Limitations of NHIC claim code-based surveillance and the necessity of UDI implementation in Korea.

The E-Health Big Data Evidence Innovation Network (FeederNet) in Korea, based on the observational medical outcomes partnership (OMOP) common data model (CDM), had 72.3% participation from tertiary hospitals handling severe diseases as of October 2022. While this contributes to the activation of multi-institutional research, concerns about the comprehensiveness of device data persist due to the adoption of national health insurance corporation (NHIC) claim codes as device identifiers in the medical device field. This study critically evaluated the effectiveness and compatibility of NHIC claim codes and unique device identifier (UDI) within FeederNet to identify the optimal identifier for efficient Post-market surveillance (PMS). Specifically, this study addressed three main questions: (1) the number of UDIs classified as NHIC-covered items, (2) the number of UDIs included in each NHIC claim code, and (3) the number of NHIC claim codes each UDI covers. Among the 1,979,655 UDIs registered domestically, only 36.02% (712,983) were classified as covered by National Health Insurance. NHIC-covered medical devices were limited to categories (A) medical devices, (B) medical supplies, and (C) dental materials, excluding most software and in vitro diagnostics (IVD). Multiple UDIs could be registered under a single NHIC claim code, and a single UDI could be registered under multiple NHIC claim codes. Only 32.62% (13,756/42,171) of NHIC claim codes had registered UDIs, with an average of 53 UDIs per claim code. Of the UDIs listed as NHIC covered, 92.39% (659,046/713,341) had one claim code, while 7.25% (51,652) had multiple claim codes. Additionally, 2643 UDIs were listed as NHIC covered but had no registered claim codes. Due to this complex relationship, NHIC claim code-based PMS may pool safe and unsafe models or disperse problematic models across multiple claim codes, leading to a lower problem rate or insignificant differences between claim codes, thus reducing signal detection sensitivity compared to UDI-based PMS. In conclusion, NHIC claim code-based PMS has limitations in granularity and signal detection sensitivity, necessitating the adoption of UDI-based PMS to address these issues. The UDI system can enhance the accuracy of medical device identification and tracking, playing a crucial role in generating real-world evidence (RWE) by integrating data from various sources. Future research should explore specific strategies for integrating and utilizing UDI with NHIC claim codes, contributing to the implementation of a more reliable and comprehensive PMS in Korea's healthcare system.

Enhancing Postmarketing Surveillance of Medical Products With Large Language Models.

Importance: The Sentinel System is a key component of the US Food and Drug Administration (FDA) postmarketing safety surveillance commitment and uses clinical health care data to conduct analyses to inform drug labeling and safety communications, FDA advisory committee meetings, and other regulatory decisions. However, observational data are frequently deemed insufficient for reliable evaluation of safety concerns owing to limitations in underlying data or methodology. Advances in large language models (LLMs) provide new opportunities to address some of these limitations. However, careful consideration is necessary for how and where LLMs can be effectively deployed for these purposes. Observations: LLMs may provide new avenues to support signal-identification activities to identify novel adverse event signals from narrative text of electronic health records. These algorithms may be used to support epidemiologic investigations examining the causal relationship between exposure to a medical product and an adverse event through development of probabilistic phenotyping of health outcomes of interest and extraction of information related to important confounding factors. LLMs may perform like traditional natural language processing tools by annotating text with controlled vocabularies with additional tailored training activities. LLMs offer opportunities for enhancing information extraction from adverse event reports, medical literature, and other biomedical knowledge sources. There are several challenges that must be considered when leveraging LLMs for postmarket surveillance. Prompt engineering is needed to ensure that LLM-extracted associations are accurate and specific. LLMs require extensive infrastructure to use, which many health care systems lack, and this can impact diversity, equity, and inclusion, and result in obscuring significant adverse event patterns in some populations. LLMs are known to generate nonfactual statements, which could lead to false positive signals and downstream evaluation activities by the FDA and other entities, incurring substantial cost. Conclusions and Relevance: LLMs represent a novel paradigm that may facilitate generation of information to support medical product postmarket surveillance activities that have not been possible. However, additional work is required to ensure LLMs can be used in a fair and equitable manner, minimize false positive findings, and support the necessary rigor of signal detection needed for regulatory activities.

Using Digital Instructions to Address on-Market Product Complaints and Improve User Experiences.

