Natural history studies play a crucial role in rare disease drug development, providing insights that advance discovery and shape clinical trial design. The benefits of these studies also extend to understanding patient journeys, clarifying the standard of care, identifying disease-specific centers of excellence, and surfacing opportunities to improve patient care.
In this blog post, we offer seven tips on designing and operationalizing natural history studies to support successful rare disease therapeutic development.
1. Determine the right timing for a natural history study
In their draft guidance titled Rare Diseases: Natural History Studies for Drug Development, the FDA encourages sponsors to carefully consider the timing of natural history studies in the development process.[i] The guidance includes a discussion of the pros and cons of implementing natural history studies at different stages of clinical development. Though these studies are likely to be most useful if they are completed prior to interventional studies, they are sometimes performed in parallel with clinical trials.
2. Focus on the disease, not the treatment
Though natural history studies may collect information on therapeutic interventions, it is essential to ensure that data gathering parameters include measures assessing all facets of the disease of interest. This includes disease presentation, manifestations, morbidity, and progression. Ideally, the data collected should be sufficiently robust to support the development of multiple therapeutic options.
The FDA recommends that sponsors collaborate with patient support or advocacy groups to obtain input from patients, caregivers, and clinicians with experience in caring for individuals with the target rare disease. This feedback helps ensure that the outcomes assessments selected reflect clinically meaningful, relevant aspects of the disease and the patient experience.
3. Ensure high-quality data
Data quality and monitoring are essential for any study subject to regulatory scrutiny. Even if the planned natural history study will not be included in regulatory submissions, it is critical to ensure high-quality data. These data may be needed to support questions posed by regulatory authorities throughout the development process. While 100 percent source data verification is not mandatory, some level of monitoring is recommended. It is essential to define critical data elements, reporting standards, and methods for handling missing data or data variations in studies collecting prospective data.
4. Understand the impact of standard of care
In natural history studies, data collection requirements and assessment type and frequency will need to encompass and align with standard of care, which may differ among providers or institutions and may change over time. Standard of care may provide insight into selection of meaningful endpoints and appropriate assessments for measuring or monitoring disease progression. It is also essential to understand how standard of care may impact site feasibility, study duration, and inclusion or exclusion criteria.
5. Standardize collection methods and terminology
Standardization ensures universal usage and application, especially in global settings. If the study includes questionnaire elements, it is essential to ensure consistency in questionnaire administration as variances can impact outcome data.
6. Build a flexible study database
The study database should include a broad range of data points, including disease severity, nuances, phenotype variances, and options for unknown data. Importantly, the database should be flexible enough to be adjusted as new knowledge emerges. As the study may collect unexpected but potentially meaningful data, the statistical analysis plan should also allow for flexibility and amendments.
7. Select an appropriate data collection model
Like interventional clinical trials, data collection in natural history studies can be performed through either local sites or one or more central sites. With local sites, data are collected by a patient’s existing provider and submitted to central data collection. This approach limits burden on the patient but may introduce variability. With central sites, all study assessments are performed at a small number of experienced sites. Though this increases consistency and helps mitigate the risk of missing data or protocol deviations, it may increase the burden of study participation.
Combination models offer a balanced approach where complex assessments are performed at central sites and routine assessments are completed at local sites. Sponsors may also opt for a patient-reported model where all assessments and data collection are performed in the patient’s home. While this approach is the most convenient for the patient, it requires significant training of in-home providers and may introduce variability. The most appropriate data collection model for a natural history study will depend on overall objectives.
Natural history studies require careful planning and built-in flexibility to accommodate changes that may be needed as new knowledge emerges. To learn more about designing and operationalizing natural history studies in rare diseases, download our white paper.
[i] U.S. Food and Drug Administration. Rare Diseases: Natural History Studies for Drug Development—Draft Guidance, Published March 2019. Available at https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM634062.pdf.