Life Sciences

Mintz/ML Strategies’ 4th Annual Pharmacy & Pharmaceutical Industry Summit has been scheduled for Thursday, May 2, 2019 – mark your calendars! People from across the industry will gather for one day to share insights about issues that the players in this complex marketplace are tackling.

The Federal Courts gave the America Hospital Association and 340B covered entities a late Christmas present and in doing so may have dealt a blow to the Trump Administration’s initiatives aimed at controlling or reducing drug prices.


On December 27, 2018, Judge Rudolph Contreras granted the American Hospital Association’s Motion for a Permanent Injunction over CMS’ Medicare Part B reimbursement cut for 340B hospitals. As I have previously written, the vehicle for that reimbursement cut was the 2018 Outpatient Prospective Payment System (OPPS) rule, and CMS’ existing authority to adjust OPPS drug reimbursement.

As 2019 quickly approaches, we would like to take a few moments to reflect on the past year of Food and Drug Administration activities and certain big ticket items that made news in 2018. As the Magic 8-Ball would say: “signs point to yes” that everything on the list below will continue to be major areas of focus for both FDA and the U.S. Congress next year and into the foreseeable future.

Along with most of us, last January DOJ set its own goals for 2018: new policies related to False Claims Act (“FCA”) enforcement. One such “resolution” for 2018 was the DOJ Civil Fraud section’s instruction to its attorneys and all AUSAs handling FCA cases to routinely consider whether declined qui tam actions should be dismissed under the Department’s authority in Section 3730(c)(2)(A) of the FCA, which it had rarely used from 1986 through 2017. Known as the “Granston Memo” (which we discuss here) and now codified in the Justice Manual, the central theme of the instruction is that seeking dismissal of qui tam actions may be in the government’s interest to “preserve limited resources and avoid adverse precedent.” We are now seeing the first evidence of DOJ following through on that resolution.

On December 10-11, 2018, FDA hosted a public workshop, Medical Device Servicing and Remanufacturing Activities, as part of its effort to develop a draft guidance that will distinguish servicing activities from remanufacturing. FDA expressed intent to develop a draft guidance on this topic as part of its May 15, 2018 report to Congress on the quality, safety, and effectiveness of medical device servicing. This post provides some observations about areas of agreement among stakeholders and FDA’s perspective on servicing versus remanufacturing.

The U.S. Food and Drug Administration (FDA) is exploring the development of a new regulatory approach for software as a medical device (SaMD) that the agency believes will promote innovation while still assuring device safety and effectiveness. SaMD is software used for a medical purpose that is not part of a hardware medical device. The new approach is known familiarly as Pre-Cert and relies on a company being certified by FDA as having a culture of quality and organizational excellence.

In his typical forceful style on December 11, 2018, U.S. Food and Drug Administration (FDA) Commissioner Scott Gottlieb announced several big pieces of policy news affecting the nascent biosimilar market. The Commissioner’s statement broadly relates to FDA’s “actions to advance the biosimilars policy framework” and is a well-articulated hodgepodge of FDA regulatory, drug pricing, industry competition, and patent thicket complaints, which Dr. Gottlieb is becoming famous for in his written and oral presentations. 

As of December 2018, 37 states (including D.C.) have adopted Medicaid expansion. Of the remaining 14 states, some are considering expanding Medicaid. States with recent activity relating to Medicaid expansion include: Florida, Idaho, Maine, Missouri, Nebraska, New Hampshire, North Carolina, and Utah. States continue to explore different opportunities as it relates to Medicaid expansion. 

Major legislation impacting FDA often accompanies user fee reauthorizations every 5 years. However, Congress has acted to address public health issues between user fee cycles. FDA regulates 20¢ of every U.S. consumer dollar spent on products ranging from heart valves to insulin to breakfast cereal, so there’s always something Congress can do in the realm of FDA’s statutory authorities. Many FDA-related bills are often bipartisan, too, which suggests action regardless of which party is in power. Here are a few key medical product issues we’ll be tracking in 2019.

In this sixth post in our series on artificial intelligence in health care, Julie Korostoff highlights the importance of securing adequate data rights to commercialize an AI technology. The post addresses the contractual commitments that a developer of a healthcare AI tool should secure in order to have the data rights necessary for development and commercialization.

