are small in numbers, collectively they impact millions globally, posing significant health and research challenges. While there is no universally accepted definition for “rare disease,” it is estimated that more than 10,000 distinct rare conditions exist, and people living with these rare diseases represent as much as 10% of the global population.

These conditions pose substantial challenges for patients, families and health care systems. Developing treatments for individuals living with rare diseases is critical, but orphan drug development is laden with unique obstacles that necessitate innovative, multifaceted approaches. Real-world data (RWD) and real-world evidence (RWE) have emerged as transformational tools in addressing these hurdles, advancing rare disease drug development.

A recent survey of rare disease clinical development stakeholders highlights the most significant challenges facing rare disease drug development over the next decade:

• Access to appropriate patient pools (62% of respondents)
• Concerns with cost (59% of respondents)
• Increasing complexity in clinical trial design (54% of respondents)
• Heterogeneity of patient population (53% of respondents)
• Expanded regulatory requirements (41% of respondents)
• Low prevalence of condition (37% of respondents)
• Lack of understanding of rare disease natural history (30% of respondents)

In this article, we explore RWD and RWE strategies that address several of these clinical research barriers and improve the development and market access of treatments for those living with a rare disease.

Breaking through research barriers

Challenge #1: Small groups of patients

Rare diseases impact a small number of individuals, making it difficult to recruit enough participants for clinical trials. This limited pool poses significant barriers to conducting statistically significant studies and validating the efficacy and safety of new treatments. Moreover, in some rare disease trials, it is unethical to design a control group of patients with a placebo.

External comparator arms (ECAs): ECAs offer a viable solution to recruitment challenges by utilizing RWD to create control groups for single-arm trials. ECAs leverage existing patient-level data– from clinical trials or real-world sources–to construct a comparator cohort, mimicking the characteristics of a randomized controlled trial (RCT) control arm. This solution allows researchers to enhance the robustness of their control populations, minimize bias and provide a real-world context to trial outcomes. This approach is particularly valuable for rare conditions in which traditional two-arm RCTs are not feasible or ethical. Properly designed and analyzed external comparators (ECs) built from RWD can strengthen data from single-arm or traditional RCTs to support regulatory, HTA and payer decisions.

Developing effective ECs requires more than just matching clinical trial inclusion and exclusion criteria within the RWD source. It involves addressing the specificity of research questions, the clinical and investigational product context, and underlying causal frameworks; selection of a suitable data source; and a deep understanding of RWD provenance, granularity, completeness, structure and curation. Careful consideration of these factors ensures that a suitable RWD-constructed EC can be designed, mitigating potential sources of bias, enabling robust comparisons and supporting regulatory decision-making.

In-trial research: In rare disease trials, where recruitment is especially challenging, gathering supplementary data from patients in clinical studies is essential. By collecting both clinical and patient experience data during clinical trials, biopharma companies gain insights into how patients perceive the benefits and risks of treatments, a factor that can be pivotal in regulatory approvals. This data can also reveal burdens to participation to support recruitment efforts.

Challenge #2: Heterogeneity of diseases

Many rare diseases are characterized by significant variability in symptoms, disease progression and patient responses to treatment. This heterogeneity among small groups of patients presents substantial challenges for rare disease research, including clinical trial design, endpoint selection and the interpretation of study findings. Identifying the right target patient profiles becomes even more difficult when there is no clear consensus on how disease progression manifests across different individuals.

Limitations of traditional patient preference research methods: Capturing patient preference data is essential for informed decision-making in rare disease clinical research, allowing researchers to understand barriers to trial participation, how individuals value various treatment benefits and risks, and their willingness to make trade-offs between competing outcomes. However, traditional preference methods like discrete choice experiments (DCEs) are unable to capture these critical insights for rare disease clinical research because of the small sample sizes and disease heterogeneity. DCEs typically produce population-level estimates and require large samples to be robust. In rare disease trials, in which small patient numbers are common, DCEs often fail to generate precise estimates or identify heterogeneity in preferences.

Multi-dimensional thresholding (MDT): A promising alternative is multi-dimensional thresholding (MDT), which offers a robust alternative for eliciting patient preferences in small sample sizes. MDT ranks improvements in various treatment attributes and provides detailed insights into patient preferences, even for small sample sizes. MDT effectively captures patient preferences at the individual level and aids in determination of the source of the preference heterogeneity, offering valuable insights into rare disease patients’ diverse needs and perspectives. By tailoring treatment approaches to these preferences, researchers can better address the needs of different patient subgroups. MDT helps bridge the gap in preference heterogeneity to ensure that rare disease therapies are developed with the diverse needs of patients in mind.

