Postmarketing Safety-Related Regulatory Actions for New Therapeutic Biologics Approved in the United States 2002-2014: Similarities and Differences With New Molecular Entities

FDA Adverse Event Reporting System (FAERS) Essentials: A Guide to Understanding, Applying, and Interpreting Adverse Event Data Reported to FAERS

The US Food and Drug Administration (FDA) performs safety assessments throughout the life cycle of a drug. Postmarketing safety surveillance promotes the identification of adverse events not known at the time of approval. Adverse event reports, also called individual case safety reports (ICSRs), submitted to FDA, are collected and stored in the FDA Adverse Event Reporting System (FAERS). ICSRs stored in FAERS may be reviewed-along with multiple other data sources-to detect potential safety signals and to perform a thorough evaluation to determine if a causal association exists between a drug and an adverse event. Although FAERS is a powerful tool for drug safety surveillance and assessment, understanding the content, application, and proper interpretation of the data contained in FAERS is necessary to reach scientifically and medically accurate conclusions and contextualize findings. This article aims to highlight considerations and explain fundamental concepts of FAERS to promote accurate analyses and appropriate interpretation of the data available.

Deduplicating the FDA adverse event reporting system with a novel application of network-based grouping

OBJECTIVE

To improve the reliability of data mining for product safety concerns in the Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) by robustly identifying duplicate reports describing the same patient experience.

MATERIALS AND METHODS

A duplicate detection algorithm based on a probabilistic record linkage algorithm, including features extracted from report narratives, and designed to support FAERS case safety review as part of the Information Visualization Platform (InfoViP) has been upgraded into a full deduplication pipeline for the entire FAERS database. The pipeline contains several new and updated components, including a network analysis-based community detection routine for breaking up sparsely connected groups of duplicates constructed from chains of pairwise comparisons. The pipeline was applied to all 29 million FAERS reports to assemble groups of duplicate cases.

RESULTS

The pipeline was evaluated on 12 human expert adjudicated data sets with a total of 2300 reports and was found to have better overall performance than the current tool used at the FDA for labeling duplicates on 10 of them, with F1 scores ranging from 0.36 to 0.93, with half above 0.75. Because minimizing false discovery increases human expert review efficiency, the improved deduplication pipeline was applied to all historic and daily incoming FAERS reports at FDA and identified about 5 million reports as duplicates.

CONCLUSIONS

The InfoViP deduplication pipeline is operating at FDA to identify duplicate case reports in FAERS and provide deduplicated input for improved efficiency and accuracy of safety review operations like adverse event data mining calculations.

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling

INTRODUCTION

The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents.

OBJECTIVE

Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes.

RESULTS

Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs.

CONCLUSION

The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.

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.

Prescriber perceptions of boxed warnings: A qualitative study

PURPOSE

To explore how boxed warning (BW) information fits within the context of prescribers' overall treatment decision-making and communication with patients.

METHODS

In-depth interviews (N = 52) were conducted with primary care providers and specialists. Participants were presented with one of two prescribing scenarios: (1) estrogen vaginal inserts to treat vulvovaginal atrophy (VVA) associated with menopause; or (2) direct-acting antivirals (DAA) to treat chronic hepatitis C virus infection (HCV). The semi-structured interviews explored participants' treatment decision-making within the scenario, reactions to current prescribing information for a product within the FDA-approved drug class, as well as their perceptions of BWs generally.

RESULTS

Across scenarios, providers described that the BW is only one of several factors that influence treatment decision-making. In the VVA scenario, symptom severity, family history, and experience with nonprescription drugs were raised as common factors that influence prescribing considerations; compared to comorbid infections, viral load, and HCV genotype in the HCV scenario. Perceptions of the DAA BW were generally positive or neutral, as many participants found the information important and appropriate. The VVA BW was viewed less favorably, with many participants stating the BW overstates the risk for this drug.

CONCLUSIONS

Findings suggest that BWs are one of several factors that influence providers' treatment decisions, and BW influence largely depends on context. Providers across scenarios expressed notable differences in their perceptions of the risk information provided in the presented BWs; however, across scenarios participants expressed consideration of how patients may perceive the BW.

