On October 3, 2023, the U.S. Food and Drug Administration (FDA) published its widely anticipated proposed rule on the regulation of laboratory developed tests (LDTs). The proposed rule and policy are the latest in an over decade-long discourse amongst interested stakeholders – laboratories, IVD manufacturers, regulatory agencies, Congress, providers, and patients – as FDA has sought to enhance oversight over LDTs.

In this Insight, we recap the underpinnings of the proposed rule and policy, including the two lengthy discussions contained in the proposed rule on (1) the rationale for the agency’s proposed phaseout policy and (2) FDA’s legal authority for issuing the rule.  Stay tuned next week for our additional analysis of the details of FDA’s proposed five-stage “phaseout” policy and the open questions that remain.

Contact the authors or a member of the Goodwin Life Sciences Regulatory & Compliance team for any questions. Read the full Insight here.

On September 29, 2023, the U.S. Food and Drug Administration (FDA) posted and scheduled for publication its long-awaited proposed rule concerning FDA regulation of laboratory developed tests (LDTs).  If enacted, the proposed rule would amend the Agency’s regulations to make explicit that in vitro diagnostic products (IVDs) are devices under the Federal Food, Drug, and Cosmetic Act; and this includes when the manufacturer of the IVD is a laboratory.

Upon finalization of the rule, FDA proposes to phase out its general “enforcement discretion” approach for LDTs so that tests manufactured by a laboratory would generally fall under the same enforcement approach as other IVDs.

Comments to the proposed rule are due 60 days after the date of publication of the proposed rule in the Federal Register. We will provide our full analysis of the proposed rule in the coming days. Contact the authors or a member of the Goodwin Life Sciences Regulatory & Compliance team for any questions.

Last week, FDA released a draft guidance, “Labeling for Biosimilar and Interchangeable Biosimilar Products” that—when finalized—will revise and replace its July 2018 final guidance, “Labeling for Biosimilar Products.”  FDA noted that this 2023 Draft Guidance reflects recommendations based on the “valuable experience about labeling considerations” that FDA has gained through its approval of 42 biosimilar products, including four interchangeable biosimilar products.

Notably, the 2023 Draft Guidance provides further recommendations regarding when to use a biosimilar or interchangeable biosimilar product name, and when to use the reference product name in labeling:

• The biosimilar or interchangeable biosimilar product’s proprietary name[1] (or if the product does not have a proprietary name, its proper name[2]) should be used when –
  • Information in the labeling is specific to the biosimilar (or interchangeable biosimilar) product, including such references to the product in the INDICATIONS AND USAGE, DOSAGE AND ADMINISTRATION, DESCRIPTION, and HOW SUPPLIED/STORAGE AND HANDLING sections, and/or
  • For “directive statements and recommendations for preventing, monitoring, managing, or mitigating risk,” including such references to the product in the BOXED WARNING, CONTRAINDICATIONS, WARNINGS AND PRECAUTIONS, and DRUG INTERACTIONS sections.
• When referring to the drug substance in the labeling, the biosimilar or interchangeable biosimilar product’s proper name should be used.
• When information specific to the reference product is described in the biosimilar or interchangeable biosimilar product’s labeling (for example, data from clinical trials of the reference product in the ADVERSE REACTIONS and CLINICAL STUDIES sections), the reference product’s proper name should be used.
• In sections of the labeling containing information that applies to both the biosimilar (or interchangeable biosimilar) product and the reference product—such as BOXED WARNING, CONTRAINDICATIONS, WARNINGS AND PRECAUTIONS, ADVERSE REACTIONS—the labeling should use the core name of the reference product followed by the word “products.”[3]

FDA acknowledges that the application of these recommendations is highly context-dependent and may not always be clear, but recommends that biosimilar and interchangeable biosimilar product sponsors evaluate all statements in product labeling carefully to determine the most appropriate product identification approach in each instance.