Challenge #3: Lack of natural history data

Understanding the natural progression of rare diseases is crucial to the development of effective treatments. However, there is often a lack of comprehensive natural history data, including key clinical outcome measures. This impedes the confirmation of unmet clinical need and design of appropriate endpoints and robust clinical trials.

Natural history studies and biomarkers: With the rise of precision medicine, prospective natural history studies have become essential in understanding rare diseases. These studies provide longitudinal insights into disease characteristics, patient populations and subtypes, which are crucial for clinical product development. They help identify sensitive endpoints, optimal follow-up durations and eligible patients for trials, and they serve as historical comparators in single-arm trials.

Natural history studies also aid payers by assessing disease burden and treatment response in real-world settings, identifying subtypes with higher burdens and pinpointing patient subpopulations likely to benefit from new therapies. Using biospecimens collected from study participants, natural history studies empower researchers to identify novel insights into underlying disease pathology and progression. Ideally, these studies also prioritize longitudinal profiling of participants, e.g., genetic, metabolomic or proteomic analyses. In certain diseases, digital records of medical images are crucial to holistically understanding disease. Natural history studies that combine biospecimens, profiling data and imaging with longitudinal outcome data are uniquely positioned to support biomarker research and provide a deeper molecular understanding of a disease and existing treatment effectiveness. Data from these studies can accelerate research timelines and improve trial planning throughout the product life cycle.

Challenge #4: Regulatory and market access hurdles

The regulatory and health technology assessment (HTA) pathways for rare disease therapies are complex and vary by region. With the new EU HTA Regulation impacting orphan medicines, navigating these pathways has become even more challenging. Securing market access and reimbursement for high-cost treatments remains a significant challenge, as regulators, HTA bodies and payer engagements demand robust evidence of clinical and economic value. Several factors complicate market access for rare diseases, including identifying patients (due to underdiagnosis or delayed diagnosis), quantifying disease burden, identifying appropriate comparators (due to lack of approved treatments), and inability to meet standard effectiveness quality-adjusted life year (QALY) thresholds.

Integrated scientific advice: Early and strategic evidence generation is vital for the successful development and market access of rare disease therapies. It is important to secure engagements across regulators, HTA bodies, payers and patients to better understand the types of evidence needed for regulatory approval and market access.

In rare diseases, evidence gaps are often significant, necessitating de novo evidence generation. These gaps include insufficient knowledge about the disease, pathways and natural history, lack of comparative data, small sample sizes, single-arm studies, unreliable subgroup data, and challenges in demonstrating cost-effectiveness due to uncertainty in modeling and budget impact. It is therefore essential for health technology developers to identify these gaps and begin evidence generation planning early.

A proactive approach allows for the alignment of pivotal trial designs with HTA- and payer-relevant endpoints, such as health care resource utilization and disease-specific quality of life measures. Addressing evidence gaps early enables health technology developers to optimize their development strategies and plan their health economic modelling sooner, to provide robust and comprehensive evidence packages that support their product’s value proposition.

Conclusion

RWD and RWE utilization in rare disease drug development offers opportunities to efficiently address some of the biggest research hurdles, from understanding disease characteristics to optimizing clinical trial designs, gaining valuable patient perspectives and navigating regulatory landscapes. Companies that integrate these tools into their development strategies will enhance their research, improve patient outcomes and achieve successful market access.

RWE is transforming rare disease drug development, providing the insights needed to overcome unique challenges. As the health care landscape continues to evolve, RWD and RWE will continue to be essential in driving innovation and delivering life-changing therapies to people living with a rare disease.

Takeaways

1. Enhanced understanding of rare diseases: RWD and RWE provide critical insights into disease characteristics and patient populations, essential for developing effective therapies for rare diseases.
2. Early evidence planning: Proactive and comprehensive planning for evidence generation is necessary for rare and ultrarare indications. Integrated scientific advice with regulators and HTA bodies alongside engagements with payers, combined with strategic data collection, ensure that manufacturers address challenges early and avoid delays in approval timelines.
3. Overcoming research challenges: Tools like natural history studies, external comparator arms and genetic testing help address the unique challenges in rare disease research, such as limited patient populations and regulatory hurdles.
4. Patient-centric approaches: Methods like multi-dimensional thresholding can capture patient preferences in the rare disease setting, highlighting the importance of understanding patient heterogeneity to tailor treatments and improve clinical and market outcomes for rare disease therapies.