A curated and standardized adverse drug event resource to accelerate drug safety research

Disproportionality Analysis and Characterisation of Medication Errors in EudraVigilance: Exploring Findings on Sexes and Age Groups

BACKGROUND

While medication errors (MEs) have been studied in the European Medicines Agency's EudraVigilance, extensive characterisation and signal detection based on sexes and age groups have not been attempted.

OBJECTIVES

The aim of this study was to characterise all ME-related individual case safety reports in EudraVigilance and explore notable signals of disproportionate reporting (SDRs) among sexes and age groups for the 30 most frequently reported drugs.

METHODS

Individual case safety reports were used from EudraVigilance reported between 2002 and 2021. An ME was defined as any Preferred Term from the narrow Standardised Medical Dictionary for Regulatory Activities® Query. Signals of disproportionate reporting were selected based on a lower boundary of the 95% confidence interval ≥ 1 of the reporting odds ratio, and at least 3 individual case safety reports. Analysed subgroups were female individuals, male individuals, and age groups 0-1 month, 2 months to 2 years, 3-11 years, 12-17 years, 18-64 years, 65-85 years, and >85 years. Heatmaps were utilised as a visual aid to identify striking SDRs.

RESULTS

Of the 9,662,345 EudraVigilance reports, 267,262 (2.8%) contained at least one ME, with a total of 300,324 MEs, for 429,554 drugs. The most reported ME was "Inappropriate schedule of product administration" (52,646; 17.5%), followed by "Incorrect dose administered" (32,379; 10.8%) and "Wrong technique in product usage process" (26,831; 8.9%). Individual case safety reports with MEs were most frequently related to female individuals (148,009; 55.4%), most often submitted by healthcare professionals (155,711; 58.3%), originated predominantly from the USA (98,716; 36.9%), followed by France (26,678; 10.0%), and showed a median reported age of 50 years (interquartile range: 26-68). Most ME individual case safety reports (158,991; 59.5%) were associated with a serious health outcome. A total of 847 SDRs were identified, based on the entire EudraVigilance database; for subgroups, the number of SDRs ranged from 84 for the age group 0-1 month to 749 for female individuals. Signals of disproportionate reporting for female individuals and male individuals were very similar. Most MEs were reported for the vaccine against human papillomavirus (Anatomical Therapeutic Chemical [ATC]: J07BM01; 11,086 MEs, 57% being "inappropriate schedule of product administration"), with reporting odds ratios that range from 1.5 to 47.0 among age groups. The SDR for the live-attenuated vaccine against herpes zoster (ATC: J07BK02) had a reporting odds ratio that ranged from 26.6 to 78.1 among all subgroups. Signals of disproportionate reporting for oxycodone (ATC: N02AA05; 847 cases of "Accidental overdose", 35%), risperidone (ATC: N05AX08; 469 cases "Inappropriate schedule of product administration", 22.3%) and rivaroxaban (ATC: B01AF01; 1,377 cases of "Incorrect dose administered", 34.6%) stood out with higher magnitude SDRs for the age group 2 months to 2 years, with an reporting odds ratio range between 8.2 and 10.7, while for the entire EudraVigilance the reporting odds ratio ranged between 1.3 and 1.6 for the same drugs.

CONCLUSIONS

This exploratory research provides an overview of characterised ME individual case safety reports and SDRs from the EudraVigilance database. Most conspicuous SDRs were identified in specific age groups. Signals of disproportionate reporting, not described in the literature, were found for vaccines, oxycodone, rivaroxaban and risperidone, and may prompt further examination by stakeholders. Top-reported MEs ("Inappropriate schedule of product administration", "Incorrect dose administered" and "Wrong technique in product usage process") emerged as a general priority focus to perform a further root-cause analysis involving healthcare providers, manufacturers and regulatory bodies, to improve the understanding and prevention of MEs.

Utilizing temporal pattern of adverse event reports to identify potential late-onset adverse events

OBJECTIVES

Through the use of FDA adverse event reporting system (FAERS) dataset, this study analyzes the pattern of time-to-event (TTE) for drugs and adverse events, and suggest ways to identify candidate late-onset events for monitoring.

METHODS

The duration between administration date of the drug and the onset of adverse events was explored with using FAERS data from 2012-2021. The fold change of proportional reporting ratios or reporting odds ratios were calculated to identify enriched events in the later period and to suggest the late-onset events for further monitoring. To compare the findings, we used the claims database of the Korean National Health Insurance Service (NHIS).

RESULTS

A total of 1,426,781 reports were included. The median TTE was 10 days (interquartile range [IQR]: 0-98 days), with 11.5% (n = 164,093) reporting events that occurred at least one year after administration. TTE and fold change analysis captured historical cases of late-onset events, while generating an additional less-explored list of events. The results for tumor necrosis factor (TNF) inhibitors were compared using the NHIS dataset.

CONCLUSION

Our study provides a comprehensive analysis of the FAERS dataset, focusing on TTE data. Periodic summarization of reports would be helpful in monitoring the late-onset events.

Drug-induced liver injury associated with atypical generation antipsychotics from the FDA Adverse Event Reporting System (FAERS)

BACKGROUND

Recent studies have shown that liver enzyme abnormalities were not only seen with typical antipsychotics (APs) but also with atypical antipsychotics (AAPs). During the last 20 years, the hepatotoxicity of various antipsychotics received much attention. However, systematic evaluations of hepatotoxicity associated with APs are limited.

METHODS

All drug related hepatic disorders cases were retrieved from the FDA Adverse Event Reporting System (FAERS) database using standardized MedDRA queries (SMQ) from the first quarter of 2017 to the first quarter of 2022. Patient characteristics and prognosis were assessed. In this study, a case/non-case approach was used to calculate reporting odds ratio (RORs) and 95% confidence intervals (CIs). We calculated the drug-induced liver injury (DILI) RORs for each AAPs.

RESULTS

A total of 408 DILI cases were attributed to AAPs during the study period. 18.6% of these were designated as serious adverse event (SAE), which include death (19.74%), hospitalization (68.42%), disability (2.63%), and life-threatening (9.21%) outcomes. The RORs values in descending order were: quetiapine (ROR = 0.782), clozapine (ROR = 0.665), aripiprazole (ROR = 0.507), amisulpride (ROR = 0.308), paliperidone (ROR = 0.212), risperidone (ROR = 0.198), ziprasidone (0.131).

CONCLUSION

The result found in our study was that all AAPs didn't have a significant correlation with increased hepatotoxicity. Future analysis of the FAERS database in conjunction with other data sources will be essential for continuous monitoring of DILI.

Long-term hepatobiliary disorder associated with trastuzumab emtansine pharmacovigilance study using the FDA Adverse Event Reporting System database

Trastuzumab emtansine (T-DM1) is widely utilized as a second-line and subsequent treatment for metastatic HER2+ breast cancer and has shown promise in early breast cancer treatment, particularly in adjuvant settings for residual disease after neoadjuvant chemotherapy. However, concerns have arisen regarding long-term hepatic adverse drug reactions (ADRs) not identified in clinical trials. We investigated potential safety signals of T-DM1 in hepatobiliary disorders and the time-to-onset of ADRs using the FDA Adverse Event Reporting System (FAERS) database. Suspected ADRs were extracted and divided into two groups: T-DM1 (N = 3387) and other drugs (N = 11,833,701). Potential signal for T-DM1 in hepatobiliary disorder were identified (reporting odds ratio [ROR] = 5.66, 95% confidence interval [CI] = 5.11-6.27; information component [IC] = 2.35, 95% Credibility Interval [Crl] = 2.18-2.51). A breast cancer indicated subgroup analysis (2519 T-DM1; 172,329 other drugs) also identified a potential safety signal (ROR = 3.28, 95% CI = 2.92-3.68; IC = 1.53, 95%CrI = 1.35-1.71). The median time-to-onset for T-DM1-associated hepatobiliary disorders was 41 days. For prolonged and chronic hepatobiliary disorders, median times were 322.5 and 301.5 days, respectively. These findings highlight the need for further research to inform clinical decisions on optimal T-DM1 treatment duration, balancing benefits with potential adverse reactions.