Another noteworthy aspect of the 2023 Draft Guidance is the Agency’s recommendation regarding the biosimilarity statement and footnote in the HIGHLIGHTS section of a biosimilar or interchangeable biosimilar product’s labeling.[4]  Previously, FDA recommended a biosimilarity statement for a biosimilar product and an interchangeability statement for an interchangeable biosimilar product.  The 2023 Draft Guidance now recommends a statement and footnote in the HIGHLIGHTS section that the product is biosimilar to the reference product, regardless of whether the product is a biosimilar or an interchangeable biosimilar to the reference product. In the Federal Register notice announcing the 2023 Draft Guidance, FDA acknowledges that this marks an “evolution in our thinking” and explains that “a labeling statement noting that certain products within a 351(k) [Biologics License Application] have been approved as interchangeable, and explaining the interchangeability standard, is not likely to be useful to prescribers, who can prescribe both biosimilar and interchangeable biosimilar products in place of the reference product with equal confidence that they are as safe and effective as their reference products.” FDA further states that “information about interchangeability is more appropriately located in the Purple Book rather than labeling.”

Other notable elements of the 2023 Draft Guidance include recommendations regarding how to describe pediatric use data in a range of scenarios and how to incorporate immunogenicity data. With respect to immunogenicity data, the 2023 Draft Guidance suggests that a contextual paragraph[5] generally be included in the relevant CLINICAL PHARMACOLOGY subsection before describing the available immunogenicity data for the reference product and the biosimilar or interchangeable biosimilar product.  The 2023 Draft Guidance also outlines the Agency’s expectations for patient labeling—such as a Medication Guide, Patient Information, or Instructions for Use—for a biosimilar or interchangeable biosimilar product, if the reference product has such patient labeling.

Information on how to submit comments on the 2023 Draft Guidance can be found at https://www.regulations.gov/docket/FDA-2016-D-0643.

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[1] The proprietary name of a biosimilar product is a brand name determined by the sponsor.  The fictitious example provided in the 2023 Draft Guidance is “NEXSYMEO.”

[2] The proper name of a biosimilar product is the nonproprietary name designated by FDA that consists of a biological product’s core name plus a unique four-letter suffix.  The fictitious example provided in the 2023 Draft Guidance is “replicamab-cznm.”

[3] The fictitious example provided by FDA in the 2023 Draft Guidance is “replicamab products”.

[4] The fictitious example provided by FDA in the 2023 Draft Guidance is “NEXSYMEO (replicamab-cznm) is biosimilar* to JUNEXANT (replicamab-hjxf)” and the accompanying footnote is “Biosimilar means that the biological product is approved based on data demonstrating that it is highly similar to an FDA-approved biological product, known as a reference product, and that there are no clinically meaningful differences between the biosimilar product and the reference product. Biosimilarity of [BIOSIMILAR OR INTERCHANGEABLE BIOSIMILAR PRODUCT’S PROPRIETARY NAME] has been demonstrated for the condition(s) of use (e.g., indication(s), dosing regimen(s)), strength(s), dosage form(s), and route(s) of administration) described in its Full Prescribing Information.”

[5] The Agency’s suggested paragraph is, “The observed incidence of anti-drug antibodies is highly dependent on the sensitivity and specificity of the assay.  Differences in assay methods preclude meaningful comparisons of the incidence of anti-drug antibodies in the studies described below with the incidence of anti-drug antibodies in other studies, including those of [proper name of reference product] or of other [core name] products.”

On September 6, 2023, the US Food and Drug Administration (FDA) released a trio of draft guidances in its efforts to “strengthen and modernize” the 510(k) Program and provide for more “predictability, consistency, and transparency” for the 510(k) premarket review process. In this post, we discuss the two new draft guidances with broad applicability to the 510(k) Program:

• “Best Practices for Selecting a Predicate Device to Support a Premarket Notification [510(k)] Submission”
• “Recommendations for the Use of Clinical Data in Premarket Notification [510(k)] Submissions”

The two draft guidances address a number of fundamental issues of concern with the 510(k) process.

Read the full client alert here.