Manufacturers of medical devices are legally required to carry out post-market surveillance and respond to any on-market complaints they receive about their device. When it comes to addressing use issues experienced on market, manufacturers often focus on revising the instructions for use (IFU) and labeling. One of the challenges with this approach is the potential need to re-validate via a human factors validation study, without knowing whether the changes made will be effective in addressing the identified issues. With populations becoming increasingly technically literate, there is now great potential to rethink the way we can support self-injection through digital and web-based tools. This paper introduces the potential of digital instructions to improve the user experience of self-injection and address known use issues, as well as the challenges of incorporating them in medical device design.

Premarket Pivotal Trial End Points and Postmarketing Requirements for FDA Breakthrough Therapies.

Importance: The US Food and Drug Administration (FDA) awards the breakthrough therapy designation to expedite development and review of therapeutics intended to treat serious conditions when preliminary clinical evidence demonstrates potential substantial improvement over existing therapies on clinically significant end points. Under the 21st Century Cures Act of 2016, the FDA is required to publish and routinely update a list of surrogate markers to provide industry sponsors with indication-specific information about end points that were or may be considered for approval. Therapeutics that are granted breakthrough therapy designation can receive accelerated or traditional approval; however, little is known about those approved through the latter pathway, where postmarketing confirmatory studies are typically not required, regardless of the end point used. Objective: To evaluate the primary end points used in premarket pivotal trials supporting FDA breakthrough therapy-designated approvals and to determine whether postmarketing studies confirming efficacy were required for approvals based on pivotal trials using surrogate markers as primary end points. Design, Setting, and Participants: This cross-sectional study used data from the Drugs@FDA database for all original breakthrough therapy-designated approvals from inception to December 31, 2023, in the US. The first designation was approved on November 1, 2013. Data analysis was performed in January 2024. Main Outcomes and Measures: Descriptive analyses were used to characterize the breakthrough therapy-designated indication approval pathways, the primary end points of pivotal efficacy trials, and their postmarketing requirements or commitments. Results: From 2013 to 2023, the FDA approved 157 original indications with breakthrough therapy designation. Of these, 52 (33%) were granted accelerated approval and 105 (67%) were granted traditional approval. All accelerated approvals were based on pivotal trials using surrogate markers as primary end points and had FDA-required postmarketing studies to confirm efficacy. Of these 52 indications, 51 (98%) were approved based on surrogate end points listed in the FDA table of surrogate end points for the same indication. Among traditional approvals, 61 (58%) were based on pivotal trials using surrogate markers as primary end points, of which 4 (7%) had FDA-required postmarketing studies to confirm efficacy and 39 (64%) were approved based on surrogate end points listed in the FDA table for the same indication. Conclusions and Relevance: In this cross-sectional study of original FDA breakthrough therapy-designated approvals from 2013 to 2023, trials supporting these approvals often used surrogate markers as primary end points (even when not approved via accelerated approval) and lacked FDA-required postmarketing studies to verify clinical benefit. These findings suggest that requiring postmarketing studies for breakthrough therapy-designated indications approved based on surrogate markers, regardless of approval pathway, may increase patient and clinician certainty of the expected clinical benefit.

Overview of global monitoring systems for the side effects and adverse events associated with medicinal cannabis use: a scoping review using a systematic approach.

OBJECTIVES: The use of cannabis-based medicine (CBM) as a therapeutic has surged in Australia over the past 5 years. Historically, the United Nations Single Convention on Narcotic Drugs (1961) prohibited cannabis use in Europe, the USA, the UK and Australia, leading to legislative resistance and limited preclinical data on CBM. Existing safety monitoring systems for CBM are poorly structured and do not integrate well into the workflows of busy health professionals. As a result, postmarketing surveillance is inconsistent. This review aims to evaluate international systems for monitoring CBM side effects and adverse events. DESIGN: To undertake a scoping review with a systematic approach, we used the Population, Intervention, Comparison, Outcome (PICO) framework to develop keyword elements, and two search queries to maximise search sensitivity and specificity. DATA SOURCES: Search queries were entered into Embase and Scopus for peer-reviewed literature, and additional searches for grey literature were conducted on 23 June 2023. ELIGIBILITY CRITERIA: We included 54 full-text articles in the review: 39 from peer-reviewed searches, 8 from grey literature and 7 from citations of relevant texts. DATA EXTRACTION AND SYNTHESIS: Our search yielded two main forms of monitoring systems: databases and registries. Out of the 24 monitoring systems identified, there were 10 databases and 14 registries, with databases often created by regulatory authorities. Systems differed in methods of causality assessment, level of detail collected, terminology and affiliations. RESULTS: Within the monitoring systems with enough published data for analysis, all except one remain active at the time of this review. VigiBase is the largest centralised monitoring system, receiving international case reports, however data heterogeneity persists. CONCLUSIONS: Our study emphasises the need for a centralised, consistent and accessible system for the postmarketing surveillance of side effects and adverse events associated with medicinal cannabis use.