Signal of dementia with proton pump inhibitor after minimizing competition bias: an updated disproportionality analysis

OBJECTIVE

The association between proton pump inhibitor (PPI) and dementia was controversial. The aim of the current study was to perform an updated pharmacovigilance analysis of the association between dementia event and PPI treatment after minimizing competition bias.

METHODS

We gathered cases reported with PPI treatment based on the United States Food and Drug Administration Adverse Event Reporting System database from 2004 to 2023. We employed disproportionality algorithms, including reporting odds ratio (ROR) and the information component (IC), to detect the association between dementia event and PPI. We investigated the affection of event competition bias on the current disproportionality signal detection.

RESULTS

We finally included a total of 776,191 PPI cases, and 1813 cases in the dementia group. Analyzing primary suspect PPIs, we detected a significant association between dementia and PPI (ROR = 1.38, 95%CI 1.22 to 1.56; IC = 0.46, 95%CI 0.04 to 0.86). After excluding the PPI case with renal injury events, the strength of the dementia signal increased. Omeprazole (589 cases), pantoprazole (514 cases), and esomeprazole (386 cases) were the top three PPI reported with dementia events.

CONCLUSION

The current pharmacovigilance study identified a significant association between dementia and PPIs, except vonoprazan and tegoprazan, especially taking competition bias into account. Further high-quality prospective study still needed.

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.

A pharmacovigilance study on drug-induced liver injury associated with antibody-drug conjugates (ADCs) based on the food and drug administration adverse event reporting system

BACKGROUND

This study aimed to assess the association between drug-induced liver injury (DILI) and antibody-drug conjugates (ADCs) by comprehensively evaluating spontaneous reports submitted to the Food and Drug Administration Adverse Event Reporting System (FAERS) database from 2004Q1 to 2022Q3.

RESEARCH DESIGN AND METHODS

All DILI cases with ADCs as primary suspected drugs were extracted from the FAERS database from 2004Q1 to 2022Q3 using OpenVigil 2.1. The reporting odds ratio (ROR) and the proportional reporting ratio (PRR) for reporting the association between different drugs and DILI risk were calculated.

RESULTS

A total of 504 DILI cases were attributed to ADCs during the study period. Patients with ADCs-related DILI (n = 504) had a mean age of 56.2 ± 18.4 years, with 167 cases not reporting patients' age. Females and males comprised 42.5% and 44.0% of the cases, respectively, while there was no information on gender in 13.5% of the cases. The DILI signals were detected in trastuzumab emtansine, enfortumab vedotin, brentuximab vedotin, polatuzumab vedotin, gemtuzumab ozogamicin, inotuzumab ozogamicin, and trastuzumab deruxtecan.

CONCLUSIONS

The FAERS data mining suggested an association between DILI and some ADCs. Further studies are warranted to unraveling the underlying mechanisms and taking preventive measures for ADCs-related DILI.

In-silico approaches to assessing multiple high-level drug-drug and drug-disease adverse drug effects

INTRODUCTION

Pharmacovigilance plays a pivotal role in monitoring adverse events (AEs) related to chemical substances in human/animal populations. With increasing spontaneous-reporting systems, researchers turned to in-silico approaches to efficiently analyze drug safety profiles. Here, we review in-silico methods employed for assessing multiple drug-drug/drug-disease AEs covered by comparative analyses and visualization strategies.

AREAS COVERED

Disproportionality, involving multi-stage statistical methodologies and data processing, identifies safety signals among drug-AE pairs. By stratifying data based on disease indications/demographics, researchers address confounders and assess drug safety. Comparative analyses, including clustering techniques and visualization techniques, assess drug similarities, patterns, and trends, calculate correlations, and identify distinct toxicities. Furthermore, we conducted a thorough Scopus search on 'pharmacovigilance,' yielding 5,836 publications spanning 2003 to 2023.

EXPERT OPINION

Pharmacovigilance relies on diverse data sources, presenting challenges in the integration of in-silico approaches and requiring compliance with regulations and AI adoption. Systematic use of statistical analyses enables identifications of potential risks with drugs. Frequentist and Bayesian methods are used in disproportionalities, each with its strengths and weaknesses. Integration of pharmacogenomics with pharmacovigilance enables personalized medicine, with AI further enhancing patient engagement. This multidisciplinary approach holds promise, improving drug efficacy and safety, and should be a core mission of One-Health studies.

Using electronic health records for clinical pharmacology research: Challenges and considerations

Electronic health records (EHRs) contain a vast array of phenotypic data on large numbers of individuals, often collected over decades. Due to the wealth of information, EHR data have emerged as a powerful resource to make first discoveries and identify disparities in our healthcare system. While the number of EHR-based studies has exploded in recent years, most of these studies are directed at associations with disease rather than pharmacotherapeutic outcomes, such as drug response or adverse drug reactions. This is largely due to challenges specific to deriving drug-related phenotypes from the EHR. There is great potential for EHR-based discovery in clinical pharmacology research, and there is a critical need to address specific challenges related to accurate and reproducible derivation of drug-related phenotypes from the EHR. This review provides a detailed evaluation of challenges and considerations for deriving drug-related data from EHRs. We provide an examination of EHR-based computable phenotypes and discuss cutting-edge approaches to map medication information for clinical pharmacology research, including medication-based computable phenotypes and natural language processing. We also discuss additional considerations such as data structure, heterogeneity and missing data, rare phenotypes, and diversity within the EHR. By further understanding the complexities associated with conducting clinical pharmacology research using EHR-based data, investigators will be better equipped to design thoughtful studies with more reproducible results. Progress in utilizing EHRs for clinical pharmacology research should lead to significant advances in our ability to understand differential drug response and predict adverse drug reactions.

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

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

An analysis of suicidal and self-injurious behavior reports with antiseizure medications in the FDA adverse event database

BACKGROUND

Pharmacovigilance systems such as the FDA Adverse Event Reporting System (FAERS), are established models for adverse event surveillance that may have been missed during clinical trials. We aimed to analyze twenty-five anti-seizure medications (ASMs) in FAERS to assess for increased reporting of suicidal and self-injurious behavior.

METHODS

Twenty-five ASMs were analyzed: brivaracetam, cannabidiol, carbamazepine, clobazam, clonazepam, diazepam, eslicarbazepine, felbamate, gabapentin, lacosamide, lamotrigine, levetiracetam, oxcarbazepine, perampanel, phenobarbital, phenytoin, pregabalin, primidone, rufinamide, stiripentol, tiagabine, topiramate, valproate, vigabatrin, zonisamide. Reports of "suicidal and self-injurious behavior" were collected from January 1, 2004, to December 31, 2020, using OpenVigil 2.1 tool with indication as "Epilepsy". Relative reporting ratio, proportional reporting ratio, and reporting odds ratio were calculated utilizing all other drug reports for epilepsy patients as a control.

RESULTS

Significant relative operating ratio, ROR (greater than 1, p<0.05) were observed for diazepam (2.909), pregabalin (2.739), brivaracetam (2.462), gabapentin (2.185), clonazepam (1.649), zonisamide (1.462), lacosamide (1.333), and levetiracetam (1.286).

CONCLUSIONS

Of the 25 ASMs that were analyzed in this study, 4 (16%) were identified to have been linked with a likely true adverse event. These drugs included diazepam, brivaracetam, gabapenetin, and pregabalin. Although several limitations are present with the FAERS database, it is imperative to closely monitor patient comorbidities for increased risk of suicidality with the use of several ASMs.

Teprotumumab Related Hearing Loss: A Large-Scale Analysis and Review of Voluntarily Reported Patient Complaints to the Food and Drug Administration (FDA)

PURPOSE

Accumulating case reports and series have suggested that teprotumumab may significantly increase the risk of hearing impairment that, in some cases, does not resolve. This study investigates the association between hearing impairment and teprotumumab use.

