Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance

Signal mining and gender differences analysis of adverse events in NMIBC treatment with gemcitabine and BCG bladder instillation based on the FAERS database

OBJECTIVE

To explore safety differences and perform a gender-based analysis of adverse events related to gemcitabine and Bacillus Calmette-Guérin (BCG) vaccine using the U.S. FDA Adverse Event Reporting System (FAERS) database.

METHODS

Using the Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR) methods, adverse events associated with gemcitabine and BCG were mined from FAERS database reports spanning from Q1 2004 to Q3 2023.

RESULTS

The study extracted 37,855 reports with gemcitabine and 5,455 reports with BCG as the primary suspected drugs. Adverse events were more prevalent in males (male-to-female ratio: gemcitabine 1.10, BCG 4.25). Differences in high-frequency adverse events among the top 20 signals were detected for both drugs. Both drugs affected similar organ systems, including potential pulmonary, ocular, and renal toxicity, with gemcitabine showing a broader range of adverse events. Gender analysis revealed fewer adverse reactions to gemcitabine in females, while males had fewer adverse reactions to BCG.

CONCLUSION

Differences in high-frequency adverse events between gemcitabine and BCG, including some not listed on drug labels, were observed. Both drugs affect similar organ systems, with gemcitabine showing a broader range of adverse events. Gender differences in adverse events were notable.

Development of a Framework Structuring Themes in the Course of Adverse Drug Reactions from a Patient's Perspective

INTRODUCTION

There is a need for more extensive information about adverse drug reactions (ADRs) for patients than currently available, including information on the course of ADRs. Aspects characterising the course of ADRs from the patient perspective have not been identified before.

OBJECTIVE

We aimed to develop a framework based on common themes in the course of ADRs identified from patient descriptions in patient-reported ADRs.

METHODS

In this qualitative study, patient descriptions of the course of patient-reported ADRs were analysed by a thematic analysis with an inductive approach using three different existing datasets containing patient-reported ADRs. Two datasets included patient-reported ADRs from cohort event monitoring of biologics and direct oral anticoagulants and one dataset included spontaneous reports from patients concerning medication for lower urinary tract symptoms. A conceptual framework was developed from the identified main themes and subthemes.

RESULTS

Patient-reported data concerning 3888 ADRs were analysed. Six main themes with multiple subthemes were identified from patient descriptions of the course of ADRs. Four themes were descriptive: frequency of an ADR episode, duration of an ADR episode, moment or period of ADR occurrence, and development in the intensity of the ADR. Two themes concerned factors influencing the course of ADRs: triggering factors and improving factors.

CONCLUSIONS

The presented framework illustrates that patients describe extensive details on the course and timeframe of ADRs. The identified themes provide a basis for improving the systematic data collection of more extensive details about ADRs from patients as a first step towards the provision of more comprehensive ADR information to patients.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review

INTRODUCTION

Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance.

METHODS

To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices.

RESULTS

We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations.

CONCLUSION

Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk

INTRODUCTION

Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit-risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking.

OBJECTIVES

In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk.

METHODS

The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included.

RESULTS

Twenty-seven PRAC subgroup examples representing 1719 subgroup drug-event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected.

CONCLUSIONS

We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

Evaluation of uveitis events in real-world patients receiving immune checkpoint inhibitors based on the FAERS database

PURPOSE

Immune checkpoint inhibitors (ICIs) have emerged as a novel class of drugs carrying a potential risk of uveitis. Due to the rarity, current knowledge on this safety issue is still incomplete. This study employed the post-marketing surveillance data to comprehensively describe and assess the uveitis events after the use of ICIs.

METHODS

Data between 2004 and 2021 were downloaded from the Food and Drug Administration Adverse Event Reporting System (FAERS), and the uveitis events reported for ICIs were identified and included in this study. Clinical details of these reports were collected and analyzed. Four data mining methods were utilized to investigate the potential associations between uveitis and different ICI regimens.

RESULTS

Overall, 461 uveitis cases after exposure to ICI therapies were reported. Melanoma (58.79%) was revealed as the most common indication for receiving ICIs. The median onset time of uveitis was 41 (interquartile range 18-91) days after ICI initiation. 9.54% of these cases resulted in disability. Data mining results showed 5 ICIs generated positive uveitis signals when used alone. Ipilimumab yielded the most noticeable uveitis signal with the highest reporting odds ratio (ROR = 6.73, 95% two-sided CI = 5.26, 8.60), proportional reporting ratio (PRR = 6.69, χ²=308.52), information component (IC = 2.74, IC025 = 2.14) and empirical Bayes geometric mean (EBGM = 6.66, EBGM05 = 5.42), followed by pembrolizumab, cemiplimab, nivolumab and atezolizumab. When nivolumab, pembrolizumab or atezolizumab was administrated together with ipilimumab, obviously stronger uveitis signal was detected than that for either of them.