Throughout August 2023, the Office of Information and Regulatory Affairs, Office of Management and Budget, Executive Office of the President (“OIRA”) has held stakeholder meetings regarding a proposed rule which, if enacted, would amend the U.S. Food and Drug Administration’s  (“FDA’s”) regulations to make explicit that laboratory developed tests (“LDTs”) are devices under the Federal Food, Drug, and Cosmetic Act. The next stakeholder meeting on the proposed rule is scheduled for September 6, 2023.

Per its website, OIRA received the proposed rule from FDA on July 26, 2023. The proposed rule was initially published this past spring on the Biden Administration’s Unified Agenda of Regulatory and Deregulatory Actions with a target publication date of August 2023. The forthcoming stakeholder meeting on September 6th suggests that OIRA may continue its review process well into September, if not later.

The publication of the proposed rule would mark the first significant FDA action on LDTs since its two 2014 draft guidances (available here and here) and 2017 discussion paper. The proposed rule is also expected to be controversial after prior U.S. Department of Health & Human Services statements concerning regulation of LDTs and legislative attempts to further define the LDT regulatory framework. Once cleared by OIRA, the proposed rule will be published in the Federal Register and subject to public comment.

We will continue to monitor for updates on the LDT proposed rule. Contact Goodwin Life Sciences Regulatory & Compliance team members for any questions.

The U.S. Food and Drug Administration recently issued its draft guidance entitled “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions.” The draft guidance follows the passage of the Food and Drug Omnibus Reform Act of 2022 (FDORA), which explicitly authorized the Agency to approve or clear Predetermined Change Control Plans (PCCPs).

We summarize some of the key takeaways from FDA’s draft guidance.  Read the client alert here.

On March 24, 2023, the FDA’s Center for Devices and Radiological Health announced the issuance of two much anticipated final guidances that describe the Agency’s transition plans for medical devices that fall within certain COVID-19 enforcement policies or that were issued emergency use authorizations (“EUA”s):

• Transition Plan for Medical Devices That Fall Within Enforcement Policies Issued During the Coronavirus Disease 2019 (COVID-19) Public Health Emergency (the “Enforcement Policies Final Guidance”)

• Transition Plan for Medical Devices Issued Emergency Use Authorizations (EUAs) Related to Coronavirus Disease 2019 (COVID-19) (the “EUA Transition Final Guidance”)

The guidances follow the announcement in early 2023 that the Biden Administration plans to wind-down a number of pandemic-related programs and to allow the COVID-19 public health emergency (“PHE”) declaration, which has been in effect since January 2020, to expire on May 11, 2023.

We summarize some of the key takeaways from FDA’s finalized transition plans.  Read the client alert here.

Since the beginning of the COVID-19 pandemic, the United States Food and Drug Administration (“FDA”) has issued more than eighty (80) guidance documents describing flexibilities that would be available to manufacturers of medical devices, drugs and biological products, and foods during the public health emergency.  Several of these guidance documents have been modified, updated, or withdrawn as circumstances have changed, and on March 13, 2023, the FDA issued a notice in the Federal Register that outlines how it intends to unwind a large swath of COVID-19-related guidance documents that are still in effect.  FDA sorted seventy-two (72) COVID-19-related guidances into several categories, based on how long and in what form they will continue to be in effect after the expiration of the public health emergency declaration, which is expected on May 11, 2023.

Read the client alert here.

Today, developers of innovative medical devices are increasingly utilizing artificial intelligence (AI) and machine learning (ML) technologies to derive important insights with the promise of transforming the delivery of healthcare. Yet, concerns regarding the transparency of AI/ML-enabled devices, or the degree to which information about such devices is communicated to stakeholders, threatens not only perceptions as to the safety and effectiveness of such devices by regulators, but also trust in such technologies from patients and healthcare providers alike.

Read the full article written by Steven Tjoe in PM360 Magazine.

Visit the Goodwin on Medtech hub to stay informed on important developments affecting medtech innovators and investors.