METHODS

A disproportionality analysis was conducted using the United States Food and Drug Administration Adverse Event Reporting System, a publicly accessible database used for postmarketing surveillance and research. All adverse event reports containing the terms "teprotumumab" or "Tepezza" and a similar comparison group from all patients with the same indications for teprotumumab use (e.g., autoimmune thyroiditis, endocrine ophthalmopathy, and hyperthyroidism) but who had not received the drug were selected. Hearing impairment events were identified using the hearing impairment Standardized MedDRA Query.

RESULTS

A total of 940 teprotumumab-associated adverse events were identified, including 84 hearing-related adverse events, with the first reported to the Food and Drug Administration in April 2020. A comparison group of 32,794 nonteprotumumab adverse events was identified with 127 hearing-related adverse events reported. Use of teprotumumab in patients with thyroid conditions was associated with a nearly 24-fold (proportional reporting ratio [PRR] 23.6, 95% confidence interval [CI]: 18.1-30.8) increased likelihood of any hearing disorder (p value <0.0001). The association was specifically elevated for a variety of deafness conditions (e.g., bilateral deafness [PRR: 41.9; 95% CI: 12.8-136.9]), Eustachian tube disorders (PRR: 34.9; 95% CI: 4.9-247.4), hypoacusis (PRR: 10.1; 95% CI: 7.6-13.3), and tinnitus (PRR: 8.7; 95% CI: 6.2-12.1).

CONCLUSIONS

Patients treated with teprotumumab should receive warnings regarding the increased risk of hearing-related impairments and receive audiometry before, during, and after treatment.

Antiviral influenza treatments and hemorrhage-related adverse events in the United States Food and Drug Administration Adverse Event Reporting System (FAERS) database

STUDY OBJECTIVE

To determine whether there is a signal for gastrointestinal (GI) or intracranial (IC) hemorrhage associated with the use of antiviral medications for influenza in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database.

DESIGN

Disproportionality analysis.

DATA SOURCE

The FAERS database was searched using OpenVigil 2.1 to identify GI and IC hemorrhage events reported between 2004 and 2022.

MEASUREMENTS

Antiviral medications for influenza included the following: oseltamivir, zanamivir, peramivir, and baloxavir marboxil. Hemorrhage events were identified using Standardized Medical Dictionary for Regulatory Activities (MedDRA) Queries for GI and IC hemorrhages. Reporting odds ratios (RORs) were calculated to compare the occurrence of GI and IC hemorrhage events between antiviral drugs for influenza and (i) all other medications and (ii) antibiotics. RORs were also calculated for each of the individual antiviral medications.

MAIN RESULTS

A total of 245 cases of GI hemorrhage and 23 cases of IC hemorrhage were identified in association with four antivirals. In comparison with all other drugs, the RORs of GI hemorrhage for oseltamivir, zanamivir, peramivir, baloxavir, and all antivirals combined were 1.17, 0.62, 4.44, 2.53, and 1.22, respectively, indicating potential variations in GI hemorrhage risk among the antivirals. In contrast, in comparison with all other drugs, the RORs of IC hemorrhage for oseltamivir (0.44), zanamivir (0.16), baloxavir (0.44), and all antivirals combined (0.41) were less than 1.0 which is consistent with no elevated risk of IC hemorrhage.

CONCLUSION

In this study, some signals for GI hemorrhage were observed, particularly for peramivir and baloxavir marboxil. Further investigation is warranted to better understand and evaluate the potential risks of GI hemorrhage associated with antiviral treatments for influenza.

Discovering clinical drug-drug interactions with known pharmacokinetics mechanisms using spontaneous reporting systems and electronic health records

OBJECTIVE

Although the mechanisms behind pharmacokinetic (PK) drug-drug interactions (DDIs) are well-documented, bridging the gap between this knowledge and clinical evidence of DDIs, especially for serious adverse drug reactions (SADRs), remains challenging. While leveraging the FDA Adverse Event Reporting System (FAERS) database along with disproportionality analysis tends to detect a vast number of DDI signals, this abundance complicates further investigation, such as validation through clinical trials. Our study proposed a framework to efficiently prioritize these signals and assessed their reliability using multi-source Electronic Health Records (EHR) to identify top candidates for further investigation.

METHODS

We analyzed FAERS data spanning from January 2004 to March 2023, employing four established disproportionality methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagating Neural Network (BCPNN). Building upon these models, we developed four ranking models to prioritize DDI-SADR signals and cross-referenced signals with DrugBank. To validate the top-ranked signals, we employed longitudinal EHRs from Vanderbilt University Medical Center and the All of Us research program. The performance of each model was assessed by counting how many of the top-ranked signals were confirmed by EHRs and calculating the average ranking of these confirmed signals.

RESULTS

Out of 189 DDI-SADR signals identified by all four disproportionality methods, only two were documented in the DrugBank database. By prioritizing the top 20 signals as determined by each of the four disproportionality methods and our four ranking models, 58 unique DDI-SADR signals were selected for EHR validations. Of these, five signals were confirmed. The ranking model, which integrated the MGPS and BCPNN, demonstrated superior performance by assigning the highest priority to those five EHR-confirmed signals.

CONCLUSION

The fusion of disproportionality analysis with ranking models, validated through multi-source EHRs, presents a groundbreaking approach to pharmacovigilance. Our study's confirmation of five significant DDI-SADRs, previously unrecorded in the DrugBank database, highlights the essential role of advanced data analysis techniques in identifying ADRs.

Adverse events associated with immune checkpoint inhibitors in non-small cell lung cancer: a safety analysis of clinical trials and FDA pharmacovigilance system

Objectives

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC). However, the application of ICIs can also cause treatment-related adverse events (trAEs) and immune-related adverse events (irAEs). This study was to evaluate both the irAEs and trAEs of different ICI strategies for NSCLC based on randomized clinical trials (RCTs). The study also examined real-world pharmacovigilance data from the Food and Drug Administration Adverse Event Reporting System (FAERS) regarding claimed ICI-associated AEs in clinical practice.

Methods

Based on Pubmed, Embase, Medline, and the Cochrane CENTRAL, we retrieved RCTs comparing ICIs with chemotherapy drugs or with different ICI regimens for the treatment of NSCLC up to October 20, 2023. Bayesian network meta-analysis (NMA) was performed using odds ratios (ORs) with 95% credible intervals (95%CrI). Separately, a retrospective pharmacovigilance study was performed based on FAERS database, extracting ICI-associated AEs in NSCLC patients between the first quarter (Q1) of 2004 and Q4 of 2023. The proportional reports reporting odds ratio was calculated to analyze the disproportionality.

Results

The NMA included 51 RCTs that involved a total of 26,958 patients with NSCLC. Based on the lowest risk of any trAEs, cemiplimab, tislelizumab, and durvalumab were ranked as the best. Among the agents associated with the lowest risk of grades 3-5 trAEs, tislelizumab, avelumab, and nivolumab were most likely to rank highest. As far as any or grades 3-5 irAEs are concerned, atezolizumab plus bevacizumab plus chemotherapy is considered the most safety option. However, it is associated with a high risk of grades 3-5 trAEs. As a result of FAERS pharmacovigilance data analysis, 9,420 AEs cases have been identified in 7,339 NSCLC patients treated with ICIs, and ICIs were related to statistically significant positive signal with 311 preferred terms (PTs), and comprehensively investigated and identified those AEs highly associated with ICIs. In total, 152 significant signals were associated with Nivolumab, with malignant neoplasm progression, death, and hypothyroidism being the most frequent PTs.

Conclusion

These findings revealed that ICIs differed in their safety profile. ICI treatment strategies can be improved and preventive methods can be developed for NSCLC patients based on our results.

A Real-world Toxicity Atlas Shows that Adverse Events of Combination Therapies Commonly Result in Additive Interactions

PURPOSE

Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting.

EXPERIMENTAL DESIGN

We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing.

RESULTS

The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically.

CONCLUSIONS

Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice.

