Challenges and Opportunities in Accessing and Analysing FAERS Data: A Call Towards a Collaborative Approach

A real-world pharmacovigilance study of KRAS G12C mutation inhibitors based on the food and drug administration adverse event reporting system

Introduction

Sotorasib and adagrasib have been widely used for the non-small cell lung cancer (NSCLC) patients harboring Kirsten rat sarcoma viral oncogene homolog (KRAS) G12C mutation. It's necessary to assess their safety profiles in the real-world population.

Methods

A retrospective pharmacovigilance was conducted to examine adverse events (AEs) associated with sotorasib and adagrasib therapies using the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Disproportionality analysis was performed employing Venn analysis and four data-mining algorithms, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multi-item gamma Poisson shrinker (MGPS).

Results

The most commonly reported system organ classes (SOCs) for both adagrasib and sororasib were general, gastrointestinal, and investigations disorders. Notably, sotorasib exhibited significant signals for neoplasms and hepatobiliary disorders in four algorithms. Specifically, AEs related to neoplasms were predominantly associated with lung malignancies, all of which were consistent with the therapeutic indications of KRAS G12C mutation inhibitor. A total of 19 common AEs were identified in sotorasib and adagrasib, spanning gastrointestinal, general, hepatobiliary, investigations, metabolism, musculoskeletal, neoplasms, and respiratory disorders. 4 severe AEs (SAEs) were identified in sotorasib, with 3 SAEs displaying significant signals in four algorithms, including drug-induced liver injury, pancreatitis, and hepatic failure. In adagrasib, only 2 SAEs were detected, with renal failure showing significant signals in four algorithms.

Conclusion

This study offers a comprehensive evaluation of the major safety signals associated with sotorasib and adagrasib, providing valuable information for clinicians regarding drug selection and safety considerations, thereby facilitating the design of future prospective safety studies.

The evolving role of disproportionality analysis in pharmacovigilance

INTRODUCTION

From 2009 to 2015, the IMI PROTECT conducted rigorous studies addressing questions about optimal implementation and significance of disproportionality analyses, leading to the development of Good Signal Detection Practices. The ensuing period witnessed the independent exploration of research paths proposed by IMI PROTECT, accumulating valuable experience and insights that have yet to be seamlessly integrated.

AREAS COVERED

This state-of-the-art review integrates IMI PROTECT recommendations with recent acquisitions and evolving challenges. It deals with defining the object of study, disproportionality methods, subgrouping, masking, drug-drug interaction, duplication, expectedness, the debated use of disproportionality results as risk measures, integration with other types of data.

EXPERT OPINION

Despite the ongoing skepticism regarding the usefulness of disproportionality analyses and individual case safety reports, their ability to timely detect safety signals regarding rare and unpredictable adverse reactions remains unparalleled. Moreover, recent exploration into their potential for characterizing safety signals revealed valuable insights concerning potential risk factors and the patient's perspective. To fully realize their potential beyond hypothesis generation and achieve a comprehensive evidence synthesis with other kinds of data and studies, each with their unique limitations and contributions, we need to investigate methods for more transparently communicating disproportionality results and mapping and addressing pharmacovigilance biases.

Pharmacovigilance analysis of orlistat adverse events based on the FDA adverse event reporting system (FAERS) database

Objective

Based on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database, we analyzed the signals of potential adverse events (AEs) of orlistat in the real world to provide a reference for its safe clinical use.

Methods

The FAERS database and OpenVigil 2.1 were used to obtain data on adverse events of orlistat from the first quarter of 2004 to the first quarter of 2023, and to analyze the population in which the adverse events occurred. And the signals of their potential adverse events were mined using reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and empirical Bayesian geometric mean (EBGM).

Result

A total of 21,079 reports of adverse events with orlistat as the primary suspected drug were collected in this study. Using four disproportionate analyses, we screened 117 preferred terms (PTs) involving 18 system organ classes (SOCs). We found that the most common adverse events at SOC level for orlistat remained "gastrointestinal disorders", while "metabolism and nutrition disorders", "renal and urinary disorders", "musculoskeletal and connective tissue disorders" and "hepatobiliary disorders" also ranked high in the number of case reports. In addition, at the PT level, we identified several new signals of adverse events not mentioned in the specification, including "lipiduria", "anal haemorrhage", "rectal haemorrhage", "haematochezia", "sigmoiditis", "diverticulitis" and "muscle spasms".

Conclusion

Most of the adverse events found in this study are consistent with the results described in the drug label. At the same time, we also found some new adverse events, which require more prospective studies to verify and elucidate their relationship with orlistat.

Adverse events associated with eteplirsen: A disproportionality analysis using the 2016-2023 FAERS data

Background

Eteplirsen (Exondys 51) is an orphan drug approved for the treatment of Duchenne muscular dystrophy (DMD), having received accelerated approval from the U.S. Food and Drug Administration (FDA) in 2016. The primary aim of this study is to closely monitor adverse events (AEs) associated with eteplirsen and to identify emerging signals to better characterize their safety profile.

