Despite continued progress in rare disease research, only 5% of rare diseases have an FDA-approved treatment, leaving many patients with limited therapeutic options and delayed access to trials. One of the most persistent challenges in orphan drug development is identifying and reaching eligible patients, particularly given the small and geographically dispersed populations involved. Traditional site models and recruitment strategies are frequently insufficient and impractical in these settings. However, recent advances in AI, genetic screening, and flexible site activation models are creating new opportunities to improve both reach and speed.
As part of a roundtable discussion hosted by Sano at the World Orphan Drug Congress in April 2025, experts from biotech, pharma, and advocacy groups explored how these emerging tools can reshape trial execution in rare disease. This blog highlights key strategies discussed, focusing on how more innovative, data-driven approaches to patient-finding and site design can help accelerate therapeutic development.
Smarter patient matching with AI and genomic tools
A major focus and concern for stakeholders in the rare disease space is the challenge of finding patients. New strategies are emerging to improve patient identification, particularly with advancements in AI models. For instance, a large language model (LLM) demonstrated high accuracy in identifying and matching patients to clinical trials for which they were eligible. Experts also mentioned the relevance of hotspot mapping techniques, which use geographic and clinical data to identify regions where higher concentrations of potentially eligible patients are likely to be found.
Moreover, AI can be used to model trial feasibility before protocol design to help identify eligible patient populations and optimal sites. This is currently limited by the small size of rare disease populations, which may be insufficient to train AI models. However, this shortcoming could gradually be mitigated with the expansion of natural history and real-world data studies. In addition, the genotype-first screening approach is enabling the proactive detection of individuals carrying rare pathogenic variants, many of whom remain undiagnosed through traditional clinical pathways. Together, these innovative models offer a more data-driven, scalable, and precise approach to patient-finding.
Flexible site models: Just-in-time and virtual activation
Trial setup is often associated with delays and inefficient start-and-stop phases. The just-in-time (JIT) approach was created to reduce unnecessary delays and make transitions between phases smooth. This entails engaging with study sites early in the process, prior to formal site approval. This approach prioritizes patient identification and streamlines the rest of the process thereafter. Virtual site models extend this flexibility further, enabling participation even when patients are geographically distant or unable to travel. Mobile sites offer an alternative way to deliver trial-related services closer to patients' homes, although such models can introduce clinical and research complexities that require thoughtful management.
Adapted from Lynam et al., 2012.
Global recruitment strategies
Since many rare diseases are inherited, families in regions with higher disease prevalence represent a unique opportunity for recruitment. This is particularly true in countries where consanguinity is present and inherited diseases may be more easily identified within extended family networks. Leveraging these high-yield populations can significantly improve the efficiency of trial enrollment and facilitate earlier identification of eligible patients.
To overcome the persistent challenge of patient recruitment in rare disease, stakeholders also highlighted the promise of cross-border strategies. A notable example of a global strategy is Sanofi’s Rare Disease Registry that includes data on patients from over 65 countries, representing a diverse population. However, effective and widespread cross-border coordination remains a limiting factor. Establishing global patient registries and partnerships will require commitment, regulatory alignment, data interoperability, and infrastructure investment.
These emerging models signal a shift toward faster, smarter, and more inclusive clinical research in rare disease. By rethinking how and where we find patients, and how we bring trials to them, we can begin to overcome one of the field’s most persistent barriers. Continued collaboration across data, technology, and policy will be key to making these strategies the norm rather than the exception.
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