Antibiotics-associated pseudomembranous colitis: a disproportionality analysis of the US food and drug administration adverse event reporting system (FAERS) database

BACKGROUND

Evaluating antibiotics most commonly associated with pseudomembranous colitis (PMC) based on the real-world data is of great significance.

RESEARCH DESIGN AND METHODS

We used the data from FAERS to evaluate the potential association between antibiotics and PMC by disproportionality analyzes.

RESULTS

Eighty-one antibiotics which met the three algorithms simultaneously were enrolled. There were 1683 reports of PMC associated with the enrolled antibiotics. In the top 24 antibiotics, cefoxitin, streptomycin, fosfomycin, and micafungin had a high risk of PMC, but there were few reports in the literature.

CONCLUSIONS

This study was of great significance for healthcare professionals to realize the potential PMC risks of antibiotics.

Evaluating cardiac disorders associated with triazole antifungal agents based on the US Food and Drug Administration Adverse Event reporting system database

Introduction

Triazole antifungal agents are widely used to treat and prevent systemic mycoses. With wide clinical use, the number of reported adverse events has gradually increased. The aim of this study was to analyze the cardiac disorders associated with TAAs (fluconazole, voriconazole, itraconazole, posaconazole and isavuconazole) based on data from the US Food and Drug Administration Adverse Event Reporting System FDA Adverse Event Reporting System.

Methods

Data were extracted from the FAERS database between the first quarter of 2004 and third quarter of 2022. The clinical characteristics in TAA-associated cardiac AE reports were analyzed. Disproportionality analysis was performed to evaluate the potential association between AEs and TAAs using the reporting odds ratio (ROR) and proportional reporting ratio (PRR).

Results

Among 10,178,522 AE reports, 1719 reports were TAA-associated cardiac AEs as primary suspect drug. Most reports were related to fluconazole (38.34%), voriconazole (28.56%) and itraconazole (26.76%). Itraconazole (N = 195, 42.39%) and isavuconazole (N = 2, 14.29%) had fewer serious outcome events than three other drugs including fluconazole, voriconazole, and posaconazole. 13, 11, 26, 5 and 1 signals were detected for fluconazole, voriconazole, itraconazole, posaconazole and isavuconazole, respectively. The number of new signals unrecorded in the drug label was 9, 2, 13, 2 and 0 for fluconazole, voriconazole, itraconazole, posaconazole and isavuconazole, respectively.

Conclusion

Isavuconazole might be the safest of the five TAAs for cardiac AEs. TAA-associated cardiac disorders may result in serious adverse outcomes. Therefore, in addition to AEs on the drug label, we should pay attention to new AEs unrecorded on the drug label during the clinical use of TAAs.

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.

Drug and Natural Health Product Data Collection and Curation in the Canadian Longitudinal Study on Aging

This study aimed to develop an efficient data collection and curation process for all drugs and natural health products (NHPs) used by participants to the Canadian Longitudinal Study on Aging (CLSA). The three-step sequential process consisted of (a) mapping drug inputs collected through the CLSA to the Health Canada Drug Product Database (DPD), (b) algorithm recoding of unmapped drug and NHP inputs, and (c) manual recoding of unmapped drug and NHP inputs. Among the 30,097 CLSA comprehensive cohort participants, 26,000 (86.4%) were using a drug or an NHP with a mean of 5.3 (SD 3.8) inputs per participant user for a total of 137,366 inputs. Of those inputs, 70,177 (51.1%) were mapped to the Health Canada DPD, 20,729 (15.1%) were recoded by algorithms, and 44,108 (32.1%) were manually recoded. The Direct algorithm correctly classified 99.4 per cent of drug inputs and 99.5 per cent of NHP inputs. We developed an efficient three-step process for drug and NHP data collection and curation for use in a longitudinal cohort.

Broadening the capture of natural products mentioned in FAERS using fuzzy string-matching and a Siamese neural network

Increased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network to reduce this ambiguity. Our aim is to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). For this, we utilized Gestalt pattern-matching (GPM) and Siamese neural network (SM) to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. A team of health professionals refined the candidates identified in the previous step through manual review and annotation. After candidate adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by each (Non-overlapping: GPM 347, SM 248). We identified a total of 686 novel NP names from FAERS reports. Including these names in the FAERS collection yielded 3,486 additional reports mentioning NPs.

RxNorm for drug name normalization: a case study of prescription opioids in the FDA adverse events reporting system

Numerous studies have been conducted on the US Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS) database to assess post-marketing reporting rates for drug safety review and risk assessment. However, the drug names in the adverse event (AE) reports from FAERS were heterogeneous due to a lack of uniformity of information submitted mandatorily by pharmaceutical companies and voluntarily by patients, healthcare professionals, and the public. Studies using FAERS and other spontaneous reporting AEs database without drug name normalization may encounter incomplete collection of AE reports from non-standard drug names and the accuracies of the results might be impacted. In this study, we demonstrated applicability of RxNorm, developed by the National Library of Medicine, for drug name normalization in FAERS. Using prescription opioids as a case study, we used RxNorm application program interface (API) to map all FDA-approved prescription opioids described in FAERS AE reports to their equivalent RxNorm Concept Unique Identifiers (RxCUIs) and RxNorm names. The different names of the opioids were then extracted, and their usage frequencies were calculated in collection of more than 14.9 million AE reports for 13 FDA-approved prescription opioid classes, reported over 17 years. The results showed that a significant number of different names were consistently used for opioids in FAERS reports, with 2,086 different names (out of 7,892) used at least three times and 842 different names used at least ten times for each of the 92 RxNorm names of FDA-approved opioids. Our method of using RxNorm API mapping was confirmed to be efficient and accurate and capable of reducing the heterogeneity of prescription opioid names significantly in the AE reports in FAERS; meanwhile, it is expected to have a broad application to different sets of drug names from any database where drug names are diverse and unnormalized. It is expected to be able to automatically standardize and link different representations of the same drugs to build an intact and high-quality database for diverse research, particularly postmarketing data analysis in pharmacovigilance initiatives.

Descriptive analysis on disproportionate medication errors and associated patient characteristics in the Food and Drug Administration's Adverse Event Reporting System

BACKGROUND

Medication errors (MEs) are a major public health concern which can cause harm and financial burden within the healthcare system. Characterizing MEs is crucial to develop strategies to mitigate MEs in the future.

OBJECTIVES

To characterize ME-associated reports, and investigate signals of disproportionate reporting (SDRs) on MEs in the Food and Drug Administration's Adverse Event Reporting System (FAERS).

METHODS

FAERS data from 2004 to 2020 was used. ME reports were identified with the narrow Standardised Medical Dictionary for Regulatory Activities® (MedDRA®) Query (SMQ) for MEs. Drug names were converted to the Anatomical Therapeutic Chemical (ATC) classification. SDRs were investigated using the reporting odds ratio (ROR).

RESULTS

In total 488 470 ME reports were identified, mostly (59%) submitted by consumers and mainly (55%) associated with females. Median age at time of ME was 57 years (interquartile range: 37-70 years). Approximately 1 out of 3 reports stated a serious health outcome. The most prevalent reported drug class was "antineoplastic and immunomodulating agents" (25%). The most common ME type was "incorrect dose administered" (9%). Of the 1659 SDRs obtained, adalimumab was the most common drug associated with MEs, noting a ROR of 1.22 (95% confidence interval: 1.21-1.24).

CONCLUSION

This study offers a first of its kind characterization of MEs as reported to FAERS. Reported MEs are frequent and may be associated with serious health outcomes. This FAERS data provides insights on ME prevention and offers possibilities for additional in-depth analyses.

High risks adverse events associated with usage of aspirin in chronic obstructive pulmonary disease

BACKGROUND

Despite potential benefits and widespread prescription of aspirin among chronic obstructive pulmonary disease (COPD) patients, limited research has investigated its adverse effects (AEs) in COPD population.