CONCLUSIONS

This study provided an overview of the clinical features of ICI-related uveitis cases in the FAERS. Data mining results revealed that positive uveitis signals commonly existed within this drug class, but signal strength varied among ICIs. When ICIs were used in a combined way, uveitis signals became obviously stronger. Therefore, early ophthalmic monitoring is important when applying ICIs to patients, especially those with a tendency for uveitis, such as melanoma patients.

Digital biomarkers for post-licensure safety monitoring

Post-licensure safety data form the cornerstone of safety surveillance. However, such data have some limitations related to the subjectiveness of reporting and recording, primary purpose of the collected data, or heterogeneity. Routine capture of richer data would in part help mitigate these limitations, enabling earlier, more reliable safety insights. Digital health tools that remotely acquire health-related information are increasingly available and used by patients and the wider population. However, they are rarely used for pharmacovigilance purposes. Here, we review different cases that reveal the opportunities and challenges of using these technologies for enhanced safety assessment in routine healthcare delivery. We believe such approaches will advance our understanding of the safety of drugs and vaccines in the future.

Pharmacovigilance: reporting requirements throughout a product's lifecycle

Comprehensive methods for evaluating safety are needed to objectively assess the full risk profile of a medication. The confidence of the prescribing provider in the safety and effectiveness of pharmaceuticals is extremely important. Pharmacovigilance is a key component of drug safety regulatory processes and is paramount for ensuring the safety profile of medications used to treat patients. All participants in the healthcare system, including healthcare providers and consumers, should understand and meaningfully engage in the pharmacovigilance process; healthcare providers should integrate pharmacovigilance into everyday practice, inviting feedback from patients. This narrative review aims to give an overview of the main topics underlying pharmacovigilance and drug safety in pharmaceutical research phase after the authorization of a drug in the United States. The US Food and Drug Administration guidance and post-approval regulatory actions are considered from an industry perspective.

Plain language summary

Regulatory processes that ensure the safety of drugs is monitored Government agencies regulate the safe use of medicinal products. By determining and enforcing pharmacovigilance, the monitoring of drugs for potential risks, they safeguard the welfare of consumers of medicines. Comprehensive, documented methods for evaluating the safety of a drug during its development and its subsequent use allow identification of any risks associated with the drug's use throughout its lifetime. The comprehensive identification of safety issues associated with a drug is improved when all parties involved in the development and use of drugs participate in the pharmacovigilance process. For example, clinicians should regularly ask their patients if they are experiencing any issues with their treatment, and patients should be encouraged to report problems they encounter with a particular medication to their healthcare provider. This narrative review provides an overview of the main topics underlying pharmacovigilance and drug safety after approval of a drug in the United States. Guidelines and actions from the US Food and Drug Administration are considered from an industry perspective.

Comparing the Value of Data Visualization Methods for Communicating Harms in Clinical Trials

In clinical trials, harms (i.e., adverse events) are often reported by simply counting the number of people who experienced each event. Reporting only frequencies ignores other dimensions of the data that are important for stakeholders, including severity, seriousness, rate (recurrence), timing, and groups of related harms. Additionally, application of selection criteria to harms prevents most from being reported. Visualization of data could improve communication of multidimensional data. We replicated and compared the characteristics of 6 different approaches for visualizing harms: dot plot, stacked bar chart, volcano plot, heat map, treemap, and tendril plot. We considered binary events using individual participant data from a randomized trial of gabapentin for neuropathic pain. We assessed their value using a heuristic approach and a group of content experts. We produced all figures using R and share the open-source code on GitHub. Most original visualizations propose presenting individual harms (e.g., dizziness, somnolence) alone or alongside higher level (e.g., by body systems) summaries of harms, although they could be applied at either level. Visualizations can present different dimensions of all harms observed in trials. Except for the tendril plot, all other plots do not require individual participant data. The dot plot and volcano plot are favored as visualization approaches to present an overall summary of harms data. Our value assessment found the dot plot and volcano plot were favored by content experts. Using visualizations to report harms could improve communication. Trialists can use our provided code to easily implement these approaches.

