In May 2019, the U.S. Food and Drug Administration (FDA) released a draft guidance that encourages sponsors and applicants to include a uniform cover letter to identify real-world evidence (RWE) submitted as part of a regulatory submission. The templated document builds on the Framework for FDA’s Real-World Evidence Program.
It is yet another milestone in the developing infrastructure surrounding regulatory-grade evidence.
Thanks to the template, the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) will track RWE submissions. The process also represents a broad learning opportunity for all stakeholders in RWE as it will ensure transparency and consistent documentation of key study components.
Tracking enabled by consistent submissions should help answer key empirical questions surrounding RWE. When and where can health care databases provide robust estimates of treatment safety and efficacy? When ought the FDA rely on such evidence for regulatory decision-making?
The FDA’s Dr. Jacqueline Corrigan-Curay said of using RWE in decision-making, “These studies are really complex, and the shift relies on how we build the infrastructure that makes it all possible. It’s an ongoing conversation, and we’re here to engage.”
In response to the FDA’s invitation to engage, industry has weighed in. Two of the top 15 pharmas, a biotech, three trade groups, two individuals, and Aetion shared their comments on the positioning of the cover letter, the terms it includes, and the data sources, technology, and process used to generate and submit real-world evidence studies.
These recommendations are likely to shape the FDA’s guidance; we summarize them here to help our readers prepare for the process:
The positioning of the cover letter:
- Expand the guidance to address alignment on submissions that include RWE between CDER, CBER, and the Center for Devices and Radiological Health (CDRH) for medical devices and combination product submissions containing RWE; and,
- Change the positioning from an example to a recommended format to enable the FDA to consistently track RWE submissions.
The terms in the cover letter:
- Be precise and consistent with terms including RWD, RWE, the type of study design (e.g., randomized pragmatic trial, single arm trial with external control arm, and non-randomized study), and the type of data/data set; and,
- Add definitions for natural history studies, product and/or disease registry data, and data quality and context.
Data sources and technology used to generate RWE:
- Distinguish among specific types of RWD in order to track and analyze submissions by data type. Consistent interpretation of data types, from EHRs to emerging sources of clinical data, will inform a regulatory framework; and,
- Use validated RWE software platforms to enable transparency, reproducibility, and scientific validity in methods and results.
The process for the cover letter:
- Provide information to the sponsor on whether the FDA agrees or disagrees with the sponsor’s classification of RWD/E and how the RWE was used to inform the regulatory decision;
- Consider how the Agency’s tracking can generate publicly available information. The Breakthrough Therapy program provides a model with metrics such as therapeutic area, average review time, and speed of approval. Other relevant metrics for RWE include the submission volume and context, the study design, the data type, and the number of approvals where RWE was supportive evidence vs. the basis of the approval;
- Develop best practices to inform the program (i.e., process and content via Manuals of Policies and Procedures) and other regulators (e.g., the EMA, the PMDA) to enable early harmonization; and,
- Use the aggregated information provided by tracking the cover letters to prioritize resourcing and assessment support by subject matter experts.
The FDA’s encouragement of RWE submissions represents a learning opportunity for the field in designing and executing studies, particularly if the FDA releases complete response letters or provides data from tracking the letters that reveals both successful and unsuccessful approaches. As Amgen’s Dr. Cathy Critchlow stated, “certainly we can learn from the successes, but we also need to make sure we’re learning from the failures.”