When to publish measures of disproportionality derived from spontaneous reporting databases?

The reporting of disproportionality analysis in pharmacovigilance: spotlight on the READUS-PV guideline

Disproportionality analyses are the most-commonly used study design used in the post-marketing phase to detect suspected adverse drug reactions in individual case safety reports. Recent years have witnessed an exponential increase in published articles on disproportionality analyses, thanks to publicly accessible databases. Unfortunately, this trend was accompanied by concerns on lack of transparency and misinterpretation of results, both generating unjustified alarm and diluting true signals into overwhelming noise. The READUS-PV guideline for reporting disproportionality analysis was developed to tackle this emerging issue. In this perspective article, we describe the rationale behind the development of the READUS-PV guideline, the first collaborative initiative to harmonize the reporting of disproportionality analyses. The adoption of the checklists will assist researchers, regulators, and reviewers in the reporting, assessment, and publication of disproportionality analyses. Acknowledging the challenges ahead of effective implementation, we advocate for a global endorsement by Pharmacology Journals. A wide dissemination of the READUS-PV guideline is crucial to foster transparency and reproducibility of pharmacovigilance research, supporting an effective exploitation of disproportionality analysis among other irreplaceable post-marketing research tools to ensure drug safety.

Compartment syndrome associations with drugs: a pharmacovigilance study of the FDA adverse event reporting system (FAERS)

BACKGROUND

Compartment syndrome is an uncommon but life-threatening condition. No study has comprehensively compared compartment syndrome (CS) association with available drugs. The objective of this study was to estimate the association between CS and drugs using the FDA Adverse Event Report System (FAERS).

RESEARCH DESIGN AND METHODS

FAERS reports from the first quarter of 2004 to the third quarter of 2023 were analyzed. The Medical Dictionary for Regulatory Activities (MedDRA) was used to identify CS cases. Reporting odds ratio (ROR), corresponding to 95% confidence intervals (95% CI) were calculated to detect a positive signal.

RESULTS

A total of 2197 reports were considered in the study after the inclusion criteria were applied. Totally 100 drugs were found to be associated with CS. The median time for drug-associated CS was 45 days.

CONCLUSIONS

By analyzing the FAERS database, the study revealed that certain drugs are significantly associated with compartment syndrome. Further studies are needed to verify whether these drugs are associated with such a risk.

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance.

Neuropsychiatric side reactions of leukotriene receptor antagonist, antihistamine, and inhaled corticosteroid: A real-world analysis of the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS)

BACKGROUND

There are limited real-world studies on the differences in leukotriene receptor antagonists (LTRA), H1-antihistamines (H1-AH), and inhaled corticosteroids (ICS) associated neuropsychiatric events. In this study, we aimed to analyze the characteristics of drug associated neuropsychiatric events, and compare the differences among different drug categories.

METHODS

Disproportionality analysis and Bayesian analysis were used in data mining to identify suspected neuropsychiatric events associated with LTRA, H1-AH, and ICS based on the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from January 2004 to September 2020. Demographic information, time interval to onset, and death rates of LTRA, H1-AH, and ICS-associated neuropsychiatric events were also analyzed.

RESULTS

A total of 9475 neuropsychiatric events were identified. The number of neuropsychiatric events related to LTRA, H1-AH, and ICS were 5201 (54.89%), 3226 (34.05%), and 1048 (11.06%), respectively. LTRA related neuropsychiatric events were more common in patients aged 4-6 years (18.66%). H1-AH and ICS related neuropsychiatric events were more common in patients aged 18-44 years (29.92%) and older than 65 years (30.60%), respectively. Montelukast was highly associated with neuropsychiatric events, with a high reporting odds ratio (ROR). Most neuropsychiatric symptoms occurred within the first 10 days after drug initiation (78.63% for LTRA, 91.39% for H1-AH, and 84.07% for ICS). The death rate due to neuropsychiatric events of first generation H1-AH was significantly higher than that of LTRA and ICS (p < 0.001).

CONCLUSIONS

LTRA associated neuropsychiatric events reported in FAERS were most frequent in 4 to 6-year-old children. Most reported cases occurred within the first 10 days after drug initiation. The second generation H1-AH was relatively safe for neuropsychiatric events compared with the first generation. The fatality rate due to first generation H1-AH associated neuropsychiatric events was higher than that of LTRA and ICS. More attention should be paid to specific patients treated with LTRA and H1-AH.