METHODS

We conducted a retrospective analysis of adverse drug events (ADEs) reported in the US Food and Drug Administration Adverse Event Reporting System (FAERS) between Q1 2013 and Q2 2022. COPD patients were categorized into two groups based on aspirin use. ADEs related to aspirin use were identified using combined reporting odds ratio (ROR), proportional reporting ratio (PRR), information component (IC) methods.

RESULTS

A total of 56,660 ADEs reports associated with COPD patients were included in the study. Among these reports, 144 adverse events were linked to aspirin use in COPD patients, including fatigue (4.12%), diarrhea (3.13%), dyspnea exertional (2.03%), rhinorrhea (1.99%), weight increased (1.89%) and vomiting (1.84%), muscle spasms (1.79%), cardiac disorder (1.74%), heart rate increased (1.69%) and peripheral swelling (1.59%). Subgroup analysis indicates that age and gender might affect the AEs frequency in COPD patients using aspirin.

CONCLUSIONS

Our findings identify 10 most frequently reported ADEs associated with aspirin use in COPD patients, thus offer valuable insights into the AEs of aspirin for safer clinical utilization in COPD management.

Parasitic infections related to anti-type 2 immunity monoclonal antibodies: a disproportionality analysis in the food and drug administration's adverse event reporting system (FAERS)

Introduction: Monoclonal antibodies (mAbs) targeting immunoglobulin E (IgE) [omalizumab], type 2 (T2) cytokine interleukin (IL) 5 [mepolizumab, reslizumab], IL-4 Receptor (R) α [dupilumab], and IL-5R [benralizumab]), improve quality of life in patients with T2-driven inflammatory diseases. However, there is a concern for an increased risk of helminth infections. The aim was to explore safety signals of parasitic infections for omalizumab, mepolizumab, reslizumab, dupilumab, and benralizumab. Methods: Spontaneous reports were used from the Food and Drug Administration's Adverse Event Reporting System (FAERS) database from 2004 to 2021. Parasitic infections were defined as any type of parasitic infection term obtained from the Standardised Medical Dictionary for Regulatory Activities® (MedDRA®). Safety signal strength was assessed by the Reporting Odds Ratio (ROR). Results: 15,502,908 reports were eligible for analysis. Amongst 175,888 reports for omalizumab, mepolizumab, reslizumab, dupilumab, and benralizumab, there were 79 reports on parasitic infections. Median age was 55 years (interquartile range 24-63 years) and 59.5% were female. Indications were known in 26 (32.9%) reports; 14 (53.8%) biologicals were reportedly prescribed for asthma, 8 (30.7%) for various types of dermatitis, and 2 (7.6%) for urticaria. A safety signal was observed for each biological, except for reslizumab (due to lack of power), with the strongest signal attributed to benralizumab (ROR = 15.7, 95% Confidence Interval: 8.4-29.3). Conclusion: Parasitic infections were disproportionately reported for mAbs targeting IgE, T2 cytokines, or T2 cytokine receptors. While the number of adverse event reports on parasitic infections in the database was relatively low, resulting safety signals were disproportionate and warrant further investigation.

Drugs That Interact With Colchicine Via Inhibition of Cytochrome P450 3A4 and P-Glycoprotein: A Signal Detection Analysis Using a Database of Spontaneously Reported Adverse Events (FAERS)

BACKGROUND

Colchicine has a narrow therapeutic index. Its toxicity can be increased due to concomitant exposure to drugs inhibiting its metabolic pathway; these are cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp).

OBJECTIVE

To examine clinical outcomes associated with colchicine drug interactions using the spontaneous reports of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).

METHODS

We conducted a disproportionality analysis using FAERS data from January 2004 through June 2020. The reporting odds ratio (ROR) and observed-to-expected ratio (O/E) with shrinkage for adverse events related to colchicine's toxicity (ie, rhabdomyolysis/myopathy, agranulocytosis, hemorrhage, acute renal failure, hepatic failure, arrhythmias, torsade de pointes/QT prolongation, and cardiac failure) were compared between FAERS reports.

RESULTS

A total of 787 reports included the combined mention of colchicine, an inhibitor of both CYP3A4 and P-gp drug, and an adverse event of interest. Among reports that indicated the severity, 61% mentioned hospitalization and 24% death. A total of 37 ROR and 34 O/E safety signals involving colchicine and a CYP3A4/P-gp inhibitor were identified. The strongest ROR signal was for colchicine + atazanavir and rhabdomyolysis/myopathy (ROR = 35.4, 95% CI: 12.8-97.6), and the strongest O/E signal was for colchicine + atazanavir and agranulocytosis (O/E = 3.79, 95% credibility interval: 3.44-4.03).

CONCLUSION AND RELEVANCE

This study identifies numerous safety signals for colchicine and CYP3A4/P-gp inhibitor drugs. Avoiding the interaction or monitoring for toxicity in patients when co-prescribing colchicine and these agents is highly recommended.

The Association Between Taxane Use and Lacrimal Disorders

PURPOSE

The current study seeks to investigate the association between lacrimal disorders and the use of docetaxel and paclitaxel.

METHODS

A disproportionality analysis was conducted using the United States FDA Adverse Event Reporting System (FAERS). All adverse event reports containing the term docetaxel or paclitaxel were selected. Lacrimal adverse events were identified using the lacrimal disorders Standardized MedDRA Query (SMQ), which includes disorders that affect lacrimal gland and drainage system including blockage of nasolacrimal duct, occlusion/stenosis of punctum, lacrimal gland neoplasms, and inflammations and infections.

RESULTS

The proportionate reporting ratio (PRR) comparing lacrimal events among docetaxel to paclitaxel users was 2.47 (95% CI, 2.03-3.02). With respect to specific lacrimal events, dacryostenosis (PRR 19.54 [95% CI, 7.19-53.13]), increased lacrimation (PRR 3.2 [95% CI, 2.42-4.23]), lacrimation disorder (p = 0.02), and xeropthalmia reports (p > 0.001) were significantly more common.

CONCLUSIONS

The growing body of epidemiologic, clinical, and pathophysiologic research supports the case that docetaxel leads to adverse lacrimal events in certain patients and should be taken into consideration when oncologists consider docetaxel vs. paclitaxel.

Broadening the Capture of Natural Products Mentioned in FAERS Using Fuzzy String-Matching and a Siamese Neural Network

Increased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network to reduce this ambiguity. We aim to increase the capture of reports involving NPs in the US Food and Drug Administration Adverse Event Reporting System (FAERS). Gestalt pattern-matching (GPM) and Siamese neural network (SM) were used to identify potential mentions of NPs of interest in 389,386 FAERS reports with unmapped drug names. We refined the identified candidates through manual review and annotation by health professionals. After adjudication, GPM identified 595 unique NP names and SM 504. There was little overlap between candidates identified by the approaches (Non-overlapping: GPM 347, SM 248). In total, 686 novel NP names were identified in the unmapped FAERS reports. Including these names in the FAERS collection yielded 3,486 additional reports mentioning NPs.

Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions

PURPOSE

To evaluate the impact of multiple design criteria for reference sets that are used to quantitatively assess the performance of pharmacovigilance signal detection algorithms (SDAs) for drug-drug interactions (DDIs).

METHODS

Starting from a large and diversified reference set for two-way DDIs, we generated custom-made reference sets of various sizes considering multiple design criteria (e.g., adverse event background prevalence). We assessed differences observed in the performance metrics of three SDAs when applied to FDA Adverse Event Reporting System (FAERS) data.

RESULTS

For some design criteria, the impact on the performance metrics was neglectable for the different SDAs (e.g., theoretical evidence associated with positive controls), while others (e.g., restriction to designated medical events, event background prevalence) seemed to have opposing and effects of different sizes on the Area Under the Curve (AUC) and positive predictive value (PPV) estimates.

CONCLUSIONS

The relative composition of reference sets can significantly impact the evaluation metrics, potentially altering the conclusions regarding which methodologies are perceived to perform best. We therefore need to carefully consider the selection of controls to avoid misinterpretation of signals triggered by confounding factors rather than true associations as well as adding biases to our evaluation by "favoring" some algorithms while penalizing others.

