Is the decision to submit a 510(k) application versus a Premarket Application (PMA) at the sole discretion of a medical device manufacturer? The answer is not always clear to product liability lawyers, judges, and juries. FDA recently published revised guidance on its “Refuse to Accept Policy for 510(k)s” that reinforces and clarifies that the regulatory path may be analyzed multiple times by FDA before it clears a 510(k) device. This clarification underscores the reality that the type of application submitted is largely dictated by the agency, not the applicant. This post discusses some key takeaways from this new guidance before briefly discussing how this guidance may be implicated in medical device litigation.
We have written before about the Supreme Court’s impossibility preemption decision, Merck Sharpe & Dohme Corp. v. Albrecht, 139 S. Ct. 1668 (2019) (Albrecht) (here, here, here, and here), highlighting some open questions and uncertainties that might come into play on remand. Albrecht held that impossibility preemption is a question of law for the court, not for the jury, “elaborated” on the “clear evidence” standard arising from Wyeth v. Levine, 555 U.S. 555 (2009) (Wyeth), and remanded to the Third Circuit for determination of the preemption issue. That court in turn remanded to the District of New Jersey and further directed the district court “to determine the effect of the FDA’s Complete Response Letter and other communications with Merck on the issue of whether the agency actions are sufficient” to find preemption.
We predicted that the decision on remand would be “interesting” and opined that the case for preemption was “strong.” We now have that decision, In re Fosamax (Alendronate Sodium) Prod. Liab. Litig., 2022 WL 855853 (D. N.J. Mar. 23, 2022) (Fosamax), and we were right on both counts.
FDA recently issued final guidance regarding the initiation of voluntary product recalls and its related suggestions on how to be “recall ready.” The guidance – covering voluntary recalls of food, drugs, devices, biological products, cosmetics, and tobacco – emphasizes the importance of a company’s recall readiness at all stages of a product’s distribution chain and provides companies with suggested measures to prepare for and implement voluntary recalls. It also advises companies on best practices for working with FDA to initiate a timely voluntary recall.
Telemedicine and telehealth have significantly reshaped how consumers access health care services. Even before the COVID-19 pandemic, online portals were jockeying to replace visits to primary care providers and urgent care clinics for minor illnesses or simple-to-prescribe medications. The last two years shifted that race into high gear, particularly with new products and platforms being introduced that range from virtual clinic platforms that allow patients—and their programmable implanted medical devices—to connect with their providers from the comfort of their own homes, to passive smart devices that remotely monitor patient vital signs, analyze that data using proprietary algorithms, and evaluate whether a patient is having a medical emergency or needs to schedule an appointment with their provider. These technologies are now so ubiquitous that they are being showcased at the 2022 Consumer Electronics Show.
To be sure, regulatory changes in response to the COVID-19 pandemic made telemedicine more permissible—and reimbursable—than in the past. But that alone is not driving medical device companies forward. Instead, medical device manufacturers are rapidly developing smart or algorithm-driven medical devices that take advantage of the ever-increasing power of those technologies and leveraging telemedicine to make the remote treatment and management of medical conditions less complicated. A recent article in Nature’s npj Digital Medicine confirmed the growth in this area, counting 64 separate smart- or algorithm-driven medical devices currently on the market as of 2020. See Stan Benjamens, et al., The State of Artificial Intelligence-Based FDA-Approved Medical Devices and Algorithms: An Online Database, 3 npj Digital Medicine Article No. 118 (2020). Each of these new devices endeavor to enable physicians to practice more effectively and efficiently than they could before. The future for smart or algorithm-driven medical devices looks promising.
The U.S. Food and Drug Administration (FDA) issued its third draft guidance under the Real-World Evidence (RWE) Program on November 29, 2021. In Real-World Data: Assessing Registries to Support Regulatory Decision-Making for Drugs and Biological Products, the FDA discusses considerations for sponsors and other stakeholders when designing or using an existing registry as RWD to support a regulatory decision about the safety and effectiveness of a medicine or biologic.
The goal of the RWE program, in part, is to satisfy Congress’s mandate under section 505F of the Federal Food Drug and Cosmetic Act (FD&C Act) for the FDA to provide more guidance about the use of RWE in regulatory decision-making. We discussed the FDA’s first and second guidances, released in August and October 2021, here and here.
The U.S. Food and Drug Administration (FDA) issued a draft guidance titled Data Standards for Drug and Biological Product Submissions Containing Real-World Data on October 21, 2021. The guidance provides the Agency’s thoughts on how sponsors can comply with the Federal Food, Drug and Cosmetic Act (FDCA) when submitting “certain” applications that contain study data derived from real-world data (RWD) sources. The FDA acknowledges that its current study data standards do not necessarily reflect a process derived from RWD sources. However, sponsors will need to convert RWD into established study data standards when submitting this information as part of a regulatory application (a process called “mapping”).
For context, study data standards are documented guidelines to help with the exchange of clinical and nonclinical study data between computer systems. They are used to provide a consistent framework for organizing study data (such as templates for datasets, standard names for variables, how to do calculations with common variables, and so on).