The Food and Drug Administration (FDA) recently issued both draft and final guidance regarding food allergen labeling requirements. The draft guidance document, Questions and Answers Regarding Food Allergens, Including the Food Allergen Labeling Requirements of the Federal Food, Drug, and Cosmetic Act (Edition 5), updates the previous (fourth) edition with new and revised guidance concerning food allergen labeling. FDA also issued a final guidance document with the same title in order to preserve questions and answers that were unchanged from the previous (fourth) edition, which was published in 2004 and last updated in 2006.
On January 24, 2023, FDA published a notice in the Federal Register entitled, “Clarification of Orphan-Drug Exclusivity Following Catalyst Pharms., Inc. v. Becerra.” In brief, the Catalyst decision by the 11th Circuit Court of Appeals concerned FDA’s application of the Orphan Drug Act (21 USC 360cc(a)), and in particular the extent of the 7-year orphan drug market exclusivity (ODE) provided with an orphan drug’s approval. The ODE, per the Orphan Drug Act prevents FDA from approving another applicant’s same drug for “the same disease or condition.”…
The regulatory environment at the US Food and Drug Administration (“FDA”) has a tremendous impact on how companies operate, and consequently data on that environment can be quite useful in business planning. In keeping with the theme of these posts of unpacking averages, it’s important to drill down sufficiently to get a sense of the regulatory environment in which a particular company operates rather than rely on more global averages for the entire medical device industry. On the other hand, getting too specific in the data and focusing on one particular product category can prevent a company from seeing the forest for the trees.
Recently, I was asked by companies interested in the field of digital medical devices used in the care of people with diabetes to help them assess trends in the regulatory environment. To do that, I decided to create an index that would capture the regulatory environment for medium risk digital diabetes devices, trying to avoid getting too specific but also avoiding global data on all medical devices. In this sense, the index is like any other index, such as the Standard & Poor 500, which is used to assess the economic performance of the largest companies in terms of capitalization. My plan was to first define an index of product codes for these medium risk digital diabetes products, then use that index to assess the regulatory environment in both premarket and postmarket regulatory requirements.
Continue Reading Unpacking Averages: Creating an Industry-Specific Index to Track the FDA Regulatory Environment
It is certainly easy, when writing code to accomplish some data science task, to start taking the data on face value. In my mind, the data can simply become what they claim to be. But it’s good to step back and remember the real world in which these data are collected, and how skeptical we need to be regarding their meaning. I thought this month might be an opportunity to show how two different FDA databases produce quite different results when they should be the same.
Continue Reading Unpacking Averages: The Difference Between Data and the Truth: Comparing FDA’s UDI Database with FDA’s 510(k) Database
The motivation for this month’s post was my frustration with the techniques for searching the FDA’s 510(k) database. Here I’m not talking about just using the search feature that FDA provides online. Instead, I have downloaded all of the data from that database and created my own search engine, but there are still inherent limitations in what the data contain and how they are structured. For one, if you want to submit a premarket notification for an over-the-counter product, it really isn’t easy to find predicates that are specifically cleared for over-the-counter without a lot of manual work.
To see if I could find an easier way, I decided to use the database FDA maintains for unique device identifiers, called the Global Unique Device Identification Database (GUDID). You can search that database using the so-called AccessGUDID through an FDA link that takes you to the NIH where the database is stored. That site only allows for pretty simple search, so for what I needed to do, I downloaded the entire database so I could work directly on the data myself.
While the UDI database is enormous at this juncture (over 3 million products), what I found left me with questions about just how comprehensive and complete the data are. At the same time, it seems like a good way to supplement the information that can be gleaned from the 510(k) database.
Continue Reading Unpacking Averages: Finding Medical Device Predicates Without Using FDA’s 510(k) Database
Over the spring and summer, I did a series of posts on extracting quality information from FDA enforcement initiatives like warning letters, recalls, and inspections. But obviously FDA enforcement actions are not the only potential sources of quality data that FDA maintains. FDA has what is now a massive data set on Medical Device Reports (or “MDRs”) that can be mined for quality data. Medical device companies can, in effect, learn from the experiences of their competitors about what types of things can go wrong with medical devices.
The problem, of course, is that the interesting data in MDRs is in what a data scientist would call unstructured data, in this case English language text describing a product problem, where the information or insights cannot be easily extracted given the sheer volume of the reports. In calendar year 2021, for example, FDA received almost 2 million MDRs. It just isn’t feasible for a human to read all of them.
That’s where a form of machine learning, natural language processing, or more specifically topic modeling, comes in. I used topic modeling last November for a post about major trends over the course of a decade in MDRs. Now I want to show how the same topic modeling can be used to find more specific experiences with specific types of medical devices to inform quality improvement.
Continue Reading Unpacking Averages: Using Natural Language Processing to Extract Quality Information from MDRs
A private equity client asked us recently to assess a rumor that FDA was on the warpath in enforcing the 510(k) requirement on medical devices from a particular region. Such a government initiative would significantly deter investments in the companies doing the importing. Turns out, the agency was not. The FDA’s recent activities in the region were well within their historical norms.
But the project got us thinking, what does the agency’s enormous database on import actions tell us about the agency’s enforcement priorities more generally? There are literally thousands of ways to slice and dice the import data set for insights, but we picked just one as an example. We wanted to assess, globally, over the last 20 years, in which therapeutic areas has FDA been enforcing the 510(k) requirement most often?
Continue Reading Unpacking Averages: Assessing FDA’s Focus on Enforcing 510(K) Requirements on Imports
You might be thinking, that’s an odd title: obviously FDA’s breakthrough device designation is helpful. However, after looking at the data, my conclusion is that I would avoid the breakthrough device designation for any product that qualifies for the 510(k) process. The process is likely not helpful for such devices.
[Update – August 3, 2022: See the bottom of this post.]…
Recalls have always been a bit of a double-edged sword. Obviously, companies hate recalls because a recall means their products are defective in some manner, potentially putting users at risk and damaging the brand. They are also expensive to execute. But a lack of recalls can also be a problem, if the underlying quality issues still exist but the companies are simply not conducting recalls. Recalls are necessary and appropriate in the face of quality problems.
Thus, in terms of metrics, medical device companies should not adopt as a goal reducing recalls, as that will lead to behavior that could put users at risk by leaving bad products on the market. Instead, the goal should be to reduce the underlying quality problems that might trigger the need for recall.
What are those underlying quality problems? To help medical device manufacturers focus on the types of quality problems that might force them to conduct a recall, we have used the FDA recall database to identify the most common root causes sorted by the clinical area for the medical device.
Continue Reading Unpacking Averages: Common Root Causes Driving Medical Device Recalls
Most companies want to avoid FDA warning letters. To help medical device companies identify violations that might lead to a warning letter, this post will dive deeply into which specific types of violations are often found in warning letters that FDA issues.
As you probably know, FDA has a formal process for evaluating inspection records and other materials to determine whether issuing a warning letter is appropriate. Those procedures can be found in chapter 4 of FDA’s Regulatory Procedures Manual. Section 4-1-10 of that chapter requires that warning letters include specific legal citations, in addition to plain English explanations of violations. The citations are supposed to make reference to both the statute and any applicable regulations.
As a consequence, to understand the content of the warning letters, we need to search for both statutory references as well as references to regulations. Because statutes are deliberately drafted to be broader in their language, references to the regulations tend to be more meaningful.
Continue Reading Unpacking Averages: Violations Found in Medical Device Warning Letters