A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs

In vitro secondary pharmacology assays are an important tool for predicting clinical adverse drug reactions (ADRs) of investigational drugs. We created the Secondary Pharmacology Database (SPD) by testing 1958 drugs using 200 assays to validate target-ADR associations. Compared to public and subscription resources, 95% of all and 36% of active (AC50 < 1 µM) results are unique to SPD, with bias towards higher activity in public resources. Annotating drugs with free maximal plasma concentrations, we find 684 physiologically relevant unpublished off-target activities. Furthermore, 64% of putative ADRs linked to target activity in key literature reviews are not statistically significant in SPD. Systematic analysis of all target-ADR pairs identifies several putative associations supported by publications. Finally, candidate mechanisms for known ADRs are proposed based on SPD off-target activities. Here we present a freely-available resource for benchmarking ADR predictions, explaining phenotypic activity and investigating clinical properties of marketed drugs.

Assessing adverse drug reaction reports for antidiabetic medications approved by the food and drug administration between 2012 and 2017: a pharmacovigilance study

Objective

Between 2012 and 2017, the U.S. Food and Drug Administration (FDA) approved 10 antidiabetic indicated therapies. Due to the limited literature on voluntarily reported safety outcomes for recently approved antidiabetic drugs, this study investigated adverse drug reactions (ADRs) reported in the FDA Adverse Event Reporting System (FAERS).

Research Design and Methods

A disproportionality analysis of spontaneously reported ADRs was conducted. FAERS reports from January 1, 2012 to March 31, 2022 were compiled, allowing a 5-year buffer following drug approval in 2017. Reporting odds ratios were calculated for the top 10 ADRs, comparing new diabetic agents to the other approved drugs in their therapeutic class.

Results

127,525 reports were identified for newly approved antidiabetic medications listed as the primary suspect (PS). For sodium-glucose co-transporter-2 (SGLT-2) inhibitors, the odds of blood glucose increased, nausea, and dizziness being reported was greater for empagliflozin. Dapagliflozin was associated with greater reports of weight decreased. Canagliflozin was found to have a disproportionally higher number of reports for diabetic ketoacidosis, toe amputation, acute kidney injury, fungal infections, and osteomyelitis. Assessing glucagon-like peptide-1 (GLP-1) receptor agonists, dulaglutide and semaglutide were associated with greater reports of gastrointestinal adverse drug reactions. Exenatide was disproportionally associated with injection site reactions and pancreatic carcinoma reports.

Conclusion

Pharmacovigilance studies utilizing a large publicly available dataset allow an essential opportunity to evaluate the safety profile of antidiabetic drugs utilized in clinical practice. Additional research is needed to evaluate these reported safety concerns for recently approved antidiabetic medications to determine causality.

An Early Adverse Drug Event Detection Approach with False Discovery Rate Control

Adverse drug event (ADE) is a significant challenge in clinical practice. Many ADEs have not been identified timely after the approval of the corresponding drugs. Despite the use of drug similarity network demonstrates early success on improving ADE detection, false discovery rate (FDR) control remains unclear in its application. Additionally, performance of early ADE detection has not been explicitly investigated under the time-to-event framework. In this manuscript, we propose to use the drug similarity based posterior probability of null hypothesis for early ADE detection. The proposed approach is also able to control FDR for monitoring a large number of ADEs of multiple drugs. The proposed approach outperforms existing approaches on mining labeled ADEs in the US FDA's Adverse Event Reporting System (FAERS) data, especially in the first few years after the drug initial reporting time. Additionally, the proposed approach is able to identify more labeled ADEs and has significantly lower time to ADE detection. In simulation study, the proposed approach demonstrates proper FDR control, as well as has better true positive rate and an excellent true negative rate. In our exemplified FAERS analysis, the proposed approach detects new ADE signals and identifies ADE signals in a timelier fashion than existing approach. In conclusion, the proposed approach is able to both reduce the time and improve the FDR control for ADE detection.

Selected cutaneous adverse events in patients treated with ICI monotherapy and combination therapy: a retrospective pharmacovigilance study and meta-analysis

Introduction: Cutaneous adverse events are commonly reported immune-related adverse events (irAEs), some of which are serious or even life-threatening, and it is essential to study these specific cutaneous AEs to understand their characteristics and risk. Methods: We performed a meta-analysis of published clinical trials for immune checkpoint inhibitors (ICIs) to evaluate the incidence of cutaneous adverse events, using data from PubMed, Embase, and the Cochrane Library databases. Results: A total of 232 trials with 45,472 patients were involved. Results showed that anti-PD-1 and targeted therapy combinations were associated with higher risk for most of the selected cutaneous adverse events. In addition, a retrospective pharmacovigilance study was conducted using the Food and Drug Administration (FDA) Adverse Events System database. Reporting odds ratio (ROR) and Bayesian information components (IC) were used to perform the disproportionality analysis. Cases were extracted from January 2011 to September 2020. We identified 381 (20.24%) maculopapular rash, 213 (11.32%) vitiligo, 215 (11.42%) Stevens-Johnson syndrome (SJS), and 165 (8.77%) toxic epidermal necrolysis (TEN) cases. For vitiligo, anti-PD-1/L1 combined with anti-CTLA-4 therapy showed the strongest signal (ROR: 55.89; 95% CI: 42.34-73.78; IC₀₂₅: 4.73). Palmar-plantar erythrodysesthesia (PPE) was reported with the most significant association with combined anti-PD-1/L1 and VEGF (R)-TKIs (ROR: 18.67; 95% CI: 14.77-23.60; IC₀₂₅: 3.67). For SJS/TEN, antiPD-1 inhibitors showed the strongest signal (ROR: 3.07; 95% CI: 2.68-3.52; IC₀₂₅: 1.39). The median onset time of vitiligo and SJS/TEN was 83 and 24 days, respectively. Conclusion: Overall, in selected cutaneous AEs, each of them showed specific characteristics. It is necessary to realize their differences and take appropriate interventions in patients with different regimens.

Application of an Integrative Drug Safety Model for Detection of Adverse Drug Events Associated With Inhibition of Glutathione Peroxidase 1 in Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic Obstructive Pulmonary Disease is characterised by declining lung function and a greater oxidative stress burden due to reduced activity of antioxidant enzymes such as Glutathione Peroxidase 1.

OBJECTIVES

The extent to which drugs may contribute to this compromised activity is largely unknown. An integrative drug safety model explores inhibition of Glutathione Peroxidase 1 by drugs and their association with chronic obstructive pulmonary disease adverse drug events.

METHODS

In silico molecular modelling approaches were utilised to predict the interactions that drugs have within the active site of Glutathione Peroxidase 1 in both human and bovine models. Similarities of chemical features between approved drugs and the known inhibitor tiopronin were also investigated. Subsequently the Food and Drug Administration Adverse Event System was searched to uncover adverse drug event signals associated with chronic obstructive pulmonary disease.

RESULTS

Statistical and molecular modelling analyses confirmed that the use of several registered drugs, including acetylsalicylic acid and atenolol may be associated with inhibition of Glutathione Peroxidase 1 and chronic obstructive pulmonary disease.

CONCLUSION

The integration of molecular modelling and pharmacoepidemological data has the potential to advance drug safety science. Ongoing review of medication use and further pharmacoepidemiological and biological analyses are warranted to ensure appropriate use is recommended.

A Community-of-Practice-based Evaluation Methodology for Knowledge Intensive Computational Methods and its Application to Multimorbidity Decision Support

OBJECTIVE

The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans.

MATERIALS AND METHODS

Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods-research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods.

RESULTS

Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support.Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods.

DISCUSSION

The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS.

CONCLUSION

We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).

Statistical methods for exploring spontaneous adverse event reporting databases for drug-host factor interactions

BACKGROUND

Drug toxicity does not affect patients equally; the toxicity may only exert in patients who possess certain attributes of susceptibility to specific drug properties (i.e., drug-host interaction). This concept is crucial for personalized drug safety but remains under-studied, primarily due to methodological challenges and limited data availability. By monitoring a large volume of adverse event reports in the postmarket stage, spontaneous adverse event reporting systems provide an unparalleled resource of information for adverse events and could be utilized to explore risk disparities of specific adverse events by age, sex, and other host factors. However, well-formulated statistical methods to formally address such risk disparities are currently lacking.