Emerging medical imaging technologies being developed for the terahertz spectrum may face regulatory hurdles from an unexpected Federal agency: the Federal Communications Commission (FCC). While the Food and Drug Administration (FDA) has jurisdiction over medical devices with respect to safety and effectiveness – including premarket approval, inspection of manufacturing facilities, and monitoring of post-market adverse events – the FCC has jurisdiction over certain technical and spectrum use requirements for medical devices that operate on radiofrequency (RF) spectrum. Medical device developers need to be aware of the current state of play at the FCC in these areas.

As our use of AI technology becomes more frequent, interconnected, and integral to daily life, the liability exposure to AI product designers and manufacturers continues to escalate. There are more potential liability risks, including product liability risks, in our current environment than ever. With AI technology embedded in interconnected software and hardware products, gone are the days where we can neatly separate data security and privacy from product liability exposure.

There has been a marked increase in the amount of money being invested by Chinese investors into U.S. early stage biotechnology companies since 2017, spurred on by direct encouragement from Beijing through its Made in China 2025 industrial policy, which specifically targets biotech as a strategic industry eligible for greater government backing.

Software developers are racing to develop health care products that leverage artificial intelligence (AI), including machine learning and deep learning. Examples include software that analyzes radiology images and pathology slides to help physicians diagnose disease, electronic health records software that automates routine tasks, and software that analyzes genetic information to support targeted treatment. The one thing that all of these products have in common is a need to interact, in some way, with real world medical data. However, this real world data can be protected by the Health Insurance Portability and Accountability Act of 1996 (HIPAA) as well as a patchwork of federal and state laws and regulations. Below we discuss the contexts in which developers may encounter these laws, as well as strategies to navigate related legal issues.

For much of the past 18 months, the Trump Administration, and in particular CMS, have talked a good game regarding reducing pharmaceutical prices. On October 16, 2018, a key component of the Administration’s strategy was revealed in the form of CMS’ Proposed Rule requiring manufactures to include the “list price” for prescription drugs reimbursable by Medicaid or Medicare in television advertisements. While I do think that there will be new initiatives to address drug pricing, I believe most will come through the state and not the federal level. This post addresses six potential initiatives from a recently released report of the National Governors' Association.

On October 18, 2018, FDA released a new draft guidance, Content of Premarket Submissions for Management of Cybersecurity in Medical Devices, which describes the Agency’s current thinking and recommendations on designing medical device software with adequate cybersecurity controls.  Once finalized, the draft guidance will supersede a final guidance of the same name issued in 2014.

Artificial intelligence—AI—is the future of everything. But when patient health is on the line, can we trust algorithms to make decisions instead of patients or their health care providers? This post, the second in our blog series about AI in health care, explores FDA’s proposed regulatory model that is supposed to be better suited for AI (and similar technologies) while still protecting patients.

In the first new guidance document from FDA in several years specific to the subject of direct-to-consumer (DTC) promotion of prescription drugs and biological products, the Agency is recommending that companies take additional steps to ensure that quantitative efficacy or risk information does not convey inaccurate information and does not have the potential to confuse consumers. The draft guidance defines quantitative efficacy and risk information as “information that numerically addresses the likelihood or magnitude of a drug’s effectiveness or risks.” FDA’s advice on how to most clearly share this type of information should be considered by companies when developing any form of DTC promotional media, whether they are digital, broadcast, in traditional print format, or otherwise.

The Trump Administration is moving full speed ahead with its proposals under the Blueprint to Lower Drug Prices (the “Blueprint”). Earlier this week, the Centers for Medicare & Medicaid Services (“CMS”) released a proposed rule that would require pharmaceutical manufacturers to disclose the list price of their pharmaceutical products in direct-to-consumer (“DTC”) television ads (the “Proposed Rule”).

The Journal of the American Medical Association in its September 18, 2018, issue included four articles on deep learning and Artificial Intelligence (AI). In one of several viewpoint pieces, On the Prospects for a (Deep) Learning Health Care System, the author’s conclusions aptly describes why health care providers, entrepreneurs, investors and even regulators are so enthusiastic about the use of AI in health care.