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Needle in the haystack – why finding rare disease patients is challenging

Forty years after the initial instatement of the US Orphan Drug Act, awareness of rare diseases, as well as the number of available treatments has increased significantly. Despite this positive trend, however, over 90% of rare diseases still lack effective treatments, a situation exacerbated by the challenges drug developers face when trying to bring novel treatments to rare disease patients¹.

A significant challenge within the rare disease field is finding patients who could benefit from a specific treatment. While it might be assumed that the unmet needs of a rare disease would automatically drive demand for a novel therapeutic—whether for participation in clinical trial or for access to newly approved drugs—the reality can often be quite different. In fact, matching patients with treatments can become a literal search for a needle in a haystack for drug developers.

The time to diagnosis is for rare diseases typically long, averaging 4.7 years². Time to diagnosis can be even longer for many patients, with physicians often struggling to piece together the complex puzzle of signs and symptoms that indicate a rare disease. This can result in low diagnostic accuracy, with around three-thirds of patients being misdiagnosed at least once².

Rare diseases exist on a spectrum, ranging from the easily detectable to the elusive (Figure 1). Rare diseases that are relatively easy to recognise, such as Heamophilia³, typically have high disease awareness and a well-understood pathophysiology that allows for clear symptom description. For monogenic disorders, family history and distinct phenotypic features can often aid in the recognition of the disease.

In the best-case scenario, such rare diseases also have clear diagnostic tests, making them identifiable. These tests can be based on genetic markers, such as testing for CAG repeats in the HTT gene for Huntington’s disease⁴ or SMN1 gene deletion for spinal muscular atrophy⁵, or on laboratory biomarkers, such as haemoglobin electrophoresis for sickle cell anemia⁶ or sweat chloride tests for cystic fibrosis⁷.

Rare diseases that are more difficult to recognise, such as rare metabolic syndromes or complex autoimmune conditions, often lack a clear description of tell-tale symptoms and frequently do not have clear diagnostic tests available, making them more elusive. However, even for rare diseases that are more easily recognised, diagnosis can take a significant amount of time, often depending on factors of chance, such as encountering the right physician or advocate who understands the context of the disease symptoms and can initiate the appropriate diagnostic tests.

When designing patient-finding strategies, drug developers need to understand where on the spectrum the rare disease they are targeting is located and adjust their methods accordingly. Notably, the availability of novel treatments and the accompanying patient-finding efforts can significantly increase disease awareness and diagnosis rates, potentially shifting a disease from being virtually unrecognisable to being both recognisable and testable. This can be a win-win for both patients and drug developers.

Assembling the puzzle pieces – how improved disease understanding can help find rare disease patients

In the rare disease space, disease understanding, and improved diagnosis rates are closely linked.

Genetic alterations are fundamental to many rare diseases, with around 80% believed to have a genetic component⁸. Notably, even diseases with clearly defined genetic causes can still vary widely in their genetic background, pathophysiology and treatment strategies; for example, cystic fibrosis has over 2,000 known mutations, many of which induce different disease manifestations^9,10.

Comprehensive genetic characterisation of a rare disease is crucial for expanding knowledge about variants and prevalence, ultimately leading to better diagnosis. This can help drug developers to identify patients that can participate in clinical research for novel treatments and identify patients who could benefit from newly approved treatments. In the case of diseases with low awareness and insufficient characterisation, drug developers, patients and their advocates can help to increase the coverage of screening by advocating for inclusion of known disease mutations in the screening panels used by treating physicians, specialised clinics, and non-profit organisations. Having a clearly defined genetic test can help to increase disease awareness and recognisability (Figure 1).  

In addition to genetic profiling, drug developers need to define a comprehensive set of disease symptoms, including potential tell-tale signs that might be common even among patients with varied disease presentations.

Many rare diseases affect multiple organ systems and can require the expertise of different specialists. Furthermore, treatment approaches can vary widely across countries and among patients of different ages, sexes, genders, and socioeconomic backgrounds⁹. A deeper understanding of disease trajectories and careful mapping of key points along the patient journey can help identify the stakeholders in the healthcare system that patients with a specific rare disease are likely to consult at different stages of their disease.

Once these are identified, they can be targeted through mail, phone calls, visits, or through broader disease awareness campaigns to help identify potentially undiagnosed or misdiagnosed patients. This can help move a disease from towards being recognisable (Figure 1), which will not only benefit the individual patient but also future patients, who will likely have a shorter time to diagnosis.

