The Food and Drug Administration might one day use data gathered from off-label uses of drugs to determine whether or not to approve the drugs for those uses, an agency division director said Tuesday.
The FDA approves drugs for specific conditions in certain groups of people and then spells out those prescribed uses on a drug’s label. But physicians can prescribe the drugs to anyone for anything, so-called off label.
Although clinicians happen to gather data about patients’ experiences using drugs off label in the normal course of treatment, that data is not collected in the controlled research setting that the FDA typically needs to evaluate a drug.
“I don’t think we are there yet, but that doesn’t mean we will never accept [it],” said Rajeshwari Sridhara, director of one of the biostatistics divisions in the FDA’s Center for Drug Evaluation and Research.
Sridhara’s remarks came during a daylong conference hosted by Friends of Cancer Research on “real-world data,” which is information collected outside of the controlled environment of a clinical trial. It includes everything from hospital X-rays to fitness app data to physicians’ notes in the electronic medical record.
Sridhara said the FDA might consider the rarity and severity of a disease when considering whether it might want to use data from off-label uses, as well as on the quality of the data. There also might need to be a way for the FDA to audit the data, as it can in the case of data from clinical trials.
Later in the afternoon, CDER Director Janet Woodcock said that the agency is already using real-world evidence in the regulatory process, and looks forward to using more, but also pointed out that sometimes, the important data aren’t actually recorded.
“One of the flaws in health care is, at least in the record, there’s very little reflected about how the patient feels, functions, and even survives, which are the main things we’re interested in at FDA, right?” Woodcock said.
Indeed, a constant topic of consternation among morning panelists was the difficulty of determining, in a data set, if people were alive or dead.