Food and Drug Administration (FDA)

Introduction

Let’s say FDA proposed a guidance document that would change the definition of “low cholesterol” for health claims.  Now let’s say that when FDA finalized the guidance, instead of addressing that topic, FDA banned Beluga caviar.  If you are interested in Beluga caviar, would you think you had adequate opportunity to comment?  Would you care if FDA argued that Beluga caviar was high in cholesterol so the two documents were related?
Continue Reading Unpacking Averages: Using NLP to Assess FDA’s Compliance with Notice and Comment in Guidance Development

On January 24, 2023, FDA published a notice in the Federal Register entitled, “Clarification of Orphan-Drug Exclusivity Following Catalyst Pharms., Inc. v. Becerra.”[1]  In brief, the Catalyst decision by the 11th Circuit Court of Appeals[2] 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.”

Continue Reading FDA Issues Orphan Drug Exclusivity Policy That Could Be a Catalyst for Future Litigation

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

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

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

I recommend against relying on any data I provide in today’s post.  I hope the data are at least somewhat accurate.  But they are not nearly as accurate as they should be, or as they could be, if FDA just released a key bit of information they have been promising to share for years.

One of the ways data scientists can provide insights is by grafting together data from different sources that paint a picture not seen elsewhere.  What I want to do is join the clinical trial data at www.clinicaltrials.gov with the data maintained by FDA of approved drugs, called drugs@FDA.  But I can’t, at least not with much accuracy.

Continue Reading Unpacking Averages: Connecting Published Clinical Trials with FDA Drug Approvals

On Tuesday, September 1, 2020, the Drug Enforcement Agency (“DEA”) proposed 2021 aggregate production quotas (APQs) for controlled substances in schedules I and II of the Controlled Substances Act (“CSA”) and an Assessment of Annual Needs (“AAN”) for the List I Chemicals pseudoephedrine, ephedrine, and phenylpropanolamine. This marks the second year that DEA has issued APQs pursuant to Congress’s changes to the CSA via the SUPPORT Act.  After assessing the diversion rates for the five covered controlled substances, DEA reduced the quotas for four: oxycodone, hydrocodone, hydromorphone and fentanyl.

DEA recently increased the APQ to allow for the additional manufacture of certain controlled substances in response to the COVID-19 pandemic and the need to provide greater access to these medications for patients on ventilator treatment.  According to DEA, that increased demand has been factored into the proposed APQs for 2021.

Comments are due by October 1, 2020.  Because DEA’s APQs determine the amount of quota DEA can allocate to individual manufacturers in 2021, adversely impacted parties should file comments soon.

Background on APQs

The CSA requires the establishment of aggregate production quotas for schedule I and II controlled substances, and an assessment of annual needs for the list I chemicals ephedrine, pseudoephedrine, and phenylpropanolamine.  These aggregate quotas limit the quantities of these substances to be manufactured – and with respect to the listed chemicals, imported –  in the United States in a calendar year, to provide for the estimated medical, scientific, research, and industrial needs of the United States, for lawful export requirements, and for the establishment and maintenance of reserve stocks.

Changes in Setting APQs Under The SUPPORT Act

The Substance Use-Disorder Prevention that Promotes Opioid Recovery and Treatment for Patients and Communities Act (“SUPPORT Act”) signed into law October 24, 2018, provided significant changes to the process for setting APQs.  First, under the CSA, aggregate production quotas are established in terms of quantities of each basic class of controlled substance, and not in terms of individual pharmaceutical dosage forms prepared from or containing such a controlled substance.  However, the SUPPORT Act provides an exception to that general rule by giving the DEA the authority to establish quotas in terms of pharmaceutical dosage forms if the agency determines that doing so will assist in avoiding the overproduction, shortages, or diversion of a controlled substance.

Additionally, the SUPPORT Act changed the way the DEA establishes APQs with respect to five “covered controlled substances”: fentanyl, oxycodone, hydrocodone, oxymorphone, and hydromorphone.  Under the SUPPORT Act, when setting the APQ for any of the “covered controlled substances,” DEA must estimate the amount of diversion.  The SUPPORT Act requires DEA to make appropriate quota reductions “as determined by the [DEA] from the quota the [DEA] would have otherwise established had such diversion not been considered.”  Furthermore, when estimating the amount of diversion, the DEA must consider reliable “rates of overdose deaths and abuse and overall public health impact related to the covered controlled substance in the United States,” and may take into consideration other sources of information the DEA determines reliable.