Lessons to be Learnt from Real-World Studies on Immune-Related Adverse Events with Checkpoint Inhibitors: A Clinical Perspective from Pharmacovigilance

The advent of immune checkpoint inhibitors (ICIs) caused a paradigm shift both in drug development and clinical practice; however, by virtue of their mechanism of action, the excessively activated immune system results in a multitude of off-target toxicities, the so-called immune-related adverse events (irAEs), requiring new skills for timely diagnosis and a multidisciplinary approach to successfully manage the patients. In the recent past, a plethora of large-scale pharmacovigilance analyses have characterized various irAEs in terms of spectrum and clinical features in the real world. This review aims to summarize and critically appraise the current landscape of pharmacovigilance studies, thus deriving take-home messages for oncologists. A brief primer to study design, conduction, and data interpretation is also offered. As of February 2020, 30 real-world postmarketing studies have characterized multiple irAEs through international spontaneous reporting systems, namely WHO Vigibase and the US FDA Adverse Event Reporting System. The majority of studies investigated a single irAE and provided new epidemiological evidence about class-specific patterns of irAEs (i.e. anti-cytotoxic T-lymphocyte antigen 4 [CTLA-4] versus anti-programmed cell death 1 [PD-1] receptor, and its ligand [PD-L1]), kinetics of appearance, co-occurrences (overlap) among irAEs, and fatality rate. Oncologists should be aware of both strengths and limitations of these pharmacovigilance analyses, especially in terms of data interpretation. Optimal management (including rechallenge), predictivity of irAEs (as potential biomarkers of effectiveness), and comparative safety of ICIs (also in terms of combination regimens) represent key research priorities for next-generation real-world studies.

Gabapentin drug misuse signals: A pharmacovigilance assessment using the FDA adverse event reporting system

BACKGROUND

Although there have been increasing reports of intentional gabapentin misuse, epidemiological evidence for the phenomenon is limited. The purpose of this study was to determine whether there are pharmacovigilance abuse signals for gabapentin.

METHODS

Using FDA Adverse Events Reporting System reports from January 1, 2005 to December 31, 2015, we calculated pharmacovigilance signal measures (i.e., reporting odds ratio, proportional reporting ratio, information component, and empirical Bayes geometric mean) for abuse-related adverse event (AR-AE)-gabapentin pairs. Loglinear modeling assessed the frequency of concurrent reporting of abuse-related and abuse-specific AEs (AS-AEs) associated with gabapentin. Findings were compared to a positive (pregabalin) and negative (duloxetine) control.

RESULTS

From 2005-2015 there were 5,951,229 unique AE reports submitted to the FDA including 99,977 for gabapentin, 73,977 for duloxetine, and 97,813 for pregabalin. Significant drug-AR-AE pair signals involving gabapentin included: drug abuser, multiple drug overdose, and substance-induced psychotic disorder. Significant drug AR-AE signals involving gabapentin and pregabalin, but not duloxetine, were: ataxia, dependence, drug abuse, increased drug tolerance, and overdose. Compared to duloxetine, gabapentin had significantly greater odds of a co-report for an AS-AE with drug withdrawal syndrome (OR: 6.55), auditory hallucinations (OR: 4.57), delusions (OR: 2.36), euphoric mood (OR: 5.45), ataxia (OR: 2.85), drug abuser (OR: 3.01), aggression (OR: 1.98), psychotic disorder (OR: 1.96), and feeling abnormal (OR: 1.31).

CONCLUSIONS

We identified abuse-related signals for gabapentin and highlighted several CNS effects that may be associated with its abuse. Gabapentin prescribers should be aware of the drug's abuse liability and effects that may accompany its use.

Adverse event profiles of solvent-based and nanoparticle albumin-bound paclitaxel formulations using the Food and Drug Administration Adverse Event Reporting System

Objectives

Paclitaxel is a highly effective antitumor agent with notable adverse events, including hypersensitivity reactions, peripheral neuropathy, arthralgia, myalgias, and neutropenia. Solvent-based paclitaxel causes severe allergic, hypersensitivity, and anaphylactic reactions. Nanoparticle albumin-bound paclitaxel was recently developed and provides an advantage over solvent-based paclitaxel in avoiding solvent/surfactant-related adverse events. The aim of this study was to assess the adverse event profiles of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel formulations using data from the spontaneous adverse event reporting system of the US Food and Drug Administration Adverse Event Reporting System database.

Methods

This study relied on Medical Dictionary for Regulatory Activities preferred terms and standardized queries, and calculated the reporting ratio and reporting odds ratios of paclitaxel formulations.

