On the path to U.S. commercialization, every pharmaceutical product makes a stop at the Food & Drug Administration (FDA). Whether that stop becomes a waystation or the end of the line may depend on the degree to which the presenters share the FDA’s understanding of key terminology. As our experience at pre-IND meetings illustrates, this is particularly vital when presenting real-world data (RWD) and real-world evidence (RWE).

Talk the talk: Three key terms and what they mean

Three terms come up frequently in FDA meetings. Understanding how the FDA interprets them—and therefore precisely what they expect to see in a presentation of RWE—can help you build a strong foundation for your FDA interactions.

Clinically meaningful data

Any patient-specific efficacy endpoint must address clinically meaningful aspects of the disease; precisely what that means varies depending on the disease state and subtype. The FDA determines whether the observed treatment effect size is clinically meaningful based on the context of the risks identified in the clinical development program: drug-specific risks, drug-class risks, and other risks such as those related to the formulation, route of administration, and manner of use. In short, the FDA asks: How does a drug impact either the root causes or the symptoms of a disease, and is that impact sufficient to balance the risks of taking the drug?

Study data standards

For researchers to draw reliable conclusions from data, those data need to be parallel. Within the confines of a clinical trial, that standard is relatively simple to achieve. Sponsors set clear protocols around precisely when and how to measure the primary endpoint(s). All study investigators follow those parameters, and sponsors can be confident that data are uniformly collected, whether in Montana, Mongolia, or Mauritania. The FDA will, of course, want to know the protocols and will require proof that the protocols were followed universally.

While demonstrating study-data standards is occasionally a challenge within clinical trials, it can be a serious issue with RWE, which uses data collected by physicians as parts of their normal treatment protocols. They may use different instruments, different calibrations—perhaps even slightly different definitions of each data type. Yet, it is crucial to standardize the data collection and handling to ensure both the quality and interpretability that FDA criteria demand.

Historical control group

Historical control groups are an important factor in using RWD; they enable pre- and post-treatment comparisons. However, for accuracy, all patients in a historical control group must be carefully and clearly matched (based on important baseline disease characteristics) to patients in the treatment group a priori in order to minimize bias. Only then can data illustrating the natural history of the disease state be compared to changes in clinically meaningful parameters of the treatment group, serving as substantial evidence of safety and efficacy.

Ultra-rare diseases: Raising the bar for meeting FDA standards

When a target disease state has fewer than 50 living patients globally, medical and scientific knowledge may be severely limited, and natural history data even more so. Finding appropriately standardized RWD, clear evidence of clinical meaningfulness, or a perfectly balanced historical control group can be a challenge. Yet the statutory requirements and regulatory terminology used by the FDA are the same for such rare and orphan diseases as it is for more common ones.

Nonetheless, in a population where mounting a trial is a logistical nightmare, the ability to harness RWD can be pressing. It is also possible, if a sponsor begins with a clear understanding of how the FDA considers data and a willingness to listen carefully for what will be needed to meet its requirements.

No standard deviations: The key to successful regulatory interactions

Especially for rare and ultra-rare diseases, sponsors are increasingly interested in harnessing the insights provided by RWE as they move through the drug development process. The FDA is open to these types of data—but it holds them to the same stringent standards as data collected through traditional clinical trials. Success begins with a deep understanding of what, precisely, the FDA expects to see.

• To confirm you are on the path to regulatory approval, ask for a pre-IND meeting with the FDA, especially with orphan programs. The FDA may grant more than one pre-IND meeting to review endpoints and provide additional guidance.
• To minimize bias, standardize clinical outcome assessment tools used to collect and handle data—especially when those data are real-world.
• To ensure the FDA feels you have reached reliable conclusions, organize, and present your information in a way that allows a direct comparison of like data, illustrating clinically meaningful efficacy.

Above all, listen carefully to the FDA’s feedback, to understand what more the Agency may want as you establish study data standards, use natural history data as the historical control group, and present clinically meaningful real-world data.

Gain a strategic partner for your presentation to the FDA. Premier Consulting has a deep and rich history of successfully presenting data to the FDA, in both pre-IND meetings other types of Agency interactions. We can help guide you through the appropriate nuances, while identifying the most aggressive, optimized path to approval—even for programs where no playbook exists. Read our case study to discover how Premier Consulting has used RWD and RWE to support an ultra-rare orphan program, or contact us to learn more about how we can support your program.

Author:

Ruth E. Stevens, PhD, MBA
Strategic Advisor