On October 27, 2021, the U.S. Food and Drug Administration (FDA), Health Canada and the United Kingdom’s Medicines and Healthcare products Regulatory Agency (MHRA) issued a set of ten guiding principles meant to aid the development of Good Machine Learning Practice (GMLP).

Artificial intelligence and machine learning (AI/ML) offers the potential to analyze the vast amount of real-world data generated from health care every day to provide transformative insights. These insights can not only help improve individual product design and performance, but also hold the promise of transforming health care.

However, AI/ML technology has unique complexities and considerations. The goal of these guiding principles is to help promote safe, effective, and high-quality medical devices that use AI/ML to best cultivate the future of this rapidly progressing field.

Although not formal or binding, as companies continue to leverage AI/ML in their medical devices, they should remain mindful of each of the ten guiding principles:

1. Leveraging Multi-Disciplinary Expertise Throughout the Total Product Life Cycle

Companies should leverage internal and external multi-disciplinary expertise to ensure they have a thorough understanding of the model’s integration into the clinical workflow, and the desired benefits and associated patient risks, to ensure the safety and effectiveness of the device while serving clinically meaningful needs throughout the product lifecycle.

2. Implementing Good Software Engineering and Security Practices

Companies should implement as part of model design data quality assurance, data management, good software engineering practices, and robust cybersecurity practices.

3. Utilizing Clinical Study Participants and Data Sets that Are Representative of the Intended Patient Population

Companies should ensure that their data collection protocols have sufficient representation of relevant characteristics of the intended patient population, use, and measurement inputs in an adequate sample size in their clinical study and training and test datasets so that results can reasonably be generalized to the population of interest.  Data collection protocols appropriate for the intended patient population may help to identify where the model may underperform and may mitigate bias.

4. Keeping Training Sets and Test Sets Independent

Companies should consider and address all sources of dependence between the training and test datasets, including patient, data acquisition, and site factors to guarantee independence.

5. Selecting Reference Datasets Based Upon Best Available Methods

Companies should use accepted, best available methods for developing a reference dataset, i.e., a reference standard, to ensure clinically relevant and well characterized data are collected (and that the reference’s limitations are understood).  Where available, companies should use accepted reference datasets in model development and testing that promote and demonstrate model robustness and generalizability across the target population.

6. Tailoring Model Design to the Available Data and Reflecting the Intended Use of the Device

Companies should have a solid understanding of the clinical benefits and risks related to the product and utilize this understanding to create clinically meaningful performance goals.  Additionally, companies should ensure the model design is suited to the available data and supports active mitigation of the known risks.

7. Focusing on the Performance of the Human-AI Team

Where the model has a human element, companies should consider human factors and human interpretability of the model outputs.

8. Testing Demonstrates Device Performance during Clinically Relevant Conditions

Companies should develop statistically sound tests and execute them to assess device performance data independent of the training data set. Such assessment should be conducted in clinically relevant conditions with consideration given to the intended use population, important subgroups, clinical environment and use by the Human AI-Team, measurement inputs, and potential confounding factors.

9. Providing Users Clear, Essential Information

Companies should provide users ready access to clear, contextually relevant information that is appropriate for the target audience. Such information includes not only information pertaining to the product’s intended use and indications for use, performance of the model for appropriate subgroups, characteristics of the data used to train and test the model, acceptable inputs, known limitations, user interface interpretation, and clinical workflow integration of the model, but also users should be made aware of device modifications, updates from real-world performance monitoring, the basis for decision-making (when available), and a way to communicate product concerns to the company.

10. Monitoring Deployed Models for Performance and Managing Re-Training Risks

Companies should deploy models that are capable of being monitored in real-world usage with a focus on maintaining or improving safety and performance. Further, when models are trained after deployment, companies should ensure there are appropriate controls in place to manage risks that may impact the safety and performance of the model.

FDA’s expectations with respect to GMLP will continue to advance and become more granular as additional stakeholder input is considered.  The docket for FDA’s GMLP Guiding Principles, FDA-2019-N-1185, is open for public comment.

Visit the Goodwin on Medtech hub to stay informed on important developments affecting medtech innovators and investors.