A Data-Driven Framework for Rare Disease Protocol Design: Integrating Real-World Evidence, Patient Perspectives, and Regulatory Guidance
Author(s)
Bavaajan Devarapalli, MPharm1, VATSAL CHHAYA, M.Sc.2, Shaurya Deep Bajwa, MBA, M.Sc.1, KAPIL KHAMBHOLJA, PhD2.
1Catalyst Clinical Research, Thiruvananthapuram, India, 2Catalyst Clinical Research, Baroda, India.
1Catalyst Clinical Research, Thiruvananthapuram, India, 2Catalyst Clinical Research, Baroda, India.
OBJECTIVES: Over 300 million people worldwide are affected by 7,000+ rare diseases (RDs), yet fewer than 10% have approved therapies. Between 2010-2020, only 12% of RD drug candidates gained approval, versus 20% for common conditions. Challenges in protocol design—such as small, heterogeneous populations, limited natural history data, and lack of validated endpoints—contribute to these low success rates. This study introduces a three-pillar, data-driven framework for RD protocol design, aligned with 2024 FDA, EMA, and ICH M11 guidance.
METHODS: A mixed-methods approach was used. We conducted a targeted review of 45 regulatory documents and peer-reviewed publications, analyzed real-world data (RWD) from three RD registries—including the European Rare Disease Registry Infrastructure (ERDRI)—and two post-marketing surveillance databases, such as the FDA Adverse Event Reporting System (FAERS) and EudraVigilance. Additionally, we reviewed 12 semi-structured interviews from published literature involving experts in clinical development, regulatory strategy, and patient engagement. Findings were synthesized into a three-pillar framework: design considerations, study conduct approaches, and regulatory alignment.
RESULTS: Key insights from this analysis revealed the following:
CONCLUSIONS: This framework, grounded in regulatory analysis, RWD, and expert insights, offers actionable strategies to improve RD trial design. Adaptive designs, patient engagement, and early regulatory alignment enhance feasibility, recruitment, and protocol acceptance. Future work should explore AI-driven simulations and decentralized models to further optimize trial design.
METHODS: A mixed-methods approach was used. We conducted a targeted review of 45 regulatory documents and peer-reviewed publications, analyzed real-world data (RWD) from three RD registries—including the European Rare Disease Registry Infrastructure (ERDRI)—and two post-marketing surveillance databases, such as the FDA Adverse Event Reporting System (FAERS) and EudraVigilance. Additionally, we reviewed 12 semi-structured interviews from published literature involving experts in clinical development, regulatory strategy, and patient engagement. Findings were synthesized into a three-pillar framework: design considerations, study conduct approaches, and regulatory alignment.
RESULTS: Key insights from this analysis revealed the following:
- Design Considerations: Adaptive and Bayesian designs reduced sample size needs by 30-50% in 7 of 10 trials.
- Study Conduct:
- Early patient engagement led to 25% faster recruitment and 18% lower dropout rates—critical given that 80% of trials miss enrollment targets and dropout rates average ~30%.
- Multinational trials recruited 2.3× faster and achieved 1.8× greater diversity than single-country studies, addressing common limitations in RD trials.
CONCLUSIONS: This framework, grounded in regulatory analysis, RWD, and expert insights, offers actionable strategies to improve RD trial design. Adaptive designs, patient engagement, and early regulatory alignment enhance feasibility, recruitment, and protocol acceptance. Future work should explore AI-driven simulations and decentralized models to further optimize trial design.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
PCR5
Topic
Health Policy & Regulatory, Patient-Centered Research
Topic Subcategory
Patient-reported Outcomes & Quality of Life Outcomes
Disease
Rare & Orphan Diseases