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Regulators have long relied on randomized clinical trials (RCTs) to support decision making for label expansions, clinical guideline development, and treatments efficacy.

But in an era where blockbuster drugs have been replaced by precision medicine and targeted therapies that meet the unique need of smaller and more specific patient populations, RCTs don’t always make sense. RCTs can be very expensive, and recent studies suggest there may be other ways to generate the data that would be sufficiently robust for many purposes.

Our ability to capture and analyze real world data from multiple sources is changing the way we study and understand the impact of new therapies, which could directly affect the way drugs are approved in the future, especially with regard to label expansions. We already use real world evidence (RWE) as historical comparators when approving new treatments for rare diseases, and to study the long term safety and efficacy of drugs on the market. But there are many more opportunities to harness these data earlier and more frequently in clinical development and commercialization.

One area showing great promise is pragmatic randomized clinical trials (pRCT), which combine the relevance of real world data with some of the rigor of a clinical study by randomizing patient treatment assignment. Pragmatic RCTs study treatments of interest compared with their therapeutic alternatives, and do not compare treatments to placebo. Treatments are not blinded. The goal of pRCt is to measure how a product or treatment performs in routine clinical practice, with outcomes that are relevant to everyday medical care. These pRCT are designed to capture variations in patient responses that occur in diverse populations in order to help understand which patients are most likely to benefit from treatment, and in what situations.

The information obtained can help inform treatment choices and provide support to policy-makers and payers in a variety of settings and health systems. The question now is how open are regulators to using RWE methods in decision-making, for what purposes and in what circumstances?

Regulators are ready to talk

At this year’s annual DIA event in Chicago, my colleagues and I will address these questions on a DIAmond panel entitled, The Evolution of Evidence Generation: Real World Evidence and the Next Generation of Decision Making. In this session, panelists will discuss the regulators’ readiness to use RWE to answer safety and efficacy questions and to consider its use for regulatory purposes.

This isn’t just a group of biopharma industry executives discussing the potential benefits of RWE. The panel will also include Alison Cave, principal scientific administrator for the European Medicines Agency (EMA), and Robert Temple, deputy center director for clinical science at the US Food and Drug Administration (FDA). These regulators plan to speak candidly about their willingness to consider using real world data sources for evidence generation, and what pharma companies will need to do to convince them. They will also be joined by Stephanie Devaney from NIH, who is leading the “All of Us” Research Program. Iris Loew-Friedrich from UCB will moderate the panel, and I will also be joined by Brian Bradbury from Amgen.

Participants may be surprised by what they hear in this session.

Many pharma executives assume RWE studies would never be considered adequate by regulators, but we are finding quite the opposite is true. While every case is different, regulators are open to these ideas, and are encouraging the industry to give them the chance to say yes.

This trend was spurred in part by the 21st Century Cures Act, which specifically called on the FDA to evaluate the use of RWE to support the approval of new indications for previously approved drugs and for post-approval study requirements. And last year, the National Institutes of Health earmarked $55 million to launch the “All of Us” Study as part of the Precision Medicine Initiative, which is a real world research study to engage a million U.S. participants in an effort to improve our ability to prevent and treat disease. The RWE collected in this study will be used to learn about how individual differences in lifestyle, genetics and environmental factors can affect a person’s risk of developing disease or benefitting from certain treatments.

These are early indicators that the use of RWE in a regulatory setting will continue to expand, and that regulators and payers may even start coming together to agree on research programs that could provide the evidence needed by both groups in a single study. Clearly this won’t be an easy transformation, and developers interested in using RWE to support their research will need to consult with regulators to understand their concerns and to win their approval for these studies. But if they take that time up-front, the payoff could be substantial since pRCTs cost a fraction of traditional RCTs, and can be completed in much less time while delivering comparable results.

In one example, PCORnet: The National Patient-Centered Clinical Research Network, funded a three-year pRCT with 20,000 patients called ADAPTABLE, to study whether low dose daily aspirin is more effective than higher aspirin doses in preventing heart attacks and strokes in high risk patients. The budget for a classical cardiovascular outcomes trial of similar size would be hundreds of millions of dollars, whereas this study is expected to cost less than $20 million dollars.

RWE and pRCTs are the future of clinical research. Stakeholders who are interested in understanding how this kind data can be used in their own research or how it will effect regulatory decision-making in the future, should be sure not to miss this event.

Topics in this blog post: Real-World Insights - RWI, Evidence, Registries, Biopharma