Uncovering the hidden patterns – how innovative methods can help to find rare disease patients

Innovative methods can complement the classical patient-finding strategies outlined above. By combining advanced genetic and other screening methods with AI algorithms, it is possible to uncover the patterns that underlie rare diseases, enhancing disease understanding and diagnostic efficiency. This approach is especially valuable for ultra-rare diseases that are difficult to recognise and in cases where drug developers have limited budgets for patient-finding.

Advances in next-generation sequencing and the improved interpretability of disease-causing variants have generated a wealth of genomic data, with an expanding catalogue of variants in approximately 4,000 genes linked to around 6,500 diseases¹¹, along with their annotated phenotypes.

Artificial intelligence (AI) algorithms can be valuable tools to boost the efficiency of genetic diagnoses and fill in gaps in the often-limited rare disease datasets¹². Integrating genetic profiles with other types of patient data, such as facial features, which can be uniquely linked to monogenic rare diseases can further improve diagnostic precision.

Such AI-based genotype-phenotype integrations might be especially well-suited to detect ultrarare diseases and to account for phenotypic variability within rare diseases ^13,14. The latter will be especially important to find undiagnosed patients who do not fit into the classical picture that physicians and drug developers might have of a patient affected by a specific rare disease. While many of the methods employed in this space are still at an early stage, in the future the combination of genetic and phenotypic screening and AI could turn currently “unrecognisable” diseases into “testable” ones (Figure 1).

While AI can help to effectively translate genetic knowledge into disease understanding, matching patients with the right treatments still requires a human element. Initiatives like the Broad Institute’s Matchmaker¹⁵ can link genetic causes to diagnoses and help connect patients with appropriate healthcare professionals across the world.

In addition to helping physicians derive diagnoses based on genetic and phenotypic features, AI tools can also help empower patients to actively seek diagnosis for their disease and find suitable treatments and clinical trials. For example, TrialGPT allows patients to prompt a ChatGPT-like large language model to find and rank appropriate trials^16. Informed patients are more likely to advocate for treatment and can thus play an important part in making rare diseases more “recognisable”.

However, AI algorithms are only as good as their input data, and limitations arising from biases in genomic datasets and concerns about patient anonymity need to be considered when developing AI-driven diagnostic algorithms in the rare disease field.

Importantly, while AI methods can be an important tool for more effectively matching patients to treatments, they will by no means replace the need for human interaction but can help humans make connections more quickly and thus increase patient-finding efficiency.

Learning from the experts – how integrating patient experiences can help find rare disease patients

In the rare disease space, patients and their caregivers often play a pivotal role in orchestrating care and advocating for the development of and access to novel treatments¹⁷. As the primary stakeholders, they are invaluable resources for clinical research ¹⁷and for patient-finding strategies. Understanding the experiences and needs of patients can help raise awareness, which can be transmitted through social media campaigns, educational initiatives such as podcasts, videos or dedicated websites, collaboration with influencers who live with a rare disease, and patient events organised in partnership with patient advocacy groups. Integrating patient experiences into patient-finding strategies can also help to define symptom sets and identify relevant physicians, thus increasing the recognition of a rare disease.

Patient advocacy and support groups act as an extension of the patient, bridging the gap between drug developers, physicians, patients, and other stakeholders ¹⁸. These groups often maintain records and contacts for all patients affected by a rare disease and manage patient registries, which can enhance recruitment for clinical trials. They also serve as vital sources of canonical knowledge about the disease, its natural history, symptoms, and care options—knowledge that may even exceed that of many physicians.

Collaborating with patient organisations can help amplify disease awareness through various channels, such as specialised publications, lobbying efforts, media outreach, and appearances at medical conferences and other events.

Including the voices of patients and their advocates may be the most crucial step in making rare diseases more recognisable to successfully match patients to novel treatments.

Conclusion

The number of treatments being approved for rare diseases is increasing as drug makers and regulators work together to overcome the key challenges of the process. However, novel treatments for rare diseases can only have a meaningful impact if they reach the patients who need them. Therefore, successful patient-finding strategies are essential. Innovative and data-driven technologies will synergize with patient-centric approaches to enhance disease understanding and awareness, thus making rare diseases more “recognisable”, forming the basis for successfully diagnosing rare disease patients.

Contact us: The Healthonauts help rare disease companies launch and commercialise their products to address patients’ high unmet needs. Combining extensive biotech industry expertise with agency support, The Healthonauts accelerate the development of rare disease products. Learn more about our work at The Healthonauts: https://www.thehealthonauts.com/en/

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References

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