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study.

BACKGROUND: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19. OBJECTIVE: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration's postmarket surveillance capabilities while minimizing public burden. This study aimed to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify 5 AESIs being monitored by the Center for Disease Control and Prevention for COVID-19 or other vaccines: anaphylaxis, Guillain-Barré syndrome, myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome, and febrile seizure. This study examined whether these phenotypes have sufficiently high positive predictive value (PPV) to ensure that the cases selected for surveillance are reasonably likely to be a postbiologic adverse event. This allows patient privacy, and security concerns for the data sharing of patients who had nonadverse events can be properly accounted for when evaluating the cost-benefit aspect of our approach. METHODS: AESI phenotype algorithms were developed to apply to electronic health record data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. To validate the performance of the algorithms, we applied them to electronic health record data from a US academic health system and provided a sample of cases for clinicians to evaluate. Performance was assessed using PPV. RESULTS: With a PPV of 93.3%, our anaphylaxis algorithm performed the best. The PPVs for our febrile seizure, myocarditis/pericarditis, thrombocytopenia syndrome, and Guillain-Barré syndrome algorithms were 89%, 83.5%, 70.2%, and 47.2%, respectively. CONCLUSIONS: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI postmarket detection.

Use of the Sentinel System to Examine Medical Product Use and Outcomes During Pregnancy.

While many pregnant individuals use prescription medications, evidence supporting product safety during pregnancy is often inadequate. Existing electronic healthcare data sources provide large, diverse samples of health plan members to allow for the study of medical product utilization during pregnancy, as well as pregnancy, maternal, and infant outcomes. The Sentinel System is a national medical product surveillance system that includes administrative claims and electronic health record databases from large national and regional health insurers. In addition to these data sources, Sentinel develops and maintains a sizeable selection of analytic tools to facilitate epidemiologic analyses in a way that protects patient privacy and health system autonomy. In this article, we provide an overview of Sentinel System infrastructure, including the Mother-Infant Linkage Table, parameterizable analytic tools, and algorithms to estimate gestational age and identify pregnancy outcomes. We also describe past and future Sentinel work that contributes to our understanding of the way medical products are used and the safety of these products during pregnancy.

Regulatory landscape and challenges in CAR-T cell therapy development in the US, EU, Japan, and India.

Chimeric Antigen Receptor-T cell (CAR-T) therapy has evolved as a revolutionary cancer treatment modality, offering remarkable clinical responses by harnessing the power of a patient's immune system to target and eliminate cancer cells. However, the development and commercialization of CAR-T cell therapies are accompanied by complex regulatory requirements and challenges. This therapy falls under the regulatory category of advanced therapy medicinal products. The regulatory framework and approval tools of regenerative medicine, especially CAR-T cell therapies, vary globally. The present work comprehensively analyses the regulatory landscape and challenges in CAR-T cell therapy development in four key regions: the United States, the European Union, Japan, and India. This work explores the unique requirements and considerations for preclinical studies, clinical trial design, manufacturing standards, safety evaluation, and post-marketing surveillance in each jurisdiction. Due to their complex nature, developers and manufacturers face several challenges. In India, despite advancements in treatment protocols and government-sponsorships, there are still several difficulties regarding access to treatment for the increasing number of cancer patients. However, India's first indigenously developed CAR-T cell therapy, NexCAR19, for B-cell lymphoma or leukemia, approved and available at a low cost compared to other available CAR-T therapies, raises great hope in the battle against cancer. Several strategies are proposed to address the identified hurdles from global and Indian perspectives. It discusses the benefits of aligning regulatory requirements across regions, eventually facilitating international development and enabling access to this transformative therapy.

Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration.

Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of its surveillance activities. Over the past decade, the FDA has explored the application of artificial intelligence (AI) to evaluate these reports to improve the efficiency and scientific rigor of the process. However, a gap remains between AI algorithm development and deployment. This viewpoint aims to describe the lessons learned from our experience and research needed to address both general issues in case-based reasoning using AI and specific needs for individual case safety report assessment. Beginning with the recognition that the trustworthiness of the AI algorithm is the main determinant of its acceptance by human experts, we apply the Diffusion of Innovations theory to help explain why certain algorithms for evaluating AEs at the FDA were accepted by safety reviewers and others were not. This analysis reveals that the process by which clinicians decide from case reports whether a drug is likely to cause an AE is not well defined beyond general principles. This makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers. Even accounting for the introduction of large language models, the pharmacovigilance community needs an improved understanding of causal inference and of the cognitive framework for determining the causal relationship between a drug and an AE. We describe specific future research directions that underpin facilitating implementation and trust in AI for drug safety applications, including improved methods for measuring and controlling of algorithmic uncertainty, computational reproducibility, and clear articulation of a cognitive framework for causal inference in case-based reasoning.

Premarket Evidence and Postmarketing Requirements for Real-Time Oncology Review Indication Approvals.

This cross-sectional study evaluates the use of the US Food and Drug Administration's Real-Time Oncology Review (RTOR) program in confirming the effectiveness of cancer drugs.

Online causal inference with application to near real-time post-market vaccine safety surveillance.

Streaming data routinely generated by social networks, mobile or web applications, e-commerce, and electronic health records present new opportunities to monitor the impact of an intervention on an outcome via causal inference methods. However, most existing causal inference methods have been focused on and applied to static data, that is, a fixed data set in which observations are pooled and stored before performing statistical analysis. There is thus a pressing need to turn static causal inference into online causal learning to support near real-time monitoring of treatment effects. In this paper, we present a framework for online estimation and inference of treatment effects that can incorporate new information as it becomes available without revisiting prior observations. We show that, under mild regularity conditions, the proposed online estimator is asymptotically equivalent to the offline oracle estimator obtained by pooling all data. Our proposal is motivated by the need for near real-time vaccine effectiveness and safety monitoring, and our proposed method is applied to a case study on COVID-19 vaccine safety surveillance.

[Erratum in "Point-of-Care Blood Glucose Testing: Post-Market Performance Assessment of the Accu-Chek Inform II Hospital-Use Glucose Meter" (DOI: 10.26442/00403660.2023.12.202522)].

In the article "Point-of-care blood glucose testing: post-market performance assessment of the Accu-Chek Inform II hospital-use glucose meter," published in the Terapevticheskii Arkhiv journal, Vol. 95, No.12, 2023 (DOI: 10.26442/00403660.2023.12.202522), errors were made: the term "measurements at the place of treatment" was changed, as well as the section "Conflict of interest." At the request of the authors' team, errors in the conflict of interest and the wording of the term have been corrected, and the section "Information about the authors" has been updated. The publisher replaced the original version of the published article with the corrected one; the information on the website was also corrected. Correct text of the section "Conflict of interest": Conflict of interest. All authors are not employees or consultants of Roche Diagnostics and have not received any compensation from Roche Diagnostics. Correct wording of the term in Russian: "измерения по месту лечения". Changes were made to the title of the article in Russian: "Измерения глюкозы по месту лечения: пострегистрационное испытание госпитального глюкометра Акку-Чек Информ II", the text of the abstract, keywords, citation, in the text of the article, and abbreviations. Information of the place of work has been updated: Center for Laboratory Diagnostics of the Russian Children Clinical Hospital, a Branch of the Pirogov Russian National Research Medical University. The publisher apologizes to readers and authors for the errors and is confident that the correction of errors will ensure the correct perception and interpretation of the results of the study described in the text.

Phase IV Studies: What Clinicians Need to Know!

Phase IV trials, also known as postmarketing safety and efficacy studies and postmarketing surveillance (PMS) studies, occur after a drug or medical device has received regulatory approval and is available in the market. These trials are designed to collect additional information regarding the product's safety, efficacy, and prolonged effects in a larger and more diverse patient population. The foremost goal of phase IV trials is to detect any rare or long-term adverse effects that may not have been identified during the prior phases of clinical development. During phase IV trials, pharmaceutical companies, academic institutions, or other research organizations conduct studies to evaluate various aspects of the product, including its real-world effectiveness, optimal use, and any potential safety concerns. The regulatory agencies play a role in overseeing these trials to ensure that they are conducted ethically and in compliance with good clinical practice (GCP) guidelines.

