Subject: Prescription Drugs

FDA Finalizes Voluntary Recall Guidance Imploring Companies to Be “Recall Ready”

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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.

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It’s an MDL World: The JPML issues its first orders of the year, creating two new MDLs

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Centralization of claims in multidistrict litigation has become the new normal—so much so, that MDL proceedings now comprise more than 50 percent of the federal civil caseload. But has MDL practice in the United States peaked? Only time will tell. While the total number of MDL cases remains high (424,720 cases as of mid-February), the vast majority of these cases are concentrated in just a few of the more crowded MDL dockets. And as the annual MDL statistics in recent years show, the total number of new MDL petitions submitted, and granted, has been in decline. In 2021, for example, the Judicial Panel on Multidistrict Litigation received 33 total MDL petitions, granting only 19—compared with 44 petitions (26 granted) the year before.

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The Rule 702 Toolbox: Cherry-Picking Is a Recipe for Exclusion

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Most courts (but certainly, and unfortunately, not all of them) recognize that cherry-picking is a cardinal sin under Rule 702.  Science generally requires a rigorous and conservative approach to evaluating cause-and-effect relationships.  This schema inherently clashes with litigation, an arena where parties prioritize results over neutral principles of process purity.

“Cherry-picking” involves the selective consideration of facts and data to support a desired or pre-determined result, rather than the analysis of all relevant facts and data to find a scientific truth (or determine that the truth remains elusive based on the available facts and data).  It evades the scrupulous adherence to principles of objectivity, rigor, and process validity that are the hallmark of the scientific method.  In Daubert-speak, such a methodology does not produce “scientific knowledge.”  Rather, cherry-picking represents a failure of methodology that cannot be waived off as a matter of weight rather than admissibility.

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FDA Issues Draft Guidance for Sponsors and Other Stakeholders on Using Registries as RWD to Support Regulatory Decision-Making

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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.

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Going Paperless: What Manufacturers Need to Know Before Digitizing Warnings

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By the time the COVID-19 pandemic began, society was well into the so-called “Digital Age,” relying heavily on electronic communications, apps, websites, and the like to go about daily activities. Everything from ordering food to taking the bus to work could be achieved and tracked through a simple app. During the pandemic, the reliance on electronic mediums went from preferable to necessary, as many businesses shut down and transitioned to a remote or online-only presence.

The escalation of the digital age has led some manufacturers to consider electronic warnings for their products, through the manufacturer’s website, by providing a QR code, or by recommending (or requiring) the consumer to download an app. Even the American National Standards Institute (ANSI) has bought into digital warnings. ANSI’s Z535 standards provide guidance for product manufacturers related to the size, content, and location of warnings. Recently, ANSI created a subcommittee on warnings in electronic media and is in the process of developing a new standard, ANSI Z535.7, for safety information in electronic media. This new standard is expected to be published by December 2022. The FDA has also recently utilized electronic means to communicate information regarding the COVID-19 vaccines. In October 2021, the FDA published three Consumer Fact Sheets for the three currently authorized vaccines on its website and included a QR Code linked to the “most recent” COVID-19 Vaccine Fact Sheets.

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FDA Releases Draft Guidance for Sponsors on How to Comply with Study Data Standards When Submitting RWD-Sourced Study Data

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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).

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