Real-World Evidence Supporting Rare Disease Therapy Approvals Gets Mixed Response from FDA

Rare Daily Staff

The small size of patient populations with certain rare diseases creates challenges from drug developers to produce adequate clinical evidence to satisfy regulators’ needs to support approval of a therapy, but there is growing interest in the use of real-world evidence to address this gap.

A new study in the Orphanet Journal of Rare Disease by Shailaja Vaghela of HealthEcon Consulting and others explored the use of real-world evidence to support applications to the U.S. Food and Drug Administration for the approval of new drugs. While other studies have explored the FDA’s view on real-world evidence in new drug applications, the authors said that no previous study explicitly focused on rare disease therapies and their reliance on real-world evidence to support efficacy outcomes.

Real world evidence—including data from such sources as electronic health records, claims and billing activity, and registries—can provide an important source of additional insight into a disease or therapy and can provide context beyond a randomized control trial.

The use of real-world evidence in regulatory decision-making is not universally accepted, and the authors said that opinions within regulatory agencies are evolving. Nevertheless, there is a growing recognition of its value in the drug approval process.

In the United States, the 21st Century Cures Act 2016 sought to accelerate the development of novel therapies and their availability to patients. It also broadened the FDA’s application of real-world evidence beyond post-market surveillance and recognized its potential to inform regulatory decision-making throughout drug development.

While the FDA has demonstrated a willingness to accept real-world evidence in drug reviews, the authors found that there is a lack of knowledge among stakeholders regarding the agency’s acceptance of real-world evidence for certain orphan indication approvals.

After starting with 868 applications (772 New Drug Applications and 96 Biologics License Applications) approved by the FDA between January 1, 2017, and October 31, 2022, the researchers reduced this to include only applications for non-oncology orphan indications where real world data was used to support efficacy outcomes. The researchers identified a total of 20 (12 New Drug Applications and 8 Biologics License Applications) for rare disease therapies for the review.

The FDA evaluated the appropriateness and quality of real-world data study designs in the reviewed applications. Of the 20 applications in rare disease, nine (45 percent) received overall positive feedback from the agency on their real-world data. That was because of a significantly large effect size, justifiable real-world data design, and/or the use of external controls for comparison or contextualization in the study.

The agency criticized real-world data in 11 (55 percent) applications. It cited concerns about differences in patient population, potential selection bias, measurement errors, imprecision of population matching, missing information on key input elements, or potentially subjective elements of study endpoint definitions.

Of these applications, six did not report real-world data in their label claims, whereas five applications reported real-world data in their label claims despite some of the aforementioned issues.

The researchers said it was not possible to draw a definitive pattern to strongly recommend real-world data design methodology for rare disease regulatory submissions because the FDA’s review process varied by application, depending on the rareness of a disease, rationale for the use of real-world data, quality of real-world evidence study design components, and other contributing factors.

With the growing use of real-world data in regulatory applications, the researchers said that there is an opportunity to enhance both the understanding of FDA’s expectations for utility and the quality of real-world data. But they said it will require additional research to create a roadmap with useful recommendations to enhance the scientific validity of this data.