METHODS

In this paper, we present a statistical framework to explore spontaneous adverse event reporting databases for drug-host interactions and detect risk disparities in adverse drug events by various host factors, adapting methods for safety signal detection. We proposed four different methods, including likelihood ratio test, normal approximation test, and two tests using subgroup ratios. We applied our proposed methods to simulated data and Food and Drug Administration (FDA) Adverse Event Reporting Systems (FAERS) and explored sex-/age-disparities in reported liver events associated with specific drug classes.

RESULTS

The simulation result demonstrates that two tests (likelihood ratio, normal approximation) can detect disparities in adverse drug events associated with host factors while controlling the family wise error rate. Application to real data on drug liver toxicity shows that the proposed method can be used to detect drugs with unusually high level of disparity regarding a host factor (sex or age) for liver toxicity or to determine whether an adverse event demonstrates a significant unbalance regarding the host factor relative to other events for the drug.

CONCLUSION

Though spontaneous adverse event reporting databases require careful data processing and inference, the sheer size of the databases with diverse data from different countries provides unique resources for exploring various questions for drug safety that are otherwise impossible to address. Our proposed methods can be used to facilitate future investigation on drug-host interactions in drug toxicity using a large number of reported adverse events.

Medication-induced osteonecrosis of the jaw: a review of cases from the Food and Drug Administration Adverse Event Reporting System (FAERS)

BACKGROUND

Osteonecrosis of the jaw (ONJ) is a rare but serious adverse drug reaction (ADR) commonly associated with bisphosphonate and denosumab therapy. Prior research utilized an online, public FDA Adverse Event Reporting System (FAERS) Database to explore this ADR. This data identified and described several novel medications associated with ONJ. Our study aims to build upon the prior findings, reporting trends of medication induced ONJ over time and identifying newly described medications.

METHODS

We searched the FAERS database for all reported cases of medication related osteonecrosis of the jaw (MRONJ) from 2010 to 2021. Cases lacking patient age or gender were excluded. Only adults (18 +) and reports from Healthcare Professions were included. Duplicate cases were removed. The top 20 medications were identified and described for April 2010-December 2014 and April 2015-January 2021.

RESULTS

Nineteen thousand six hundred sixty-eight cases of ONJ were reported to the FAERS database from 2010-2021. 8,908 cases met inclusion criteria. 3,132 cases were from 2010-2014 and 5,776 cases from 2015-2021. Within the cases from 2010-2014, 64.7% were female and 35.3% were male, and the average age was 66.1 ± 11.1 years. Between 2015-2021, 64.3% were female and 35.7% were male, and the average age was 69.2 ± 11.5 years. Review of the 2010-2014 data identified several medications and drug classes associated with ONJ not previously described. They include lenalidomide, corticosteroids (prednisolone and dexamethasone), docetaxel and paclitaxel, letrozole, methotrexate, imatinib, and teriparatide. Novel drugs and classes described between 2015-2021 include palbociclib, pomalidomide, radium 223, nivolumab, and cabozantinib.

DISCUSSION

While stricter inclusion criteria and removal of duplicate cases led to fewer overall identified cases of MRONJ when compared to prior research, our data represents a more reliable analysis of MRONJ reports to the FAERS database. Denosumab was the most frequently reported medication associated with ONJ. While unable to imply incidence rates from our data due to the nature of the FAERS database, our findings provide further description of the various medications associated with ONJ and elucidate patient demographics associated with the ADR. Additionally, our study identifies cases of several newly described drugs and drug classes that have not been previously described in literature.

An evaluation of adverse drug reactions and outcomes attributed to kratom in the US Food and Drug Administration Adverse Event Reporting System from January 2004 through September 2021

Kratom is a widely used Asian botanical that has gained popularity in the United States due to a perception that it can treat pain, anxiety, and opioid withdrawal symptoms. The American Kratom Association estimates 10-16 million people use kratom. Kratom-associated adverse drug reactions (ADRs) continue to be reported and raise concerns about the safety profile of kratom. However, studies are lacking that describe the overall pattern of kratom-associated adverse events and quantify the association between kratom and adverse events. ADRs reported to the US Food and Drug Administration Adverse Event Reporting System from January 2004 through September 2021 were used to address these knowledge gaps. Descriptive analysis was conducted to analyze kratom-related adverse reactions. Conservative pharmacovigilance signals based on observed-to-expected ratios with shrinkage were estimated by comparing kratom to all other natural products and drugs. Based on 489 deduplicated kratom-related ADR reports, users were young (mean age 35.5 years), and more often male (67.5%) than female patients (23.5%). Cases were predominantly reported since 2018 (94.2%). Fifty-two disproportionate reporting signals in 17 system-organ-class categories were generated. The observed/reported number of kratom-related accidental death reports was 63-fold greater than expected. There were eight strong signals related to addiction or drug withdrawal. An excess proportion of ADR reports were about kratom-related drug complaints, toxicity to various agents, and seizures. Although further research is needed to assess the safety of kratom, clinicians and consumers should be aware that real-world evidence points to potential safety threats.

Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data

INTRODUCTION

Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness.

OBJECTIVE

This study aimed to identify methods for the early detection of a wide range of ADR signals.

METHODS

First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance.

RESULTS

We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method.

CONCLUSIONS

ARM of claims data may be effective in the early detection of a wide range of ADR signals.

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

Analysis of adverse event of interstitial lung disease in men with prostate cancer receiving hormone therapy using the Food and Drug Administration Adverse Event Reporting System

AIMS

To investigate interstitial lung disease (ILD) in men with prostate cancer receiving hormone therapy.

METHODS

We gathered cases diagnosed with prostate cancer based on the United States Food and Drug Administration Adverse Event Reporting System (FAERS) database from 2004 to 2020. We divided the included cases into 3 groups based on the primary suspected drugs: a hormone therapy group, a positive control group (taxanes), and a negative control group. We employed reporting odds ratio, a disproportionality method, to detect the association between ILD events and target drugs.

RESULTS

We finally included a total of 85 403 cases, 69 894 cases (628 ILD event cases) in the hormone therapy group, 2302 cases (158 ILD event cases) in the positive control group and 13 207 cases (72 ILD event cases) in the negative control group. There were 394 ILD event cases (62.74%) in the hormone therapy group in Japan; 78.68% of the ILD events occurred within the first year after hormone treatment. Disproportionality analysis indicated that ILD events were significantly associated with nilutamide, flutamide, bicalutamide, goserelin, degarelix and apalutamide; the reporting odds ratios (95% confidence interval) were 32.14 (11.03-93.63), 9.93 (3.62-27.21), 8.19 (6.01-11.16), 3.74 (2.61-5.37), 2.41 (1.55-3.75) and 1.94 (1.01-3.75), respectively.

CONCLUSION

Based on this FAERS pharmacovigilance analysis, the association between ILD events and hormone therapy drugs, including bicalutamide, flutamide, nilutamide, goserelin, degarelix and apalutamide, should not be ignored, especially in the Japanese population. Lung function of prostate cancer patients should be monitored when receiving the hormone therapy drugs mentioned above, especially for the first year post medication.