Results

Of 8,867,135 reports recorded in the US Food and Drug Administration Adverse Event Reporting System database from January 2004 to December 2016, 3469 and 4447 adverse events corresponded to solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel, respectively. Reporting odds ratios (95% confidence interval) for anaphylactic reaction (standardized MedDRA query code: 20000021) associated with the use of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel were 1.69 (1.56-1.84) and 0.75 (0.68-0.83), respectively. Reporting odds ratio signal for anaphylactic reaction was not detected for nanoparticle albumin-bound paclitaxel. Reporting odds ratios (95% confidence interval) for acute renal failure (standardized MedDRA query code: 20000003) associated with the use of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel were 0.75 (0.58-0.98) and 1.60 (1.37-1.89), respectively.

Conclusion

This is the first study to evaluate the adverse event profile of nanoparticle albumin-bound paclitaxel using US Food and Drug Administration Adverse Event Reporting System data. Considering that the US Food and Drug Administration Adverse Event Reporting System database does not allow to infer causality or risk ranking, the different reporting frequencies of anaphylactic reaction and acute renal failure between solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel must be further investigated via analytical observational research.

Pharmacovigilance of sodium-glucose co-transporter-2 inhibitors: What a clinician should know on disproportionality analysis of spontaneous reporting systems

Sodium-glucose co-transporter-2 inhibitors (SGLT2-Is) have consistently demonstrated a clinically significant reduction of cardiovascular mortality. However, their safety in clinical practice is still incompletely characterized, and post-marketing monitoring is required considering the expected increase in clinical use. Different analyses of international spontaneous reporting systems, known as disproportionality analyses (DAs), have highlighted the occurrence of ketoacidosis, amputations, acute renal failure and skin toxicity. In this viewpoint, we critically appraise these pharmacovigilance data on SGLT2-Is, with the aim of supporting clinicians in proper interpretation of these studies, and discussing their risk-benefit profile. To this aim, we offer a broad perspective on basic technical aspects subtending DAs of spontaneous reporting databases (describing peculiarities of the Food and Drug Administration Adverse Event Reporting System), their common and evolving uses, key pitfalls in presenting study results (in terms of "risk" or "association") and relevant strategies to account for major confounders. This will also facilitate reviewers and editors in proper evaluation of DAs, and prompt pharmacovigilance experts in converging towards a set of minimum requirements in standardization of design, performance and reporting of DAs. A consensus on quality assessment of DAs will finally establish their transferability to clinical practice. It is anticipated that DAs cannot be used per se as a standalone approach to assess a drug-related risk and cannot replace clinical judgment in the individual patient.

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

BACKGROUND

Safety data are continuously evaluated throughout the life cycle of a medical product to accurately assess and characterize the risks associated with the product. The knowledge about a medical product's safety profile continually evolves as safety data accumulate.

METHODS

This paper discusses data sources and analysis considerations for safety signal detection after a medical product is approved for marketing. This manuscript is the second in a series of papers from the American Statistical Association Biopharmaceutical Section Safety Working Group.

RESULTS

We share our recommendations for the statistical and graphical methodologies necessary to appropriately analyze, report, and interpret safety outcomes, and we discuss the advantages and disadvantages of safety data obtained from passive postmarketing surveillance systems compared to other sources.

CONCLUSIONS

Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.

Psychiatric disorders, acne and systemic retinoids: comparison of risks

BACKGROUND

The link between isotretinoin, treatment of a severe form of acne, and psychiatric disorders remains controversial, as acne itself could explain the occurrence of psychiatric disorders. This study aims at assessing the disproportionality of psychiatric adverse events reported with isotretinoin in the French National PharmacoVigilance Database, compared with other systemic acne treatments and systemic retinoids.

MATERIALS AND METHODS

Data were extracted from the French National PharmacoVigilance Database for systemic acne treatments, systemic retinoids and drugs used as comparators. Each report was subjected to double-blind analysis by two psychiatric experts. A disproportionality analysis was performed, calculating the number of psychiatric ADRs divided by the total number of notifications for each drug of interest.

RESULTS

Concerning acne systemic treatments: all 71 reports of severe psychiatric disorders involved isotretinoin, the highest proportion of mild/moderate psychiatric adverse events was reported with isotretinoin (14.1%). Among systemic retinoids, the highest proportion of severe and mild/moderate psychiatric events occurred with isotretinoin and alitretinoin.

CONCLUSION

Our study raises the hypothesis that psychiatric disorders associated with isotretinoin are related to a class effect of retinoids, as a signal emerges for alitretinoin. Complementary studies are necessary to estimate the risk and further determine at-risk populations.

