Trends In Rare Disease Trials: Recommendations

By Sapna Rani and Monica Nandagopal, Beroe, Inc.

The evolution of rare disease trials demands not incremental adjustments but a decisive reimagining of how evidence is generated, validated, and shared. Moving from concept to practice requires coordinated action across science, policy, and patient communities.^1,2

Our previous articles laid the groundwork by outlining the evolution of rare disease clinical trial designs from traditional RCTs to innovative models, such as micro trials and patient registries, and by evaluating the pros, cons, and adoption rates of these key designs. Having established this foundation, we now turn to the next critical question: How do sponsors decide which trial design fits a particular rare disease development program?

This decision is multifaceted, balancing scientific goals and patient needs against regulatory requirements and operational feasibility. Drawing on recent advances in statistical methodology, patient engagement, and digital technologies, this third article provides an integrated framework of design selection criteria and solution options to optimize rare disease research outcomes.

6 Next Steps To Develop An Action Plan

1. Researchers must embrace innovation with discipline.

Micro-trials and adaptive designs should not be viewed as shortcuts but as scientifically rigorous tools that demand careful hypothesis-building, deep phenotyping, and transparent statistical methods. Their success lies not just in flexibility but in credibility, ensuring that even ultra-small studies produce evidence that can withstand scrutiny and replication.

Highlights:

• Pre-specified adaptive protocols: Adaptations, such as adjusting sample size, dropping ineffective arms, or modifying endpoints, are planned before unblinded data review, preserving integrity.
• Risk and resource optimization: Trials can stop early for success, enabling efficient use of resources and minimizing patient exposure to ineffective or harmful treatments.
• Effective data integration: High-resolution molecular, clinical, or imaging data enable precise patient subgroup definitions, improving understanding of heterogeneity and treatment effects.
• Regulatory engagement: Agencies such as the FDA and EMA increasingly endorse adaptive designs when methodologies follow rigorous statistical standards and transparency.
• Ethical enhancements: Allowing modifications based on accumulating evidence ensures participants receive better treatments sooner, aligning with ethical research principles.⁶

Case Example: Adaptive Trials Help Improve Lung Cancer Research

AstraZeneca’s studies with Tagrisso (osimertinib) show how adaptive clinical trials can make cancer research more flexible and effective. In FLAURA2 studies, doctors tested Tagrisso both alone and in combination with chemotherapy. The trial design allowed them to look at results along the way and explore how long patients should continue chemotherapy to get the best results. This helped researchers quickly identify which treatment combinations work best for different patient groups.^12,13

Another study, called ORCHARD, used an adaptive approach in which new treatment combinations were added or adjusted as scientists learned more about why some patients stopped responding to Tagrisso. Instead of starting a new trial each time, the same study evolved to test new drug options, saving time and helping patients access potentially better treatments sooner.⁷

Together, these trials show how adaptive designs help make cancer research faster, smarter, and more personalized for patients with EGFR‑mutated lung cancer.

2. Regulators hold the key to unlocking wider adoption.

Agencies should move beyond case-by-case exceptions toward structured frameworks that explicitly define how micro-trial data, registry evidence, and AI-generated insights can be weighed in regulatory assessments. Forward-looking pilots, such as conditional approvals tied to registry follow-up, can serve as bridges between methodological novelty and public trust.

Highlights:

• From ad-hoc evidence to predefined evidence hierarchies: Agencies and buyers are moving to explicit frameworks that say which micro-trial designs, registry endpoints, and AI-derived outputs are acceptable.
• Regulators are enabling pragmatic, embedded designs: Draft and final guidance now encourage trials integrated into routine care (point-of-care/streamlined randomized designs) and greater use of RWE for regulatory decisions.
• AI as a regulatory partner: AI systems are not just analytical tools but components of the evidence pipeline, providing reproducibility, bias assessment, and predictive accuracy validation during regulatory review.

Case Example: Regulatory Agencies Embracing AI Tools

In March 2025, the EMA’s Committee for Medicinal Products for Human Use (CHMP) issued a qualification opinion for AIM-NASH, an AI tool that assists in pathologist scoring of liver biopsy samples.⁹

With that qualification, data produced via AIM-NASH in compliant settings was accepted by the EMA as valid supporting evidence in future MASH therapeutic submissions. The tool helps reduce variability across pathologists, improving consistency and reproducibility in assessments

3. Patient organizations must be recognized as co-architects, not just participants.

By championing registry enrollment, shaping endpoint selection, and advocating for transparent consent processes, they can ensure that rare disease research remains anchored in lived experience. Registries thrive when patients see value in contributing not only to science but to their own care journeys.