Exploring a Safety Signal of Antipsychotic-Associated Pneumonia: A Pharmacovigilance-Pharmacodynamic Study

An association between antipsychotic drugs and pneumonia has been demonstrated in several studies; however, the risk for pneumonia caused by specific antipsychotics has not been extensively studied. The underlying mechanism is still unknown, and several receptor mechanisms have been proposed. Therefore, using a combined pharmacovigilance-pharmacodynamic approach, we aimed to investigate safety signals of US Food and Drug Administration (FDA)-approved antipsychotics for reporting pneumonia and the potential receptor mechanisms involved. A disproportionality analysis was performed to detect a signal for reporting "infective-pneumonia" and "pneumonia-aspiration" and antipsychotics using reports submitted between 2004 and 2019 to the FDA adverse events spontaneous reporting system (FAERS) database. Disproportionality was estimated using the crude and the adjusted reporting odds ratio (aROR) and its 95% confidence interval (CI) in a multivariable logistic regression. Linear regressions investigated the relationship between aROR and receptor occupancy, which was estimated using in vitro receptor-binding profiles. Safety signals for reporting infective-pneumonia were identified for clozapine (LL = 95% 3.4, n = 546 [aROR: 4.8]) as well as olanzapine (LL = 95% 1.5, n = 250 [aROR: 2.1]) compared with haloperidol, while aRORs were associated with higher occupancies of muscarinic receptors (beta = .125, P-value = .016), yet other anti-muscarinic drugs were not included as potential confounders. No safety signals for reporting pneumonia-aspiration were detected for individual antipsychotics. Multiple antipsychotic use was associated with both reporting infective-pneumonia (LL 95%: 1.1, n = 369 [aROR:1.2]) and pneumonia-aspiration (LL 95%: 1.7, n = 194 [aROR: 2.0]). Considering the limitations of disproportionality analysis, further pharmacovigilance data and clinical causality assessment are needed to validate this safety signal.

Immune-related adverse events associated with immune checkpoint inhibitors: An updated comprehensive disproportionality analysis of the FDA adverse event reporting system

BACKGROUNDS

Immune-related adverse events were reported in patients treated with immune checkpoint inhibitors (ICIs). However, with the increasing number of immune-related adverse events (irAEs), the differences of each immune checkpoint inhibitor regimen had not been fully assessed.

METHODS

Disproportionality analysis was used in data mining of the suspected adverse events after ICIs administration based on the Food and Drug Administration Adverse Event Reporting System (FAERS) from January 2004 to December 2019. The onset time and fatality proportion of ICI-associated irAEs were further evaluated.

RESULTS

A total of 32,441 reports of ICI-associated irAEs were gathered. This study showed that all ICI regimens generated lung toxicity and endocrine toxicity signals. Colitis, pneumonitis and interstitial lung disease were the most common ICI-associated irAEs. Five regimens including durvalumab monotherapy, ipilimumab monotherapy, ipilimumab plus nivolumab, ipilimumab plus pembrolizumab, durvalumab plus tremelimumab were associated with irAEs. Anti-PD-1 agents generated more signals of ocular toxicities than anti-PD-L1 agents, while anti-PD-L1 agents reported more signals of hematologic toxicities. Anti-CTLA-4 agents showed more signals of gastrointestinal toxicities compared with anti-PD-1 or anti-PD-L1 agents. The highest fatality proportion of lung toxicities with durvalumab monotherapy, hematological toxicities with avelumab monotherapy, renal and skin toxicities with cemiplimab monotherapy were found.

CONCLUSION

Our results demonstrated that each ICI regimen had different characteristics of irAEs. Pembrolizumab had the highest fatality proportion. Ipilimumab plus pembrolizumab had the shortest median time to onset irAEs. Further studies were expected to assess whether there were clinically relevant differences exist among ICIs.

Safety of antipsychotic drugs: A systematic review of disproportionality analysis studies

Antipsychotic drugs are commonly prescribed, mainly for the treatment of schizophrenia and other psychotic disorders. Disproportionality analysis of pharmacovigilance data from national and international databases have been recently utilized to investigate the side-effect profiles of antipsychotics and have provided unique insights of their safety. Among several national and international spontaneous reporting databases the databases of the World Health Organization (VigiBase), of the European Medicines Agency (EudraVigilance) and the US Food and Drug Administration (FAERS) incorporate millions of Individual Case Safety Reports. The aim of our study was to systematically review published disproportionality analyses on antipsychotic drugs, in order to summarize the current state of methodology and potential strengths of this analysis while highlighting safety signal generated for these pharmacological group. PubMed was searched using a search algorithm combining terms for antipsychotic drugs and disproportionality analysis. A total of 39 articles were found to be eligible corresponding to 38 original disproportionality studies. Different measures of disproportionality were used in each study: reporting odds ratio (ROR), proportional reporting ratio (PRR), empirical Bayes geometric mean (EBGM) and the information component (IC). Despite the inherent limitations of the pharmacovigilance databases disproportionality analysis provides complemented evidence from RCTs on the safety of antipsychotics, especially regarding participants often excluded from RCTs, such as pregnant and breastfeeding women, children and participants with drug abuse, comorbidities or concomitant medications.

Medical device active surveillance of spontaneous reports: A literature review of signal detection methods

PURPOSE

The collection and analysis of real-world data for the active monitoring of medical device performance and safety has become increasingly important. Spontaneous reports, such as those in the Food & Drug Administration's (FDA's) Manufacturer and User Facility Device Experience (MAUDE), provide early warning of potential issues with marketed devices. This review synthesizes the current literature on medical device surveillance signal detection and provides a framework for application of methods to active surveillance of spontaneous reports.