Methods

AEs due to eteplirsen usage reported from the third quarter (Q3) of 2016 to the fourth quarter (Q4) of 2023 were collected from the FDA Adverse Event Reporting System (FAERS). The role_code of AEs mainly includes primary suspect (PS), secondary suspect (SS), concomitant (C), and interaction (I). This study targeted reports with a role_cod of 'PS.' According to the FDA deduplication rule, the latest FDA_DT is selected when the CASEID is the same, and the higher PRIMARYID is selected when the CASEID and FDA_DT are the same. Disproportionality analyses, encompassing four algorithms for reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian configuration promotion neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS), were utilized to quantify the signals of AEs associated with eteplirsen.

Results

From the FAERS database, a total of 13,205,369 reports were amassed throughout the study duration. Following the eradication of duplicates, the number of reports with eteplirsen designated as the PS amounted to 1480 encompassed 25 organ systems. Among these, "general disorders and administration site conditions," "injury, poisoning, and procedural complications," "respiratory, thoracic, and mediastinal disorders," "infections and infestations," "vascular disorders," and "product issues" met at least one of the four computational criteria. Additionally, 55 Preferred Terms (PTs) aligned with the prescribed algorithms. The median time to AEs in these patients was 903 days with an interquartile range (IQR) of 269-1575 days. Moreover, 70.04 % of AEs manifested one year or more after the initiation of treatment.

Conclusion

As an orphan drug granted accelerated approval, our study has confirmed well-known adverse drug reactions and identified potential safety issues associated with eteplirsen treatment. This has contributed to a deeper understanding of the complex interrelations between adverse reactions and the use of eteplirsen. The findings underscore the critical importance of ongoing monitoring and sustained observation to promptly detect and effectively manage AEs, thereby enhancing the overall safety and well-being of patients treated with eteplirsen for DMD.

Hepatitis-related adverse events associated with immune checkpoint inhibitors in cancer patients: an observational, retrospective, pharmacovigilance study using the FAERS database

Background: Immune checkpoint inhibitors (ICIs), including anti-PD-1, anti-PD-L1 and anti-CTLA-4 antibodies, have become a standard treatment for multiple cancer types. However, ICIs can induce immune-related adverse events, with hepatitis-related adverse events (HRAEs) being of particular concern. Our objective is to identify and characterize HRAEs that exhibit a significant association with ICIs using real-world data. Methods: In this observational and retrospective pharmacovigilance study, we extracted real-world adverse events reports from the FDA Adverse Event Reporting System database spanning from the first quarter of 2004 to the first quarter of 2023. We conducted both Frequentist and Bayesian methodologies in the framework of disproportionality analysis, which included the reporting odds ratios (ROR) and information components (IC) to explore the intricate relationship between ICIs and HRAEs. Results: Through disproportionality analysis, we identified three categories of HRAEs as being significantly related with ICIs, including autoimmune hepatitis (634 cases, ROR 19.34 [95% CI 17.80-21.02]; IC025 2.43), immune-mediated hepatitis (546 cases, ROR 217.24 [189.95-248.45]; IC025 4.75), and hepatitis fulminant (80 cases, ROR 4.56 [3.65-5.70]; IC025 0.49). The median age of patients who report ICI-related HRAEs was 63 years (interquartile range [IQR] 53.8-72), with a fatal outcome observed in 24.9% (313/1,260) of these reports. Cases pertaining to skin cancer, lung cancer, and kidney cancer constituted the majority of these occurrences. Patients treated with anti-PD-1 or anti-PD-L1 antibodies exhibited a higher frequency of immune-mediated hepatitis in comparison to those undergoing anti-CTLA-4 monotherapy, with a ROR of 3.59 (95% CI 1.78-6.18). Moreover, the dual ICI therapy demonstrated higher reporting rates of ICI-related HRAEs compared to ICI monotherapy. Conclusion: Our findings confirm that ICI treatment carries a significant risk of severe HRAEs, in particular autoimmune hepatitis, immune-mediated hepatitis, and hepatitis fulminant. Healthcare providers should exercise heightened vigilance regarding these risks when managing patients receiving ICIs.

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.

Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS

INTRODUCTION

In refining drug safety signals, defining the object of study is crucial. While research has explored the effect of different event definitions, drug definition is often overlooked. The US FDA Adverse Event Reporting System (FAERS) records drug names as free text, necessitating mapping to active ingredients. Although pre-mapped databases exist, the subjectivity and lack of transparency of the mapping process lead to a loss of control over the object of study.

OBJECTIVE

We implemented the DiAna dictionary, systematically mapping individual free-text instances to their corresponding active ingredients and linking them to the World Health Organization Anatomical Therapeutic Chemical (WHO-ATC) classification.

METHODS

We retrieved all drug names reported to the FAERS (2004-December 2022). Using existing vocabularies and string editing, we automatically mapped free text to ingredients. We manually revised the mapping and linked it to the ATC classification.

RESULTS

We retrieved 18,151,842 reports, with 74,143,411 drug entries. We manually checked the first 14,832 terms, up to terms occurring over 200 times (96.88% of total drug entries), to 6282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98.94%). The DiAna dictionary showed a higher sensitivity compared with RxNorm alone, particularly for specific drugs (e.g., rimegepant, adapalene, drospirenone, umeclidinium). The most prominent drug classes in the FAERS were immunomodulating (37.40%) and neurologic drugs (29.19%).

CONCLUSION

The DiAna dictionary, as a dynamic open-source tool, provides transparency and flexibility, enabling researchers to actively shape drug definitions during the mapping phase. This empowerment enhances accuracy, reproducibility, and interpretability of results.