Presentation (CTI)

OBJECTIVES: The use of real-world data (RWD) to construct external controls (ECs) for clinical trials is increasingly common in regulatory submissions, especially when randomization is infeasible or unethical. However, the extent of RWD utilization, contexts involved, and regulatory response from the European Medicines Agency (EMA) remain incompletely understood. This study examines how RWD has been applied in EMA submissions for EC construction, detailing associated data sources, methodologies, and regulatory feedback.
METHODS: We conducted a targeted review of selected 75 EMA submissions from 2015-2023 that utilized real-world evidence (RWE). From these, we identified submissions specifically using RWD for developing ECs. We collected detailed information including disease areas, approval pathway, data sources, study design, and regulatory feedback.
RESULTS: Of the 21 identified submissions using RWD-based ECs, most were in oncology (n=10, 48%), followed by rare genetic diseases (n=5, 24%), neurology (n=3, 14%), and one each in dermatology, hematology, and immunology (5% each). Seventeen submissions (81%) had orphan designation; all four non-orphan cases were in oncology. A majority (n=19, 90%) received expedited approval pathways (conditional approval or exceptional circumstances). The predominant study designs were retrospective non-interventional cohort studies (n=9, 43%) and natural history studies (n=7, 33%). The primary sources of RWD were medical records (n=7, 33%), registries (n=5, 24%), published literature (n=3, 14%), claims databased (n=2, 10%), hybrid RWD-trial datasets (n=2, 10%), multiple sources (n=1, 5%), and unspecified sources (n=1, 5%). EMA considered the RWD-based ECs supportive in 11 cases (52%), of limited importance in 3 (14%), and inadequate in 7 (33%). Frequent regulatory concerns included selection bias, non-comparable baseline characteristics, unmeasured confounding, and inconsistent outcome definitions.
CONCLUSIONS: EMA accepts RWD-based ECs, notably for rare diseases and high unmet medical needs, yet emphasizes methodological rigor and data quality. Advancements in RWE methodologies and bias mitigation are essential to strengthen RWE’s regulatory acceptance and impact.