Using chemical and biological data to predict drug toxicity

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

Lisinopril prevents bullous pemphigoid induced by dipeptidyl peptidase 4 inhibitors via the Mas receptor pathway

Recent studies have suggested that dipeptidyl peptidase 4 (DPP4) inhibitors increase the risk of development of bullous pemphigoid (BP), which is the most common autoimmune blistering skin disease; however, the associated mechanisms remain unclear, and thus far, no therapeutic targets responsible for drug-induced BP have been identified. Therefore, we used clinical data mining to identify candidate drugs that can suppress DPP4 inhibitor-associated BP, and we experimentally examined the underlying molecular mechanisms using human peripheral blood mononuclear cells (hPBMCs). A search of the US Food and Drug Administration Adverse Event Reporting System and the IBM^® MarketScan^® Research databases indicated that DPP4 inhibitors increased the risk of BP, and that the concomitant use of lisinopril, an angiotensin-converting enzyme inhibitor, significantly decreased the incidence of BP in patients receiving DPP4 inhibitors. Additionally, in vitro experiments with hPBMCs showed that DPP4 inhibitors upregulated mRNA expression of MMP9 and ACE2, which are responsible for the pathophysiology of BP in monocytes/macrophages. Furthermore, lisinopril and Mas receptor (MasR) inhibitors suppressed DPP4 inhibitor-induced upregulation of MMP9. These findings suggest that the modulation of the renin-angiotensin system, especially the angiotensin1-7/MasR axis, is a therapeutic target in DPP4 inhibitor-associated BP.

Inhibitors of the Mechanistic Target of Rapamycin Can Ameliorate Bortezomib-Induced Peripheral Neuropathy

Bortezomib, an anticancer drug for multiple myeloma and mantle cell lymphoma, causes severe adverse events and leads to peripheral neuropathy. The associated neuropathy limits the use of bortezomib and could lead to discontinuation of the treatment; therefore, effective intervention is crucial. In the present study, we statistically searched for a drug that could alleviate bortezomib-induced peripheral neuropathy using adverse event self-reports. We observed that specific inhibitors of the mechanistic target of rapamycin (mTOR) lowered the incidence of bortezomib-induced peripheral neuropathy. These findings were experimentally validated in mice, which exhibited long-lasting mechanical hypersensitivity after repeated bortezomib treatment. This effect was inhibited for hours after a systemic injection with rapamycin or everolimus in a dose-dependent manner. Bortezomib-induced allodynia was accompanied by the activation of spinal astrocytes, and intrathecal injection of mTOR inhibitors or an inhibitor of ribosomal protein S6 kinase 1, a downstream target of mTOR, exhibited considerable analgesic effects in a dose-dependent manner. These results suggest that mTOR inhibitors, which are readily available to patients prescribed bortezomib, are one of the most effective therapeutics for bortezomib-induced peripheral neuropathy.

Vitamin D supplementation is effective for olanzapine-induced dyslipidemia

Olanzapine is an atypical antipsychotic drug that is clinically applied in patients with schizophrenia. It increases the risk of dyslipidemia, a disturbance of lipid metabolic homeostasis, usually characterized by increased low-density lipoprotein (LDL) cholesterol and triglycerides, and accompanied by decreased high-density lipoprotein (HDL) in the serum. In this study, analyzing the FDA Adverse Event Reporting System, JMDC insurance claims, and electronic medical records from Nihon University School of Medicine revealed that a co-treated drug, vitamin D, can reduce the incidence of olanzapine-induced dyslipidemia. In the following experimental validations of this hypothesis, short-term oral olanzapine administration in mice caused a simultaneous increase and decrease in the levels of LDL and HDL cholesterol, respectively, while the triglyceride level remained unaffected. Cholecalciferol supplementation attenuated these deteriorations in blood lipid profiles. RNA-seq analysis was conducted on three cell types that are closely related to maintaining cholesterol metabolic balance (hepatocytes, adipocytes, and C2C12) to verify the direct effects of olanzapine and the functional metabolites of cholecalciferol (calcifediol and calcitriol). Consequently, the expression of cholesterol-biosynthesis-related genes was reduced in calcifediol- and calcitriol-treated C2C12 cells, which was likely to be mediated by activating the vitamin D receptor that subsequently inhibited the cholesterol biosynthesis process via insulin-induced gene 2 regulation. This clinical big-data-based drug repurposing approach is effective in finding a novel treatment with high clinical predictability and a well-defined molecular mechanism.

Increased expression of glutathione peroxidase 3 prevents tendinopathy by suppressing oxidative stress

Tendinopathy, a degenerative disease, is characterized by pain, loss of tendon strength, or rupture. Previous studies have identified multiple risk factors for tendinopathy, including aging and fluoroquinolone use; however, its therapeutic target remains unclear. We analyzed self-reported adverse events and the US commercial claims data and found that the short-term use of dexamethasone prevented both fluoroquinolone-induced and age-related tendinopathy. Rat tendons treated systemically with fluoroquinolone exhibited mechanical fragility, histological change, and DNA damage; co-treatment with dexamethasone attenuated these effects and increased the expression of the antioxidant enzyme glutathione peroxidase 3 (GPX3), as revealed via RNA-sequencing. The primary role of GPX3 was validated in primary cultured rat tenocytes treated with fluoroquinolone or H₂O₂, which accelerates senescence, in combination with dexamethasone or viral overexpression of GPX3. These results suggest that dexamethasone prevents tendinopathy by suppressing oxidative stress through the upregulation of GPX3. This steroid-free approach for upregulation or activation of GPX3 can serve as a novel therapeutic strategy for tendinopathy.

Urinary incontinence as a possible signal of neuromuscular toxicity during immune checkpoint inhibitor treatment: Case report and retrospective pharmacovigilance study

Background

Immune checkpoint inhibitors (ICIs) are associated with different immune-related adverse events (irAEs), but there is limited evidence regarding the association between urinary incontinence and ICIs.

Methods

We described the case of a patient experiencing urinary incontinence who later experienced a series of irAEs such as myocarditis, myositis, and neurologic diseases while on ICI treatment in our hospital. In addition, we queried the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from the third quarter of 2010 to the third quarter of 2020 to perform a retrospective study to characterize the clinical features of urinary incontinence associated with ICIs.

Result

In the FAERS study, 59 cases of ICI-related urinary incontinence were retrieved, and approximately 32.2% of the cases were fatal. Combination therapy with nervous system drugs and age >80 years old were the significant risk factors for fatal outcomes. Among these cases of ICI-related urinary incontinence, 40.7% (n = 24) occurred concomitantly with other adverse events, especially, neurological (fifteen cases), cardiovascular (seven cases), musculoskeletal (six cases), and urological disorders (five cases). Five cases had an overlapping syndrome similar to our case report, including one case of myasthenia gravis with myocarditis and another of myasthenic syndrome with polymyositis.

Conclusion

ICI-related urinary incontinence might be a signal of fatal neuromuscular irAEs, especially when it occurs concomitantly with ICI-associated neuromuscular–cardiovascular syndrome. Clinicians should be aware of the occurrence of urinary incontinence to identify potentially lethal irAEs in the early phase.

Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system

Background: COVID-19 patients with underlying medical conditions are vulnerable to drug-drug interactions (DDI) due to the use of multiple medications. We conducted a discovery-driven data analysis to identify potential DDIs and associated adverse events (AEs) in COVID-19 patients from the FDA Adverse Event Reporting System (FAERS), a source of post-market drug safety.

Materials and Methods: We investigated 18,589 COVID-19 AEs reported in the FAERS database between 2020 and 2021. We applied multivariate logistic regression to account for potential confounding factors, including age, gender, and the number of unique drug exposures. The significance of the DDIs was determined using both additive and multiplicative measures of interaction. We compared our findings with the Liverpool database and conducted a Monte Carlo simulation to validate the identified DDIs.

Results: Out of 11,337 COVID-19 drug-Co-medication-AE combinations investigated, our methods identified 424 signals statistically significant, covering 176 drug-drug pairs, composed of 13 COVID-19 drugs and 60 co-medications. Out of the 176 drug-drug pairs, 20 were found to exist in the Liverpool database. The empirical p-value obtained based on 1,000 Monte Carlo simulations was less than 0.001. Remdesivir was discovered to interact with the largest number of concomitant drugs (41). Hydroxychloroquine was detected to be associated with most AEs (39). Furthermore, we identified 323 gender- and 254 age-specific DDI signals.

Conclusion: The results, particularly those not found in the Liverpool database, suggest a subsequent need for further pharmacoepidemiology and/or pharmacology studies.