METHODS

Ovid MEDLINE, Ovid Embase, Scopus, and PubMed databases were systematically searched up to January 2019. Additionally, five methods articles from pharmacovigilance were added that had potential applications to medical devices.

RESULTS

Among 105 articles included, the most common source of data (84%) was registries; median time between data collection and publication was 8 years. Surgical procedure outcome signal detection articles comprised 83% while 14% were on device outcome signal detection. The most common family of methods cited (70%) was Sequential Probability Ratio.

CONCLUSION

Application of any signal detection algorithm requires careful consideration of influential factors, data limitations, and algorithmic assumptions. We recommend approaches using disproportionality, statistical process control, and sequential probability tests and provide R packages to further development efforts. The small number of published examples suggest that further development of statistical methods and technological solutions to analyze large amounts of data for device safety and performance is needed. Fundamental differences in products, data infrastructure, and the regulatory landscape suggest that medical device vigilance requires its own body of research distinct from pharmacovigilance.

Uses of pharmacovigilance databases: An overview

Over the past decades, assessment of drug safety and of their benefits harms balance has been profoundly modified by the availability of large databases and computerized automated statistical approaches. Improvement of digital data storage capacity has been applied to pharmacovigilance reports. VigiBase, the international pharmacovigilance database, is now aggregating over 21 million individual case safety reports in 2020. Identification and investigation of drug safety signals - concerning notably rare and unknown adverse drug reactions - is one of the major tasks in pharmacovigilance that can be amplified by automated signal detection. Several quantitative statistical methods exist, each with its own strengths and limits. Integrating signal detection, pharmacovigilance databases can be used for a wide variety of retrospective observational studies illustrated here by concrete examples. Confirming these signals by orthogonal validation using pre-clinical platforms and prospective trials is helpful. Pharmacovigilance databases represent a considerable source of information. However, the quality of signal detection and of pharmacoepidemiology studies in the field of adverse drug reaction closely depends on the quality of the individual data recorded.

An evaluation of statistical approaches to postmarketing surveillance

Safety of medical products presents a serious concern worldwide. Surveillance systems of postmarket medical products have been established for continual monitoring of adverse events (AEs) in many countries, and the proliferation of electronic health record systems further facilitates continual monitoring for AEs. We review existing statistical methods for signal detection that are mostly in use in postmarketing safety surveillance of spontaneously reported AEs and we study their performance characteristics by simulation. We compare those with the likelihood ratio test (LRT) method (appropriately modified for use in pharmacovigilance) and use three different methods to generate data (AE based, drug based, and a modification of the method of Ahmed et al). Performance metrics include type I error, power, sensitivity, and false discovery rate, among others. The results show superior performance of the LRT method in almost all simulation experiments. An application to the FDA Adverse Event Reporting System database is illustrated using rhabdomyolysis-related preferred terms reported to FDA during the third-quarter of 2014 to the first-quarter of 2017 for statin drugs. We present a critical discussion and recommendations for use of these methods.

Interdisciplinary Safety Evaluation and Quantitative Safety Monitoring: Introduction to a Series of Papers

The American Statistical Association and DIA have created an interdisciplinary working group of drug safety experts from academia, industry and regulatory backgrounds to explore the future direction for safety monitoring. This introduction to the series explains the background and rationale for this special section.

Sources of Safety Data and Statistical Strategies for Design and Analysis: Transforming Data Into Evidence

BACKGROUND

Safety evaluation is a key aspect of medical product development. It is a continual and iterative process requiring thorough thinking, and dedicated time and resources.

METHODS

In this article, we discuss how safety data are transformed into evidence to establish and refine the safety profile of a medical product, and how the focus of safety evaluation, data sources, and statistical methods change throughout a medical product's life cycle.

RESULTS

Some challenges and statistical strategies for medical product safety evaluation are discussed. Examples of safety issues identified in different periods, that is, premarketing and postmarketing, are discussed to illustrate how different sources are used in the safety signal identification and the iterative process of safety assessment. The examples highlighted range from commonly used pediatric vaccine given to healthy children to medical products primarily used to treat a medical condition in adults. These case studies illustrate that different products may require different approaches, and once a signal is discovered, it could impact future safety assessments.

CONCLUSIONS

Many challenges still remain in this area despite advances in methodologies, infrastructure, public awareness, international harmonization, and regulatory enforcement. Innovations in safety assessment methodologies are pressing in order to make the medical product development process more efficient and effective, and the assessment of medical product marketing approval more streamlined and structured. Health care payers, providers, and patients may have different perspectives when weighing in on clinical, financial and personal needs when therapies are being evaluated.