Can Disproportionality Analysis of Post-marketing Case Reports be Used for Comparison of Drug Safety Profiles?

Clinical trials usually do not have the power to detect rare adverse drug reactions. Spontaneous adverse reaction reports as for example available in post-marketing safety databases such as the FDA Adverse Event Reporting System (FAERS) are therefore a valuable source of information to detect new safety signals early. To screen such large data-volumes for safety signals, data-mining algorithms based on the concept of disproportionality have been developed. Because disproportionality analysis is based on spontaneous reports submitted for a large number of drugs and adverse event types, one might consider using these data to compare safety profiles across drugs. In fact, recent publications have promoted this practice, claiming to provide guidance on treatment decisions to healthcare decision makers. In this article we investigate the validity of this approach. We argue that disproportionality cannot be used for comparative drug safety analysis beyond basic hypothesis generation because measures of disproportionality are: (1) missing the incidence denominators, (2) subject to severe reporting bias, and (3) not adjusted for confounding. Hypotheses generated by disproportionality analyses must be investigated by more robust methods before they can be allowed to influence clinical decisions.

Drug-induced progressive multifocal leukoencephalopathy: a case/noncase study in the French pharmacovigilance database

Progressive multifocal leukoencephalopathy (PML) is an often fatal demyelinating disease of the central nervous system. As effective treatment is unavailable, identification of all drugs that could be associated with PML is essential. The objective of this study was to investigate the putative association of reports of PML and drugs. We used the case/noncase method in the French PharmacoVigilance database (FPVD). Cases were reports of PML in the FPVD between January 2008 and December 2015. Noncases were all other reports during the same period. To assess the association between PML and drug intake, we calculated an adverse drug report odds ratio (ROR) with its 95% confidence interval. We have studied the delay of onset of PML for each drug concerned. Among the 101 cases of PML, 39 drugs were mentioned as suspect. The main therapeutic classes suspected with significant ROR were antineoplastic agents (n = 85), immunosuppressants (n = 67), and corticosteroids. A latent interval from the time of drug initiation to the development of PML is established: the median time to onset was 365 days (123-1095 days). The onset of PML is highly variable and differs among drug classes [from 1 to 96 months (IQR: 39.0-126)]. An association between PML and some immunosuppressant drugs was found as expected, but also with antineoplastic agents and glucocorticoids. An important delay of PML onset after stopping treatment is suspected and should alert prescribers. Prescribers but also patients should be informed about the potential associations with all these drugs. Monitoring could be necessary for many drugs to early detect PML.

Liver injury with novel oral anticoagulants: assessing post-marketing reports in the US Food and Drug Administration adverse event reporting system

AIM

We assessed the hepatic safety of novel oral anticoagulants (NOACs) analyzing the publicly available US-FDA adverse event reporting system (FAERS).

METHODS

We extracted reports of drug-induced liver injury (DILI) associated with NOACs, including acute liver failure (ALF) events. Based on US marketing authorizations, we performed disproportionality analyses, calculating reporting odds ratios (RORs) with 95% confidence interval (CI), also to test for event- and drug-related competition bias, and case-by-case evaluation for concomitant medications.

RESULTS

DILI reports represented 3.7% (n = 146) and 1.7% (n = 222) of all reports for rivaroxaban and dabigatran, respectively. No statistically significant association was found for dabigatran, in primary and secondary analyses. Disproportionality signals emerged for rivaroxaban in primary analysis (ALF: n = 25, ROR = 2.08, 95% CI 1.34, 3.08). In a large proportion of DILI reports concomitant hepatotoxic and/or interacting drugs were recorded: 42% and 37% (rivaroxaban and dabigatran, respectively), especially statins, paracetamol and amiodarone. Among ALF reports, fatal outcome occurred in 49% of cases (44% and 51%, rivaroxaban and dabigatran, respectively), whereas rapid onset of the event (<1 week) was detected in 46% of patients (47% and 44%, respectively).

CONCLUSIONS

The disproportionality signal for rivaroxaban calls for further comparative population-based studies to characterize and quantify the actual DILI risk of NOACs, taking into account drug- and patient-related risk factors. As DILI is unpredictable, our findings strengthen the role of (a) timely pharmacovigilance to detect post-marketing signals of DILI through FAERS and other data sources, (b) clinicians to assess early, on a case-by-case basis, the potential responsibility of NOACs when they diagnose a liver injury.

