By Billy Amzal, Head of Strategic Consulting, Phastar
Introduction
Rare disease research demands tailored development and access strategies supported by innovative trial designs and dedicated analytics. From Bayesian designs to data augmentation approaches that strengthen probability of success, specialist CROs and experienced consultants are helping deliver smarter, faster, and more efficient drug development, ultimately bringing better treatments to patients sooner.
In his recent article for Rare Revolution Insider and in our latest webinar, Billy Amzal, Head of Strategic Consulting at Phastar, explored the key innovations shaping the future of rare disease research.
A Growing Focus on Rare Diseases
Despite regulatory and health economic challenges, rare diseases remain a major focus for pharmaceutical and biotech companies due to their significant societal and ethical burden.
In 2024, more than half of novel drugs approved by the FDA were orphan therapies [1]. Furthermore, industry-sponsored rare disease trials and the number of conditions being targeted both reached record highs [2].
However, traditional clinical trial designs are often ill-suited to this space. Small populations, high variability, and limited historical data call for a new era of smarter, adaptive design and analytics, one that reduces risk and increases value for sponsors tackling previously unmet medical needs.
External Control Arms and Synthetic Cohorts
Large randomized controlled trials (RCTs) are often impractical, or even unethical, in rare disease settings. External control arms (ECAs) provide a solution, offering comparator data without randomization. ECAs can be developed through:
- Trial arm emulation using matched historical or real-world controls
- Predictive modeling and statistical simulations
- Synthetic patient generation using AI-based digital twins
These methods can accelerate development, enhance regulatory confidence, and improve generalizability to real-world populations. One compelling example is a treatment for refractory precursor B-cell acute lymphoblastic leukemia, granted accelerated approval based on data from a single-arm Phase II study supported by historical and external control arms [3]. This strategy helped provide the robust evidence needed for regulatory confidence and faster patient access.
Data Augmentation and Bayesian Borrowing
Data augmentation, particularly Bayesian dynamic borrowing, enables integration of prior clinical or real-world evidence into ongoing trials while maintaining statistical integrity.
Bayesian trial designs can require 30% to 2,400% fewer participants than frequentist models [4], as they enable borrowing of information from prior or similar studies [5]. This simulation-based optimization increases efficiency, shortens timelines, and improves the likelihood of meaningful outcomes [6].
For example, when Chinese patients were not included in a global drug study, a Bayesian dynamic borrowing (BDB) approach was used to efficiently leverage existing global data. This allowed robust local evidence generation and accelerated medicine availability in China [7].
However, rigorous methodological planning is critical to ensure credible, actionable results, a principle reinforced by the FDA and MHRA in their recent guidance [8,9].
Harnessing Real-World Data
Over the past five years, access to and interoperability of real-world data (RWD) have improved dramatically, transforming drug development and evaluation frameworks. Regulators are now recognizing RWD as an important complement to clinical trials for rare diseases.
Integrating RWD into trial planning can help sponsors:
- Characterize disease progression patterns and disease epidemiology
- Identify fast/slow progressors and clinically relevant endotypes
- Select meaningful endpoints
- Estimate baseline risks and standard-of-care outcomes
- Model long-term treatment effects through in-silico simulations
Real-world insights are increasingly informing not just trial design, but regulatory submissions, health technology assessments, and post-market decision-making. Examples include using RWD to optimize heart failure treatment switching strategies, guide pricing agreements, and support public health projections for cholesterol-lowering therapies.
Looking Ahead
The convergence of innovative trial designs, data augmentation, and RWD is enabling a new era of rare disease research. Success relies on rigorous methodological planning, statistical expertise, and strategic collaboration. By partnering with specialist CROs and biometrics experts, pharmaceutical and biotech companies can de-risk development, improve evidence generation, and ultimately bring better therapies to patients faster.
References
1.https://www.fda.gov/drugs/novel-drug-approvals-fda/novel-drug-approvals-2024
2.https://www.citeline.com/en/resources/rare-disease-r-and-d
3.Khachatryan, A., Read, S. H., & Madison, T. (2023). External control arms for rare diseases: Building a body of supporting evidence. Journal of Pharmacokinetics and Pharmacodynamics, 50(6), 501–506. https://doi.org/10.1007/s10928-023-09858-8
4.Partington, G., Cro, S., Mason, A., Phillips, R., & Cornelius, V. (2022). Design and analysis features used in small population and rare disease trials: A targeted review. Journal of Clinical Epidemiology, 144, 93–101. https://doi.org/10.1016/j.jclinepi.2021.12.009
5.Viele, K., Berry, S., Neuenschwander, B., Amzal, B., Chen, F., Enas, N., Hobbs, B., Ibrahim, J. G., Kinnersley, N., Lindborg, S., Micallef, S., Roychoudhury, S., & Thompson, L. (2014). Use of historical control data for assessing treatment effects in clinical trials. Pharmaceutical Statistics, 13(1), 41–54. https://doi.org/10.1002/pst.1589
6.Cetinyurek Yavuz, A., Nur Fayyad, M. B., Jiang, C., Brion Bouvier, F., Beji, C., Zebachi, S., Hayek, G. Y., Amzal, B., Porcher, R., Tanniou, J., Roes, K., & Rodwell, L. (2025). On the concepts, methods, and use of “probability of success” for drug development decision-making: A scoping review. Clinical Pharmacology & Therapeutics, 117(4), 967–977. https://doi.org/10.1002/cpt.3571
7.Edwards, D., Best, N., Crawford, J., Zi, L., Shelton, C., & Fowler, A. (2023). Using Bayesian dynamic borrowing to maximize the use of existing data: A case study. Therapeutic Innovation & Regulatory Science, 58(1), 1–10. https://doi.org/10.1007/s43441-023-00585-3
