When it comes to regulatory science and the broader issues facing the Food and Drug Administration, there will always be significant gradations of nuance and ambiguity. “Predictability” is a trail that regulators and regulated must blaze together — sometimes heroically and at other times with greater caution.
In a world increasingly driven by outcomes reporting and “Big Data,” more patient-level information from individual consumers is not always synonymous with validated data. Despite the frustrating increase in the signals-to-noise ratio, artificial intelligence is becoming an ever-more significant source of potentially valuable, electronically generated health care information.
Once considered junk science, “real world evidence” (clinical outcomes data not collected in conventional randomized controlled trials) is the new star on the precision medicine horizon and will help define the scope and strategies of 21st-century medicines development and regulation. Artificial intelligence will facilitate what this ecosystem lacks today — a coordinated and efficient system for developing actionable evidence on safety and effectiveness. Such information will also prove crucial in pricing and reimbursement decisions and post-approval labeling updates.
There is so much data to utilize: patient medical history records, treatment data, and lately, information coming from wearable health trackers and sensors. This huge amount of data must be analyzed not only to provide patients who want to be proactive with better suggestions about lifestyle, but also to serve providers with instructive pieces of information about how to design health care based on the needs and habits of patients, as well as provide regulators not just with more data, but better data in context.
Artificial intelligence will have a huge impact from genetics to genomics, helping to identify patterns in huge data sets of information and medical records, and looking for mutations and linkages to disease. But for any of this to happen, we must view artificial intelligence through the lens of 21st-century interoperability: the idea that different systems used by different groups of people can be used for a common purpose because those systems share standards and approaches.
The good news is that the FDA is enhancing its ability to handle real-world evidence by training reviewers in data science via a curriculum on machine learning and artificial intelligence. According to FDA Commissioner Scott Gottlieb, “We’re working to develop new guidance documents to assist sponsors interested in developing and using real world evidence.”
The agency’s “Framework for FDA’s Real-World Evidence Program” evaluates the use of RWE to support additional indications for already approved drugs as well as to satisfy drug post-marketing study requirements. Per Gottlieb, “The framework is aimed at leveraging information gathered from patients and the medical community to inform and shape the FDA’s decisions across our drug and biologic development efforts.” The goal is to develop a path for ensuring that RWE solutions can play a more integral role in drug development and the regulatory life cycle at the FDA.
Recently, the FDA announced four additional activities to help the agency and the broader drug development ecosystem advance these opportunities for the benefit of patients.
The FDA plans to:
— Support the seamless integration of digital technologies in clinical trials by developing a framework on how digital systems can be used to enhance the efficient oversight of clinical trials. These technologies present important opportunities to streamline drug trials and improve data site integrity by remotely monitoring data trends, accrual and integrity over the course of a trial.
— Use digital technologies to bring clinical trials to the patient, rather than always requiring the patient to travel to the investigator. More accessible clinical trials can facilitate participation by more diverse patient populations within diverse community settings where patient care is delivered, and in the process can generate information that’s more representative of the real world and may help providers and patients make more-informed treatment decisions.
— Explore how reviewers can have more insight into how labeling changes inform provider prescribing decisions and patient outcomes. The FDA’s Information Exchange and Data Transformation is using RWD to examine the impact of a recent FDA labeling change for two approved products from weight-based dosing to flat-dosing of immune checkpoint inhibitors. This project is focused on how community practices are adopting the flat dose after the labeling change, and factors that may affect adoption.
— Work with the medical product centers to develop an FDA curriculum on machine learning and artificial intelligence in partnership with external academic partners. The aim of this program is to improve the ability of FDA reviewers and managers to evaluate products that incorporate advanced algorithms and facilitate the FDA’s capacity to develop novel regulatory science tools harnessing these approaches.
According to Dr. Bertalan Meskó of the Medical Futurist Institute, we are experiencing the fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human. Health care will lead this revolution, and artificial intelligence will be one of the major catalysts for change with actionable consequences.
Much depends not just on FDA programs and infrastructure, but also on capabilities and trust.
Peter J. Pitts, a former FDA associate commissioner, is president of the Center for Medicine in the Public Interest and a visiting professor at the University of Paris Descartes Medical School.
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