Lessons learned from a multi-data source research collaboration: The mirabegron post-authorization safety study program.

BACKGROUND: Many factors contribute to developing and conducting a successful multi-data source, non-interventional, post-authorization safety study (NI-PASS) for submission to multiple health authorities. Such studies are often large undertakings; evaluating and sharing lessons learned can provide useful insights to others considering similar studies. OBJECTIVES: We discuss challenges and key methodological and organizational factors that led to the delivery of a successful post-marketing requirement (PMR)/PASS program investigating the risk of cardiovascular and cancer events among users of mirabegron, an oral medication for the treatment of overactive bladder. RESULTS: We provide context and share learnings, including sections on research program collaboration, scientific transparency, organizational approach, mitigation of uncertainty around potential delays, validity of study outcomes, selection of data sources and optimizing patient numbers, choice of comparator groups and enhancing precision of estimates of associations, potential confounding and generalizability of study findings, and interpretation of results. CONCLUSIONS: This large PMR/PASS program was a long-term commitment from all parties and benefited from an effective coordinating center and extensive scientific interactions across research partners, scientific advisory board, study sponsor, and health authorities, and delivered useful learnings related to the design and organization of multi-data source NI-PASS.

Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects.

Translational approaches can benefit post-marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug-drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug-target-adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data-driven biological plausibility assessment in the context of challenging post-marketing DDI surveillance.

Unveiling the future: precision pharmacovigilance in the era of personalized medicine.

In the era of personalized medicine, pharmacovigilance faces new challenges and opportunities, demanding a shift from traditional approaches. This article delves into the evolving landscape of drug safety monitoring in the context of personalized treatments. We aim to provide a succinct reflection on the intersection of tailored therapeutic strategies and vigilant pharmacovigilance practices. We discuss the integration of pharmacogenetics in enhancing drug safety, illustrating how genetic profiling aids in predicting drug responses and adverse reactions. Emphasizing the importance of phase IV-post-marketing surveillance, we explore the limitations of pre-marketing trials and the necessity for a comprehensive approach to drug safety. The article discusses the pivotal role of pharmacogenetics in pre-exposure risk management and the redefinition of pharmacoepidemiological methods for post-exposure surveillance. We highlight the significance of integrating patient-specific genetic profiles in creating personalized medication leaflets and the use of advanced computational methods in data analysis. Additionally, we examine the ethical, privacy, and data security challenges inherent in precision medicine, emphasizing their implications for patient consent and data management.

Development and Application of a Data-Driven Signal Detection Method for Surveillance of Adverse Event Variability Across Manufacturing Lots of Biologics.

INTRODUCTION: Postmarketing drug safety surveillance research has focused on the product-patient interaction as the primary source of variability in clinical outcomes. However, the inherent complexity of pharmaceutical manufacturing and distribution, especially of biologic drugs, also underscores the importance of risks related to variability in manufacturing and supply chain conditions that could potentially impact clinical outcomes. We propose a data-driven signal detection method called HMMScan to monitor for manufacturing lot-dependent changes in adverse event (AE) rates, and herein apply it to a biologic drug. METHODS: The HMMScan method chooses the best-fitting candidate from a family of probabilistic Hidden Markov Models to detect temporal correlations in per lot AE rates that could signal clinically relevant variability in manufacturing and supply chain conditions. Additionally, HMMScan indicates the particular lots most likely to be related to risky states of the manufacturing or supply chain condition. The HMMScan method was validated on extensive simulated data and applied to three actual lot sequences of a major biologic drug by combining lot metadata from the manufacturer with AE reports from the US FDA Adverse Event Reporting System (FAERS). RESULTS: Extensive method validation on simulated data indicated that HMMScan is able to correctly detect the presence or absence of variable manufacturing and supply chain conditions for contiguous sequences of 100 lots or more when changes in these conditions have a meaningful impact on AE rates. Applying the HMMScan method to FAERS data, two of the three actual lot sequences examined exhibited evidence of potential manufacturing or supply chain-related variability. CONCLUSIONS: HMMScan could be utilized by both manufacturers and regulators to automate lot variability monitoring and inform targeted root-cause analysis. Broad application of HMMScan would rely on a well-developed data input pipeline. The proposed method is implemented in an open-source GitHub repository.