Thiazolidinediones and associated risk of bladder cancer: a systematic review and meta-analysis

AIMS

To determine whether thiazolidinedione use is associated with a risk of bladder cancer.

METHODS

We searched MEDLINE and EMBASE in June 2012 (with PubMed update to July 2013) and conducted meta-analysis on the overall risks of bladder cancer with pioglitazone or rosiglitazone and the risk with different categories of cumulative dose or duration of drug use.

RESULTS

We screened 230 citations and included 18 studies, comprising five randomized controlled trials (RCTs) and 13 observational studies. Meta-analysis showed a significantly higher overall risk of bladder cancer with pioglitazone in RCTs [7878 participants; odds ratio (OR) 2.51, 95% confidence interval (CI) 1.09-5.80] and observational studies (>2.6 million patients; OR for 'ever' users vs. non-users 1.21, 95% CI 1.09-1.35). Subgroup analysis of observational studies by cumulative dose showed the risk of bladder cancer to be greatest with >28.0 g of pioglitazone (OR 1.64, 95% CI 1.28-2.12). A significantly increased risk was found with both 12-24 months (OR 1.41, 95% CI 1.16-1.71) and >24 months (OR 1.51, 95% CI 1.26-1.81) cumulative durations of pioglitazone exposure. No significant risk was seen with rosiglitazone in RCTs (OR 0.84, 95% CI 0.35-2.04) or 'ever' users vs. non-users in observational studies (OR 1.03, 95% CI 0.94-1.12); the evidence for any relationship between bladder cancer risk and rosiglitazone cumulative duration is limited and inconsistent. Direct comparison of pioglitazone to rosiglitazone 'ever' users yielded an OR of 1.25 (95% CI 0.91-1.72).

CONCLUSIONS

A modest but clinically significant increase in the risk of bladder cancer with pioglitazone was found, which appears to be related to cumulative dose and duration of exposure. We recommend that prescribers limit pioglitazone use to shorter durations.

Computing limits on medicine risks based on collections of individual case reports

Background

Quantifying a medicine’s risks for adverse effects is crucial in assessing its value as a therapeutic agent. Rare adverse effects are often not detected until after the medicine is marketed and used in large and heterogeneous patient populations, and risk quantification is even more difficult. While individual case reports of suspected harm from medicines are instrumental in the detection of previously unknown adverse effects, they are currently not used for risk quantification. The aim of this article is to demonstrate how and when limits on medicine risks can be computed from collections of individual case reports.

Methods

We propose a model where drug exposures in the real world may be followed by adverse episodes, each containing one or several adverse effects. Any adverse episode can be reported at most once, and each report corresponds to a single adverse episode. Based on this model, we derive upper and lower limits for the per-exposure risk of an adverse effect for a given drug.

Results

An upper limit for the per-exposure risk of the adverse effect Y for a given drug X is provided by the reporting ratio of X together with Y relative to all reports on X, under two assumptions: (i) the average number of adverse episodes following exposure to X is one or less; and (ii) adverse episodes that follow X and contain Y are more frequently reported than adverse episodes in general that follow X. Further, a lower risk limit is provided by dividing the number of reports on X together with Y by the total number of exposures to X, under the assumption that exposures to X that are followed by Y generate on average at most one report on X together with Y. Using real data, limits for the narcolepsy risk following Pandemrix vaccination and the risk of coeliac disease following antihypertensive treatment were computed and found to conform to reference risk values from epidemiological studies.

Conclusions

Our framework enables quantification of medicine risks in situations where this is otherwise difficult or impossible. It has wide applicability, but should be particularly useful in structured benefit-risk assessments that include rare adverse effects.

Data mining of the public version of the FDA Adverse Event Reporting System

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, formerly AERS) is a database that contains information on adverse event and medication error reports submitted to the FDA. Besides those from manufacturers, reports can be submitted from health care professionals and the public. The original system was started in 1969, but since the last major revision in 1997, reporting has markedly increased. Data mining algorithms have been developed for the quantitative detection of signals from such a large database, where a signal means a statistical association between a drug and an adverse event or a drug-associated adverse event, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM). A survey of our previous reports suggested that the ROR provided the highest number of signals, and the EBGM the lowest. Additionally, an analysis of warfarin-, aspirin- and clopidogrel-associated adverse events suggested that all EBGM-based signals were included in the PRR-based signals, and also in the IC- or ROR-based ones, and that the PRR- and IC-based signals were in the ROR-based ones. In this article, the latest information on this area is summarized for future pharmacoepidemiological studies and/or pharmacovigilance analyses.