Highlights:

• Patient-led registry governance: Regulatory, academic, and industry partners increasingly integrate patient advocacy organizations (PAOs) in early framework design. PAOs help define endpoints, advise on meaningful outcomes, and drive ethical consent and transparency processes. 
• Policy and global alignment: The 2025 World Health Assembly’s action plan promotes rare disease registry investments, harmonization of ethical oversight, and digital infrastructure explicitly emphasizing patient-centric data governance and inclusion in study logistics to ensure representation across diverse communities and socioeconomic backgrounds.
• Value-driven participation: Patients increasingly see personal benefit through linked care pathways, feedback loops, and access to updates from the registries they contribute to. Engagement is maintained through digital platforms, multilingual tools, and feedback mechanisms, boosting retention and registry completeness.^10,11

Case Example: FDA’s START Pilot Program

The FDA CDER & CBER has launched the Support for Clinical Trials Advancing Rare Disease Therapeutics (START) pilot program to accelerate development of therapies for rare diseases. It allows sponsors to have more frequent interactions with FDA staff to troubleshoot product-specific development issues, including clinical study design, choice of control group, population selection, etc. It is part of a broader initiative, Accelerating Rare disease Cures (ARC). However, START is more about regulatory feedback and trial design than about registry building. It helps refine protocol design, endpoints to better meet both regulatory and patient-relevant criteria.⁵

4. AI and digital platforms are not add-ons but are foundational to trial efficiency.

The integration of machine learning for repurposing, decentralized trial technologies for access, and telemedicine for patient retention can redefine feasibility. Rare disease research must not be constrained by geography; digital tools make inclusivity achievable.

Highlights:

• Decentralized design for access: DCT adoption surged in 2025, with mobile and telemedicine components allowing patients worldwide to participate without visiting centralized sites. Mobile clinicians and AI-driven remote monitoring ensure both trial compliance and data fidelity
• Digital inclusivity: Telehealth, electronic consent, and wearable devices are now integral for real-time monitoring and retention, particularly beneficial for geographically dispersed rare disease populations.
• AI as a trial backbone: Innovative models use graph neural networks and deep learning to identify new therapeutic opportunities for rare and ultra-rare diseases, cutting early-stage development time drastically.

Case Example: Rare Disease Study Uses Digital Health Tools

Fortrea, a specialist CRO, designed a rare disease investigational study integrating digital and decentralized technologies to address these challenges. It embedded telehealth, home nursing, ePROs/eDiaries, and eConsent to enhance data capture, patient engagement, and protocol adherence. Recruitment finished 52 days early, retention reached ~85%, and primary endpoint compliance was 94.8%, with no investigational medical product-related dropouts. This illustrates that digital tools are foundational to improving trial feasibility, inclusivity, and data quality rather than serving as optional add-ons.⁸

5. Global consortia must push past fragmentation.

A rare disease in one country is often equally rare elsewhere. Interoperable registries, shared biobanks, and harmonized data standards transform isolated efforts into global networks. The European examples (ERDRI, DARWIN EU) should be treated not as regional exceptions but as templates for worldwide adoption. Strengthen partnerships between the WHO, FDA, EMA, and regional bodies to create unified approval standards, privacy frameworks, and synthetic control arm guidelines and fast-track conditional approval pathways for ultra-rare disease therapies.³

Highlights:

• Registries and biobanks reuse across borders: Rare disease registries are increasingly being designed for interoperability, common data models, and shared metadata so that patient data (clinical, molecular, and imaging) and biospecimens can be queried and pooled across countries. This unlocks higher statistical power and accelerates discovery.
• Synthetic control arms, real-world data, and natural history data as a global asset: With ultra-rare disease trials, randomized controls are often infeasible. Regulatory bodies are more open to using synthetic or external control arms drawn from pooled, interoperable natural history datasets across jurisdictions when standards are transparent.
• Regulatory policy developments creating clarity for small populations: New guidance, principles, and pilot programs are being introduced to give clear expectations on data quality, evidentiary support, and use of external data, synthetic arms, etc., reducing uncertainty for sponsors.

Case Example: RareLink Interoperable Registry Framework

RareLink is an open-source framework designed to enable rare disease registries to exchange data in interoperable formats. It links REDCap-based registries to global standards such as GA4GH Phenopackets and HL7 FHIR R4. It builds on the ontology-based rare disease common data model. Preconfigured pipelines to export/import data between local registry instances and standardized schemas, and adherence to international profiles like HL7’s International Patient Summary and Genomics Reporting profiles are some features of the initiative.¹⁴

6. Monitor and publish progress.

Mandate publication of trial outcomes, recruitment metrics, and patient-reported outcomes in centralized open-access platforms. Continually refining protocols based on real-world feedback and evolving data.

Conclusion

Rare disease clinical trials are at a turning point, with rapid advances in micro-trial designs, decentralized methodologies, AI, organ-on-chip platforms, and robust patient registries redefining the landscape. Despite operational, regulatory, and data challenges, these approaches collectively offer new hope for efficient, evidence-based, and patient-focused development in rare diseases, transforming innovation pipelines, market dynamics, and ultimately patient access and outcomes.⁴ Building on policy momentum, digital infrastructure, and increasing global awareness, stakeholders must act decisively to expand access, encourage innovation, and ensure that no patient is left behind.