An overview of methodological flaws of real-world studies investigating drug safety in the post-marketing setting.

INTRODUCTION: The evaluation of the post-marketing safety profile of drugs is a continuous monitoring process for approved and marketed medicines and it is crucial for detecting new adverse drug reactions. As such, real-world studies are essential to complement pre-marketing evidence with information concerning drug risk-benefit profile and use in wider patient populations and they have a great potential to support post-marketing drug safety evaluations. AREAS COVERED: A detailed description of the main limitations of real-world data sources (i.e. claims databases, electronic healthcare records, drug/disease registers and spontaneous reporting system databases) and of the main methodological challenges of real-world studies in generating real-world evidence is provided. EXPERT OPINION: Real-world evidence biases can be ascribed to both the methodological approach and the specific limitations of the different real-world data sources used to carry out the study. As such, it is crucial to characterize the quality of real-world data, by establishing guidelines and best practices for the assessment of data fitness for purpose. On the other hand, it is important that real-world studies are conducted using a rigorous methodology, aimed at minimizing the risk of bias.

Assessment of the Impact of Mandated Postmarketing Pediatric-Focused Safety Reviews on Safety-Related Regulatory Actions 2013-2019.

The US Food and Drug Administration's (FDA's) routine postmarketing drug safety monitoring may lead to safety-related labeling changes for identified risks. Additionally, the Best Pharmaceuticals for Children Act (BPCA) and Pediatric Research Equity Act (PREA) require the FDA to conduct postmarket pediatric-focused safety reviews of adverse events. The purpose of these pediatric reviews is to identify risks associated with drug or biological products 18 months after the FDA approves a pediatric labeling change pursuant to studies conducted under the BPCA or PREA. These reviews are presented to the FDA Pediatric Advisory Committee (PAC) or publicly posted on FDA's website. The aim of this study was to evaluate the impact of pediatric reviews prompted by BPCA/PREA from October 1, 2013, to September 30, 2019. The impact was quantified by the number of new safety signals identified and the subsequent safety-related labeling changes resulting from pediatric reviews relative to safety-related labeling changes triggered by other data sources. Among 163 products with at least one pediatric review completed, a new safety signal that resulted in a safety-related labeling change was found for 5 of these products (representing 3 active ingredients); none described risks specific to the pediatric population. Between October 2013 and September 2021, there were 585 safety-related labeling changes implemented for products with at least one completed pediatric review. Less than 1% of 585 safety-related labeling changes were the result of a mandated pediatric review. Our study suggests that mandated pediatric reviews conducted 18 months after a pediatric labeling change provided minimal value over other postmarket safety surveillance activities.

US FDA Postmarketing Requirements and Commitments: A Systematic Assessment of Clinical Pharmacology Studies and Their Impact on US FDA Prescribing Information.

Many of the conditions for the safe and effective use of new molecular entities (NMEs) are understood at the time of initial drug approval. However, some remaining knowledge gaps can be addressed after drug approval through postmarketing requirements (PMRs) or commitments (PMCs) established by the US Food and Drug Administration (FDA). Our objective was to conduct an assessment of clinical pharmacology-related PMRs and PMCs established at the time of approval and evaluate the impact of fulfilled PMRs and PMCs on prescription information (PI). This analysis included clinical pharmacology-related PMRs and PMCs established for NMEs approved between 2009 and 2020. Of the 1171 PMRs and PMCs, over one-third were clinical pharmacology-related. Of these, 46% were to evaluate drug interactions, 16% were to evaluate drug dosing in patients with hepatic impairment, and 10% were related to dose. The majority (57%) of PMRs and PMCs were fulfilled at the time of analysis, with a median time to fulfillment of approximately 2.3 years. The majority (94%) of the fulfilled PMRs and PMCs, either with or without a PI revision, resulted in new or modified instructions for use or supported existing instructions for use. This is the first time that clinical pharmacology-related PMRs and PMCs have been catalogued and analyzed to understand their impact on PI. An understanding of the knowledge gaps that exist at the time of drug approval could inform the most effective and efficient methods for evidence generation prior to and after new drug approval.