The Dawn of a New Era: Targeting the "Undruggables" with Antibody-Based Therapeutics

The high selectivity and affinity of antibodies toward their antigens have made them a highly valuable tool in disease therapy, diagnosis, and basic research. A plethora of chemical and genetic approaches have been devised to make antibodies accessible to more "undruggable" targets and equipped with new functions of illustrating or regulating biological processes more precisely. In this Review, in addition to introducing how naked antibodies and various antibody conjugates (such as antibody-drug conjugates, antibody-oligonucleotide conjugates, antibody-enzyme conjugates, etc.) work in therapeutic applications, special attention has been paid to how chemistry tools have helped to optimize the therapeutic outcome (i.e., with enhanced efficacy and reduced side effects) or facilitate the multifunctionalization of antibodies, with a focus on emerging fields such as targeted protein degradation, real-time live-cell imaging, catalytic labeling or decaging with spatiotemporal control as well as the engagement of antibodies inside cells. With advances in modern chemistry and biotechnology, well-designed antibodies and their derivatives via size miniaturization or multifunctionalization together with efficient delivery systems have emerged, which have gradually improved our understanding of important biological processes and paved the way to pursue novel targets for potential treatments of various diseases.

How do safety warnings on medicines affect prescribing?

INTRODUCTION

Many adverse effects of medicines only become known after approval, prompting regulatory agencies to issue post-market safety advisories to inform clinicians and support safer care. Our team evaluated advisories issued by national regulators in Australia, Canada, Denmark, the United Kingdom, and the United States from 2007 to 2016 inclusive, comparing regulators' decisions to warn, effects on prescribing, doctors' awareness and responses to warnings, relevant regulatory policies, and specific case studies.

AREAS COVERED

Based mainly on our research program and a narrative review, this commentary describes how often regulators issue safety advisories and effects on clinical practice. We found extensive differences in decisions to warn, timing and content of warnings. Monitoring advice is often inadequate. The most systematic estimate suggests an average reduction in prescribing of around 6% compared with settings with no advisory. Interviews with doctors suggest limited awareness, uptake, and at times belief in these warnings.

EXPERT OPINION

Post-market safety advisories are an important intervention aiming to improve prescribing and use of medicines. However, differing warnings mean that some patients may be exposed to riskier prescribing than others. Better integration of new safety information into clinical practice is needed, as well as improved transparency, independence, and public engagement in regulatory decision-making.

On the use of the likelihood ratio test methodology in pharmacovigilance

The safety of medical products due to adverse events (AE) from drugs, therapeutic biologics, and medical devices is a major public health concern worldwide. Likelihood ratio test (LRT) approaches to pharmacovigilance constitute a class of rigorous statistical tools that permit objective identification of AEs of a specific drug and/or a class of drugs cataloged in spontaneous reporting system databases. However, the existing LRT approaches encounter certain theoretical and computational challenges when an underlying Poisson model assumption is violated, including in cases of zero-inflated data. We briefly review existing LRT approaches and propose a novel class of (pseudo-) LRT methods to address these challenges. Our approach uses an alternative parametrization to formulate a unified framework with a common test statistic that can handle both Poisson and zero-inflated Poisson (ZIP) models. The proposed framework is computationally efficient, and it reveals deeper insights into the comparative behaviors of the Poisson and the ZIP models for handling AE data. Our extensive simulation studies document notably superior performances of the proposed methods over existing approaches particularly under zero-inflation, both in terms of statistical (eg, much better control of the nominal level and false discovery rate with substantially enhanced power) and computational ( ∼ $$ \sim $$ 100-500-fold gains in average running times) performance metrics. An application of our method on the statin drug class from the FDA FAERS database reveals interesting insights on potential AEs. An R package, pvLRT, implementing our methods has been released in the public domain.

Association of expedited review programmes with postmarketing safety events of new drugs approved by the US food and drug administration between 2007 and 2017

Objective

To explore the associations between the risks of postmarketing safety events of new drugs and the four expedited programmes of priority review, accelerated approval, fast track and breakthrough therapy established by the US Food and Drug Administration (FDA); and to investigate whether multiple uses of expedited programmes, and the combinations of expedited programmes with orphan designation, were relevant to different safety profiles.

