- Food and Drug Administration Commissioner Scott Gottlieb on Monday laid out the agency's four priorities for use of real-world data and evidence this year, part of a broader push by the agency to modernize its regulatory decision making.
- One of the agency's priorities will be to focus on the use of digital technologies in streamlining clinical trials, with the hope of potentially cutting costs, strengthening data integrity and improving regulatory oversight. This will begin with a stakeholder meeting to develop a framework on the use of digital systems.
- Another priority will center on decentralizing clinical studies — put simply, bringing the trial to the patient rather than the patient to the trial. This could increase the diversity of patients who can participate in trials, as well as generate data in settings where a drug would eventually be used following approval. The FDA has created a formal working group and is developing a guidance document, Gottlieb said.
How a drug works in the real world doesn't always reflect its efficacy in tightly controlled clinical trials where compliance is encouraged and patients are carefully selected and monitored. As such, real-world data (RWD) and evidence (RWE) are becoming increasingly important for understanding the day-to-day use of a drug once it reaches market.
Drug developers are already tapping into the potential of RWD and RWE. Bristol-Myers Squibb and Pfizer, for example, built up a body of real-world evidence to help make a case for their blood thinner Eliquis (apixaban).
Traditionally, post-marketing studies take years to complete and can cost millions of dollars. Yet RWD and RWE have already shown some value in reducing the need for post-market monitoring.
"Our use of RWD and RWE, derived from our Sentinel system, eliminated the need for post-marketing studies on nine potential safety issues involving five products, making our post-market evaluation of safety timelier and more effective," said Gottlieb in prepared remarks at the Bipartisan Policy Center conference.
The FDA's four priorities also include: using RWD to explore how labeling changes affect prescribing decisions, and using software-based machine learning algorithms to help develop regulatory science tools like surrogate endpoints or digital biomarkers.
This latter effort will be supported by an FDA curriculum on machine learning and artificial intelligence that's being developed in partnership with external academic partners.