CMS. DCT. FDA. AI (it’s not what you think). OTC. PI, PMS (they’re not what you think). R&D. RWD. RWE. STD, TSA (they’re not what you think).* The pharmaceutical industry is a veritable alphabet soup of acronyms. One acronym, however ― RWD,  real-world data ― is playing an increasingly important role in clinical research.

According to an article in Global Forum, published by the industry organization Drug Information Association (DIA), over half of all FDA submissions approved for new drugs and biologics in 2019 included a real-world evidence study. In 2020, that figure jumped to 78 percent. In Europe, RWE was used in 40 percent of marketing authorization applications and 18 percent of indication extensions that were submitted to the European Medicines Agency (EMA) in 2018-2019. And in Japan, some orphan drugs have already been based on the assessment of RWD.

The federal Food and Drug Administration (FDA) defines real-world data as “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 (EHRs), medical claims data, data from product or disease registries, and data gathered from other sources (such as digital health technologies) that can inform on health status.” Although the two are interrelated, RWD should not be confused with real world evidence (RWE), which the FDA defines as “the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.”

To promote progress in the field, the FDA has issued a series of draft guidances on aspects of both RWD and RWE in regulatory decision-making. This series includes many aspects of RWD, including: EHRs and medical claims, clinical trial registries, data standards, and regulatory considerations. The DIA notes that work towards global convergence on RWD policies and standards is being done through the International Council for Harmonisation (ICH), the International Coalition of Medicines Regulatory Authorities (ICMRA), and the Council for International Organizations of Medical Sciences (CIOMS).

A report by the EMA and Heads of Medicines Agencies (HMA) reviewed the value of RWD studies conducted between September 2021 and February 2023 aimed at addressing needs of EU regulators and external stakeholders including health technology assessment (HTA) bodies and payers’ organizations. The EMA and the European Medicines Regulatory Network (EMRN) are working on a framework to better integrate RWD/RWE alongside the gold standard of randomized controlled trials into regulatory decisions on the development, authorization, and supervision of medicines.

Real benefits of RWD

Learnings from medical research come from beyond just the petri dish in the lab or results from clinical trials. They are a compilation of factors, based on the overlay of various datasets to determine synchronicities as well as deviations.

The pharmaceutical industry as a whole has much to gain from the use of RWD in clinical trials. One area in particular that can greatly benefit from RWD ― one that has been under the microscope, so to speak, in recent years ― is diversity in clinical trials. To improve a lack of representation in clinical research, the FDA has recommended study sponsors include a Race and Ethnic Diversity Plan and encourages leveraging various data sources, including RWD. For example, a study team can analyze RWD to pinpoint where under-represented patients live and receive care, thus addressing awareness, access, and trust issues by locating nearby clinical trial sites. RWD can also inform the creation of eligibility criteria that optimize enrollment and representativeness of study populations.

Not only can RWD be used for any type of clinical trial, study sponsors can use RWD to inform all stages of a study: planning/strategy, protocol design, site/principal investigator (PI) selection, and patient engagement. RWD can be used to optimize study design, including endpoints that help determine a drug’s safety and efficacy, so the study can produce statistically significant results with fewer participants. Using RWD during the protocol writing process can help with study execution, acceleration of identifying sites, patient recruitment, and shortening trial timelines.

For patients, RWD can increase access to clinical trials, allowing for flexibility in study design such as decentralized clinical trials. RWD can help identify patients at risk for certain diseases. What’s more, it can contribute to increased overall longevity. Of course, policies and procedures must be in place to protect patient privacy at every step along the way. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) protects privacy and confidentiality in RWD collected from sources in compliance with the law. Other de-identification methods, such as tokenism, can be used to further protect patient records. This article, by Citeline Head of Data and Partnerships Thomas Wicks, takes an in-depth look at data anonymization.

“Today, the pharmaceutical industry has access to more data than ever before,” said Dave Laky, Global Head, Regulatory and Data, for Citeline. “I think the research community has grossly underestimated the power of RWD in transforming study design, and we have yet to tap its tremendous potential. With the introduction of real-world data into early stages of the clinical trial planning process, study sponsors can work more efficiently and effectively. Most importantly, they can use this data to improve the entire patient journey, from recruitment to enrollment and participation in clinical trials.”

An article in The Journal of Clinical Investigation lists over 20 potential uses for RWD in the life sciences, including:

• Post-approval drug safety – updating and discovering novel side effects
• Supporting regulatory approval – supporting single-arm experimental trials, digital approvals, biosimilar development
• More efficient data collection – informing trial designers which variables are most often used, which are informative, and which may be redundant
• Long-term, post-trial outcomes – identifying former study participants not being tracked and further track their outcomes

Data mapping: Mix ’n’ match multiple sources for maximum results

As noted above, RWD can be mined from various sources and includes patient data and live lab test results, biomarkers, and genomics data from labs, hospitals, and health systems. When these specific data points are coupled with broad-stroke census figures and data from the Centers for Medicare and Medicaid Services, for example, the result is a comprehensive dataset that mirrors the U.S. population. Additional insights, such as a researcher’s or study site’s historical trial experience, competitive trial workload, regulatory actions, and patient volumes enable study teams to not only create better trials but implement them more efficiently.

For optimal results, RWD should not only be part of a last-ditch effort to rescue clinical trials in jeopardy, reserved for studies in “rescue mode.” It should be part of the initial trial planning process. In order for researchers to derive the most benefit from RWD, however, cooperation is crucial. 

“Teamwork and in‐depth understanding of data sources are keys to successful RWD mining,” according to an article in the medical journal Clinical and Translational Science. This requires collaboration among study sponsors, contract research organizations (CROs), study sites, physicians, pharmacists, pharmacologists, pharmacovigilance specialists, statisticians, and other stakeholders. Only when silos are dismantled can the industry realize the true potential of RWD. Researchers who integrate RWD are able to paint a more complete clinical picture and, in turn, improve patient outcomes. 

Citeline is the industry leader in clinical trial intelligence using RWD from multiple sources to help sponsors optimize trial design, more efficiently select trial sites and investigators, and accelerate patient enrollment. Learn more about Citeline’s RWD capabilities.

*Decoding Pharma’s Alphabet Soup:

• AI – adverse incident (an event not caused by underlying disease resulting in an undesirable clinical outcome)
• CMS – Centers for Medicare & Medicaid Services
• DCT – Decentralized clinical trial
• FDA – U.S. Food & Drug Administration
• OTC – over the counter (drugs sold without a doctor’s prescription)
• PI – principal investigator (the researcher, usually a doctor or other medical professional, who leads the clinical research team)
• PMS – post-market surveillance (monitoring the safety of a pharmaceutical drug or medical device after it has been released on the market)
• R&D – research & development
• RWD – real world data
• RWE – real world evidence
• STD – severely toxic dose (the quantity of a drug producing an extremely harmful or untoward effect)
• TSA – Therapeutic Substances Act (a UK law governing manufacturing, sale, supply, dispensing and administration of therapeutic substances)