Design

Cohort study.

Setting

USA.

Participants

All new drugs approved by the FDA between 1 January 2007 and 31 December 2017, followed up until 10 April 2021.

Outcome measures

Safety events included safety-related withdrawal, new boxed warning, drug safety communication, postapproval risk evaluation mitigation strategy and safety-related labelling changes. The duration from marketing approval to the occurrence of a safety event was measured.

Method

Cox models were performed to determine the factors related to the time-to-safety event.

Results

The FDA approved 338 new drugs between 2007 and 2017, among which 53.6% (181) were under expedited review and 32.2% (109) received two or more expedited programmes. It took median time of 1.75 years (IQR 1.10–2.93) and 2.31 years (IQR 1.33–4.21), respectively, for new drugs to be observed of their first event and first serious event. The raised risk for first safety event was found to associate with breakthrough therapy (adjusted HR 1.83; 95% CI 1.21 to 2.77; p=0.004), and with the combination of accelerated approval with orphan designation (adjusted HR 2.84; 95% CI 1.12 to 7.23; p=0.028). Triple or more use of expedited programmes correlated with higher risk for first serious event (adjusted HR 4.16; 95% CI 1.69 to 10.22; p=0.002).

Conclusions

The increased risks of the breakthrough therapies, accelerated orphan drugs and triple or more use of expedited programmes indicated the necessity for intensive postmarketing risk surveillance.

Postmarketing safety of orphan drugs: a longitudinal analysis of the US Food and Drug Administration database between 1999 and 2018

Background

Information about the specific regulatory environment of orphan drugs is scarce and inconsistent. Uncertainties surrounding the postmarketing long-term safety of orphan drugs remain. This study aimed to evaluate the labelling changes of orphan drugs and to identify postmarketing safety-associated approval factors.

Methods

This retrospective cohort study includes all drugs with orphan drug designation approved by the Center for Drug Evaluation and Research of the US Food and Drug Administration between 1999 and 2018. Main outcomes are safety-related labelling changes up to 31 December 2019. We defined any safety-related labelling changes as postmarketing safety events (PMSE). Safety-related withdrawals, suspensions, and boxed warnings were further categorised as severe postmarketing safety events (SPSE). Outcome measurements include frequencies of PMSE, SPSE, and association between approval factors and the occurrence of safety events.

Results

Amongst the 214 drugs identified with orphan drug designation (25.7% biologics), 83.6% were approved through at least one expedited programme, and 29.4% were approved with boxed warnings. During a median follow-up of 6.74 years since approval, 69.2% and 14.5% of the analysed orphan drugs had PMSE and SPSE, respectively. Safety-related withdrawal (0%, 0/214), suspended marketing (0.46%, 1/214) and new boxed warnings are uncommon (3.7%, 8/214). The safety-related labelling changes were more frequent in the drugs approved with boxed warnings [Incidence rate ratio (IRR): 1.95 (1.02–3.73)] and approved for long-term use [IRR: 2.76 (1.52–5.00)].

Conclusions and Relevance

In this long-term postmarketing analysis, approximately 70% of FDA-approved orphan drugs had safety-related labelling changes although severe safety events were rare. While maintaining early access to orphan drugs, the drug regulatory body has taken timely regulatory action with postmarketing surveillance to ensure patient safety.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-02166-9.

"Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time?

There is great interest in the application of 'artificial intelligence' (AI) to pharmacovigilance (PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application of AI to the processing and evaluation of Individual Case Safety Reports (ICSRs) submitted to the FDA Adverse Event Reporting System (FAERS). We describe a general framework for considering the readiness of AI for PV, followed by some examples of the application of AI to ICSR processing and evaluation in industry and FDA. We conclude that AI can usefully be applied to some aspects of ICSR processing and evaluation, but the performance of current AI algorithms requires a 'human-in-the-loop' to ensure good quality. We identify outstanding scientific and policy issues to be addressed before the full potential of AI can be exploited for ICSR processing and evaluation, including approaches to quality assurance of 'human-in-the-loop' AI systems, large-scale, publicly available training datasets, a well-defined and computable 'cognitive framework', a formal sociotechnical framework for applying AI to PV, and development of best practices for applying AI to PV. Practical experience with stepwise implementation of AI for ICSR processing and evaluation will likely provide important lessons that will inform the necessary policy and regulatory framework to facilitate widespread adoption and provide a foundation for further development of AI approaches to other aspects of PV.