Estimating Diversion  

In accordance with this mandate under the SUPPORT Act, in setting the proposed APQs for 2021 DEA requested information from various agencies within the Department of Health and Human Services (“HHS”), including the U.S. Food and Drug Administration (“FDA”), Centers for Disease Control and Prevention (“CDC”), and the Centers for Medicare and Medicaid Services (“CMS”), regarding overdose deaths, overprescribing, and the public health impact of covered controlled substances.  DEA also solicited information from each state’s Prescription Drug Monitoring Program (“PDMP”), and any additional analysis of prescription data that would assist DEA in estimating diversion of covered controlled substances.

After soliciting input from these sources, DEA extracted data on drug theft and loss from its internal databases and seizure data by law enforcement nationwide.  DEA then calculated the estimated amount of diversion by multiplying the strength of the active pharmaceutical ingredient (“API”) listed for each finished dosage form by the total amount of units reported to estimate the metric weight in kilograms of the controlled substance being diverted.

Continue Reading Deadline Looms for Responding to DEA’s Proposed Aggregate Production Quotas for 2021

FDA took two important steps last week to clarify the regulatory landscape for cannabis products, including CBD products.  First, FDA issued a draft guidance on Quality Considerations for Clinical Research Involving Cannabis and Cannabis Derived Compounds.  This guidance builds off of earlier guidance FDA has issued about the quality and regulatory considerations that govern the development and FDA approval of cannabis and/or cannabinoid drug products.  See e.g., here and here.  The draft guidance iterates a federal standard for calculating delta-9 THC content in cannabis finished products, which addresses a significant gap in federal policy regarding those products.  While the testing standard is neither final nor binding on FDA or DEA, when finalized it would iterate what FDA considers to be a scientifically valid method for making the determination of whether a cannabis product is a Schedule I controlled substance.  Therefore, it may be useful in many contexts, including federal and state cannabis enforcement actions.  We encourage affected parties to file comments on FDA’s Guidance, which they may do until September 21, 2020.

Second, FDA sent to the Office of Management and Budget for review a proposal on how FDA intends to exercise enforcement discretion over CBD consumer products.  See here.  While the contents of this guidance have not yet been made public, we forecast that it likely will align with FDA’s past enforcement actions and memorialize the agency’s intent to pursue enforcement actions against CBD consumer product companies that make egregious claims about their products treating or preventing serious diseases or conditions.

Guidance on Considerations for Cannabis Clinical Research

FDA’s guidance recognizes that Congress’s enactment of the Agricultural Improvement Act of 2018 (“2018 Farm Bill”) improved domestic access to pre-clinical and clinical cannabis research material that may be used in the research and development of novel therapies.   However, currently marijuana only may be obtained domestically from the University of Mississippi under contract with the National Institute on Drug Abuse.  While DEA issued a policy in 2016 to allow for the additional registration of marijuana cultivators for legitimate research and licit commercial purposes, the Office of Legal Counsel in June 2018 issued an opinion finding that such policy violates the United States’ obligations under applicable treaties.  However, in March of this year, DEA issued a proposed rule to allow for the registration of additional cultivators of cannabis for these licit purposes.  See here.

There is an alternative pathway to the procurement of Schedule I research material which FDA’s guidance does not mention: importation.  Researchers may obtain certain Schedule I material pursuant to a federal DEA Schedule I importer registration, and DEA has in the past issued such registrations.  See 21 CFR 1301.13(e)(1)(viii).

Continue Reading FDA Issues Draft Guidance on Cannabis Clinical Research and Sends CBD Enforcement Discretion Guidance to OMB for Review

On March 18, 2020, the United States Food and Drug Administration (FDA) announced the suspension of all domestic routine surveillance facility inspections until further notice. FDA took this measure to protect the health and well-being of its staff and those who conduct the inspections for the agency under contract at the state level, and due