Continuous investment in infrastructure, collaboration, and harmonization will be essential to realize the full promise of these trends. As global registries and data-driven trial models mature, they will enable more rapid therapeutic advances for some of the world’s most vulnerable patient populations. The recommendations and road map outlined in this three-part series provide a blueprint for harnessing current trends and bridging the gap from research to real-world care for millions living with rare diseases.

References:

1. "Understanding & Overcoming the Challenges in Rare Disease Clinical Trials," 2023. [Online]. Available: https://alexion.com/news-centre-resources/articles/understanding-overcoming-challenges-rare-disease-clinical-trials. [Accessed 2025].
2. C. &. V. Kumar, "Rare diseases: a comprehensive literature review and future directions," Springer, vol. 4, 2025.
3. "Citeline-Rare Disease R&D: Continued Growth Amid Challenges," Feb 2025. [Online]. Available: https://www.citeline.com/en/resources/rare-disease-r-and-d. [Accessed 2025].
4. "Rare Disease Clinical Outcome Assessment Consortium," 2025. [Online]. Available: https://c-path.org/program/rare-disease-clinical-outcome-assessment-consortium/.
5. US Food and Drug Administration, “Support for clinical Trials Advancing Rare disease Therapeutics (START) Pilot Program,” [Online]. Available: https://www.fda.gov/science-research/clinical-trials-and-human-subject-protection/support-clinical-trials-advancing-rare-disease-therapeutics-start-pilot-program.
6. World Health Organization, “Rare diseases: a global health priority for equity and inclusion,” [Online]. Available: https://apps.who.int/gb/ebwha/pdf_files/WHA78/A78_R11-en.pdf.
7. ClinicalTrials.gov, “Phase 2 Platform Study in Patients With Advanced Non-Small Lung Cancer Who Progressed on First-Line Osimertinib Therapy (ORCHARD) (ORCHARD),” [Online]. Available: https://clinicaltrials.gov/study/NCT03944772.
8. Fortrea, “https://www.fortrea.com/sites/default/files/2025-02/retain-patients-in-a-rare-disease-hematology-study.pdf,” [Online]. Available: https://www.fortrea.com/sites/default/files/2025-02/retain-patients-in-a-rare-disease-hematology-study.pdf.
9. EMA, “EMA qualifies first artificial intelligence tool to diagnose inflammatory liver disease (MASH) in biopsy samples,” [Online]. Available: https://www.ema.europa.eu/en/news/ema-qualifies-first-artificial-intelligence-tool-diagnose-inflammatory-liver-disease-mash-biopsy-samples.
10. “E20 Adaptive Designs for Clinical Trials,” September 2025. [Online]. Available: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e20-adaptive-designs-clinical-trials.
11. “Adaptive trials and the new draft FDA guidance on data monitoring committees,” [Online]. Available: https://www.iconplc.com/insights/blog/2025/04/14/understanding-new-draft-fda-guidance-data-monitoring-committees.
12. ClinicalTrials.gov, “A Study of Osimertinib With or Without Chemotherapy as 1st Line Treatment in Patients With Mutated Epidermal Growth Factor Receptor Non-Small Cell Lung Cancer (FLAURA2) (FLAURA2),” [Online]. Available: https://clinicaltrials.gov/study/NCT04035486.
13. “A study of Osimertinib with or without Chemotherapy as 1st line Treatment in Patients with mutated Epidermal Growth Factor Receptor Non-Small Cell Lung Cancer (FLAURA2) - FLAURA2,” [Online]. Available: https://www.astrazenecaclinicaltrials.com/study/D5169C00001/
14. S. L. G. Adam, R. Filip, S. Alkarkoukly, D. Danis, A. Grönke, M. R. Hübner, A. Bartschke, T. Debertshäuser, S. A. I. Klopfenstein, J. Saß, J. Fleck, M. Rehberg, J. Zschüntzsch, E. F. Nyoungu, T. Kalashnikova, L. M. Favela, B. Derfalvi, N. A. M. Wright, S. Moosa , S. Ogishima, O. Semler, S. Wiegand, P. Kuhnen, C. J. Mungall, P. A. Robinson, P. N. Robinson, S. Thun and O. Beyan, “Linking international registries to FHIR and Phenopackets with RareLink: a scalable REDCap-based framework for rare disease data interoperability,” National Center for Biotechnology Research

About The Authors:

Sapna Rani is a lead analyst, pharma R&D – clinical and preclinical research, with over nine years of experience in market research and consulting. Her insights have enabled top pharma companies in their strategic decisions on supplier outsourcing, category management, and planning. In the past year, she engaged in more than 12 market sourcing studies, five supplier data visualizations, and multiple quick reactive analyses across clientele for global and regional requirements

Monica Nandagopal is a senior research analyst, pharma R&D – clinical and preclinical research, with over five years of experience in market research and consulting. Her insights have enabled top pharmaceutical companies in their strategic decisions on supplier outsourcing, category management, and planning. In the past year, she engaged in more than 12 market sourcing studies, five supplier data visualizations, and multiple quick reactive analyses across clientele for global and regional requirements.