The Use of Real-World Data to Assess the Impact of Safety-Related Regulatory Interventions

The regulation of medicines seeks to ensure the efficacy, safety, and quality of prescription and non-prescription medicines. Given that the conditions under which a medicine's benefits outweigh its risks are complex, it is essential that communications about the safe and effective use of medicines be clear and actionable. Assessing the impact of interventions to improve the safe and effective use of medicines is a developing area, and one in which real-world data are playing an increasingly important role. Although real-world data are commonly used to assess the impact of regulatory interventions, there are several areas where their use could be improved. Specific areas for improvement include assessing regulatory interventions across a wider range of medicines, rather than concentrating on a relatively few therapeutic areas; assessing more clinically relevant outcomes rather than relying on measures such as changes in the number of prescriptions, which may not always correlate with the desired impact; assessing the potential unintended or negative consequences of regulatory interventions; applying methods to address potential confounders; assessing long-term, rather than just short-term, impacts of an intervention; increasing the use of comparator groups, when feasible; and evaluating the impact of regulatory interventions from multiple dimensions, rather than from a single dimension. Expanded use of real-world data could inform some of these efforts, although data sources beyond administrative claims data will likely be necessary to achieve all these goals.

Comprehensive evaluation of post-approval regulatory actions during the drug lifecycle - a focus on benefits and risks

Background: Prior studies investigated regulatory actions that reflected a negative impact on drug risks. We aimed to evaluate occurrence of regulatory actions that reflected a negative or positive impact on benefits or risks, as well as relations between them.Research design and methods: We followed EMA-approved innovative drugs from approval (2009-2010) until July 2020 or withdrawal to identify regulatory actions. We assessed these for impact on benefits or risks and relations between actions. Additionally, we scrutinized drug lifecycles for time-variant characteristics that may contribute to specific patterns of regulatory actions.Results: We identified 14 letters and 361 label updates for 40 drugs. Of the label updates, 85 (24%) reflected a positive impact, mostly concerning indications, and 276 (76%) a negative impact, mostly adverse drug reactions. Many updates (54%) occurred simultaneously with other updates, also if these reflected a different impact. Furthermore, levels of patient exposure, innovativeness, needs for regulatory learning and unexpected risks may contribute to patterns of regulatory actions.Conclusions: Almost a quarter of regulatory actions reflected a positive impact on benefits and risks. Also, simultaneous learning about benefits and risks suggests an important role for drug development in risk characterization. These findings may impact regulatory analyses and decision-making.

The Impact of Variability in Patient Exposure During Premarket Clinical Development on Postmarket Safety Outcomes

We characterized the size of the premarket safety population for 278 small-molecule new molecular entities (NMEs) and 61 new therapeutic biologics (NTBs) approved by the US Food and Drug Administration (FDA) between October 1, 2002, and December 31, 2014, evaluating the relationship of premarket safety population size to regulatory characteristics and postmarket safety outcomes. The median size of the safety population was 1,044, and was lower for NTBs than NMEs (median: 920 vs. 1,138, P = 0.04), orphan products than nonorphan products (393 vs. 1,606, P < 0.001), and for products with fast-track designation (617 vs. 1,455, P < 0.001), priority review (630 vs. 1,735, P < 0.001), and accelerated approval (475 vs. 1,164, P < 0.001), than products without that designation. The median number of postmarket safety label updates and issues added to the label were higher with larger premarket exposure among nonorphan products, but not among orphan products. Products with accelerated approval using a surrogate end point had a higher median number of safety issues added to the label than those with full approval, but this did not vary with the size of the safety population; fast-track and priority review were not associated with the number of safety issues added to the label. A smaller safety population size was associated with a longer time to first safety outcome for nonorphan products but not orphan products. For orphan and nonorphan products combined, smaller premarket safety population size is not associated with the number or timing of postmarket safety outcomes, regardless of expedited program participation.