Pink Sheet - US FDA Looking To Real-World Evidence To Fill in Gaps On COVID-19 Vaccines
Principal Deputy Commissioner Amy Abernethy suggests FDA may clear a COVID-19 vaccine on smaller than usual clinical datasets and rely on real-world data to fill in the gaps. Abernethy said a new public-private partnership is starting to explore how it can use real-world data and evidence for COVID-19 vaccine evaluation.
The US Food and Drug Administration is working with public and private partners to figure out how it could use real-world data to help evaluate COVID-19 vaccines, principal deputy commissioner Amy Abernethy said Tuesday at the Biotechnology Innovation Organization's digital convention.
The COVID-19 Evidence Accelerator, a project launched by the Reagan-Udall Foundation, Friends of Cancer Research and FDA to bring together a variety of government, industry and academic health data experts to answer key COVID-19 questions, is “gearing up” to think about how the project can be used for vaccines, Abernethy said.
Her remarks hinted at the idea that FDA might approve or grant an emergency use authorization (EUA) to a vaccine without the amount of clinical trial data normally expected of a vaccine.
“So imagine a world where we develop a vaccine or vaccines that we need to start to use them before we fully test them. How are we going to right size that use? How are we going to understand what that means at scale and make sure we keep each other safe? So the whole idea is that we create mechanisms now so that we can ready for that future,” Abernathy said.
In response to follow-up questions from the Pink Sheet, FDA said that Abernethy was referring to a “hypothetical situation for a vaccine that is in the development stage and does not have an approved BLA” and that she used the word, “we” to refer to the broader public health community.
FDA said that real-world data could be used to inform both vaccine development and use, including to supplement prospectively designed clinical studies of vaccines to help understand vaccine safety and effectiveness in different groups of patients.
Abernethy: COVID-19 Has Necessitated FDA’s Regulatory Flexibility
Earlier in the panel Abernethy endorsed the idea of regulators being more flexible and learning how to move faster during COVID-19, then correcting any wrong decisions on the back end, espousing a similar philosophy to that laid out by Commissioner Stephen Hahn in a speech last week where he defended the agency’s choice to make fast decisions on less robust data sets during COVID-19, paired with constantly reevaluating those decisions. (Also see "Hahn Defends Using Less ‘Robust’ Data During COVID, But Critics Contend It Has Gone Too Far" - Pink Sheet, 3 Jun, 2020.)
“What’s interesting to me in the context of COVID-19 is that we have all had to figure it out really fast. Companies are on development cycles where they are moving quickly … meanwhile on the FDA side we’ve realized we need to have regulatory flexibility in many different directions and then learn from that regulatory flexibility and be clear about that,” she said.
Abernethy cited EUAs that were issued for diagnostic tests and medical masks for COVID-19 as areas that taught FDA what is OK to do, and also where they “need to pull back.”
“So as FDA we’ve been right-sizing right along with companies. And I think COVID-19 gives us this really important opportunity to figure out how we innovate better especially in settings that have been historically really risk adverse,” she said.
Abernethy has previously said that COVID-19 is forcing the agency to step outside its comfort zone when it comes to working with real-world data and evidence. (Also see "Real-World Evidence On COVID-19: US FDA Approaching With 'Sense Of Urgency'" - Pink Sheet, 21 Apr, 2020.)
Abernethy said the Evidence Accelerator has so far been focusing on therapeutics, noting it came to be because of the recognition that “there is a finite number of clinical trial resources we can bring to this problem.”
But answering questions in the context of COVID-19 with real-world data “is very hard,” she acknowledged because of the newness of the virus and the unique challenges it has placed on overwhelmed health care delivery systems.
“We don’t’ have common definitions, we don’t have natural history of the disease, we don’t even really understand what it means to be an ICU patients because suddenly the ICU exists in the observational unit outside the operating room. All these different pieces are mixed up in the context of COVID,” Abernethy said.
Comparing Vaccines In The Real World
Reagan-Udall Foundation CEO Susan Winckler said she envisions a multiple uses of the Evidence Accelerator for COVID-19 vaccines.
If there are multiple vaccines, Winckler said the Evidence Accelerator could be used to get information about the relative performance of the products. If there is just a singular vaccine the Accelerator might look at how it is performing in the “real world” so that data could be used to improve that product or future vaccines.
“It’s all about a feedback loop. How do you take what you learn about the real life performance of a vaccine and feed that back into the development and improvement of the intervention,” Winckler said in an interview.
The need for real-world evidence is elevated in a pandemic response, she said.
“It’s always important, but it’s more important when we know we had to work in an environment that compelled moving rigorously but quickly to try and get to a safe and effective enough intervention that we can continue to improve.”
Data elements that will be helpful for this work might include what specific about the vaccine, including the lot number and where it was delivered, she said. Data scientists may have to piece together some of this information. For example, a patient's main electronic health record may be at their physicians office, but a person may get the vaccine at a public health department.
Winckler said the Evidence Accelerator’s work on therapeutics will likely be able to be adapted to the vaccine space. For example, they are working to determine what data sources are appropriate for use and understand the complexities of those data sources, and the analytical plans that work.