How Can Real-World Data (RWD) Be Used in FDA Drug Registration Applications

Introduction of Real-world Data (RWD)

RWD are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources. Examples of RWD include data derived from electronic health records, medical claims data, data from product or disease registries, and data gathered from other sources (such as digital health technologies) that can inform health status.

Real-world evidence (RWE) is clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.

At present, the processes of recording, collecting, and storing real-world data lack strict quality control. This can result in incomplete data, non-uniform data standards, data models, and description methods, all of which create obstacles to the effective use of RWD. Therefore, a key issue is how to make collected real-world data usable, either as-is or after data governance, to meet clinical research objectives. Additionally, assessing whether RWD is appropriate for generating RWE is crucial for supporting drug regulatory decisions. ‍

Applications of RWD

Discover and development stage

RWD contributes to the discovery of new targets and mechanisms of new therapeutic drugs or treatment programs.

Clinical stage

Relevant personnel utilize real-world research to understand the disease burden and unmet needs in drug-related treatment areas and explore market prospects. For example, they can learn about existing treatment products and therapies for the disease from real-world databases.

At the same time, pharmaceutical companies can use RWD to accurately identify hospitals and departments where target patients are located, facilitating rapid enrollment in clinical trials.

Marketing stage

During the drug marketing application and review stages, based on the information on drug safety risks obtained from RWD, companies formulate risk management plans, pharmacovigilance plans, and risk management minimization plans in advance.

Post-marketing stage

After approval for marketing, regulatory agencies continuously track and judge the risk-benefit profile of the drug through the results of real-world studies, and conduct safety and effectiveness monitoring of the drug.

For companies, extensive RWD collection (such as establishing patient registry databases) and conducting real-world research can help explore new applicable populations and dosages for medical solutions. These activities also aid in managing products throughout their life cycle.

In addition, market access departments conduct health economic evaluations and patient quality of life research using RWD to demonstrate the economic value and patient value of innovative drugs. The results can serve as an important basis for inclusion in the medical insurance catalogue and price negotiations.

Types of RWD

There are many different types of RWD, such as Clinical, Medication, Claims, Molecular Profiling, Family History, Mobile Health, Environmental, Patient Reported, Social Media, Literature, etc.

Characteristics and Challenges of RWD

RWD has several characteristics as compared to data collected from randomized trials in controlled settings.

1. RWD are observational as opposed to data gathered in a controlled setting.
2. Many types of RWD are unstructured (e.g., texts, imaging, networks) and at times inconsistent due to entry variations across providers and health systems.
3. RWD may be generated in a high-frequency manner (e.g., measurements at the millisecond level from wearables), resulting in voluminous and dynamic data.
4. RWD may be incomplete and lack key endpoints for analysis given that the original collection is not for such a purpose. For example, claims data usually do not have clinical endpoints; registry data have limited follow-ups.
5. RWD may be subject to bias and measurement errors (random and non-random). For example, data generated from the internet, mobile devices, and wearables can be subject to selection bias.

Therefore, enhancing data quality and generating valid, unbiased RWE from RWD is both a challenge and an essential task.

Current Data Standards Support RWD

In December 2023, the Food and Drug Administration (FDA) issued guidance on Data Standards for Drug and Biological Product Submissions Containing Real-World Data Guidance for Industry. This guidance addresses considerations for the use of data standards currently supported by FDA in applicable drug submissions containing study data derived from RWD sources.

Data standard catalog

The FDA Data Standards Catalog is updated periodically to reflect changes to the supported and required data standards, exchange formats, and terminologies that FDA can process, review, and archive. FDA plans to update the Catalog, and/or issue other guidance documents, to reflect standards for studying data that are derived from RWD sources as they are developed and evaluated.

Format requirements

Sponsors submitting clinical and nonclinical study data (including those derived from RWD sources) in submissions subject to section 745A(a) of the FD&C Act are required to use the formats described in the Study Data Guidance and the supported study data standards listed in the Catalog. Sponsors should refer to the specifications, recommendations, and general considerations provided in the Study Data Technical Conformance Guide (TCG) when submitting study data.

Data transformation

When seeking to conform RWD to FDA-supported data standards, sponsors should consider the necessary data transformations, conversions, or mappings. These are required to produce study datasets in the appropriate formats for a drug submission.

Sponsors should, as early as possible, discuss any planned submission of study data derived from RWD sources with the appropriate FDA review division, including their approaches to data mapping and transformation.

1. Take data from its original RWD format to intermediary formats, including those used for analysis.
2. Ultimately, the study data set prepared for submission to FDA (using the applicable FDA-supported data standards as listed in the Catalog).

Sponsors should describe these approaches, including in the protocol, data management plan, and/or final study reports.

FDA recognizes that a range of approaches may be used to apply the supported study data standards (e.g., Clinical Data Interchange Standards Consortium's (CDISC's) Study Data Tabulation Model (SDTM) or Analysis Data Model (ADaM)) to RWD sources such as EHR or claims data. FDA encourages sponsors to discuss such approaches with FDA. With adequate documentation of the conformance methods used and their rationale, study data derived from RWD can be transformed into SDTM and ADaM datasets and submitted to FDA in an applicable submission.

Proregulations provides a series of customized services designed to help customers make full use of RWD under the FDA regulatory system to improve the success rate of drug registration. If you are interested in our services or need more details, please contact us.