FDA Adverse Event Reporting System Research Articles

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

In this study, we introduce a novel combination of layer-static-weighted attention and ascending feature selection techniques to predict the seriousness level of adverse drug events using the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We utilized natural language processing (NLP) to analyze the terms in the active substance field, in addition to considering demographic and event information such as patient sex, healthcare provider qualification, and drug characterization. Our ascending feature selection method, which progressively incorporates additional features based on their importance, demonstrated continuous enhancements in prediction performance. Simultaneously, we employed a layer-static-weighted attention technique, which dynamically adjusts the model’s focus between natural language processing (NLP) and demographic features. This technique achieved its best performance at a balanced weight of 50%, yielding an average test accuracy of 74.56% and CV ROC score of 0.83 when 4000 features were included, indicating a compelling advantage to include a larger volume of meaningful features. By integrating these methodologies, we constructed a robust model capable of effectively predicting seriousness levels, offering significant potential for improving pharmacovigilance and enhancing drug safety monitoring. The results underscore the value of NLP and demographic data in predicting drug event seriousness and demonstrate the effectiveness of our combined techniques. We encourage further research to refine these methods and evaluate their application to other clinical datasets.

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

ABSTRACT 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.

Post-marketing safety concerns with palbociclib: a disproportionality analysis of the FDA adverse event reporting system

ABSTRACT Objectives To explore the association between palbociclib and related adverse events (AEs) in the real world through U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. Methods The signal strength of palbociclib-related AEs was done by disproportionality analysis. Clinical priority of palbociclib-related AEs was scored and ranked by assessing five different features. Outcome analysis, time to onset analysis, dose-report /AEs number analysis, and stratification analysis were all performed. Results There were 61,821 ‘primary suspected (PS)’ reports of palbociclib and 195,616 AEs associated with palbociclib. The four algorithms simultaneously detected 18 positive signals at the SOC level, and 65 positive signals at the PT level. Bone marrow failure, neuropathy, peripheral, pleural effusion, myelosuppression, pulmonary edema, and pulmonary thrombosis were also found to have positive signals. Gender (female vs male, χ2 = 5.287, p = 0.022) and age showed significant differences in serious and non-serious reports. Palbociclib-related AEs had a median onset time of 79 days (interquartile range [IQR] 20–264 days). Conclusions The study identified potential Palbociclib-related AEs and offered warnings for special AEs, providing further data for palbociclib safety studies in breast cancer patients. Nonetheless, prospective clinical trials are needed to validate these results and explain their relationship.

A real‑world pharmacovigilance study of FDA adverse event reporting system events for daratumumab

ABSTRACT Background Daratumumab, a first-in-class humanized IgG1κ monoclonal antibody that targets the CD38 epitope, has been approved for treatment of multiple myeloma by FDA. The current study was to evaluate daratumumab-related adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). Research design and methods Disproportionality analyses, 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) algorithms were employed to quantify the signals of daratumumab-associated AEs. Results Out of 10,378,816 reports collected from the FAERS database, 8727 reports of daratumumab-associated AEs were identified. A total of 183 significant disproportionality preferred terms (PTs) were retained. Unexpected significant AEs such as meningitis aseptic, leukoencephalopathy, tumor lysis syndrome, disseminated intravascular coagulation, hyperviscosity syndrome, sudden hearing loss, ileus and diverticular perforation were also detected. The median onset time of daratumumab-related AEs was 11 days (interquartile range [IQR] 0–76 days), and most of the cases occurred within 30 days. Conclusion Our study found potential new and unexpected AEs signals for daratumumab, suggesting prospective clinical studies are needed to confirm these results and illustrate their relationship.

Investigating bleeding adverse events associated with BTK inhibitors in the food and drug administration adverse event reporting system (FAERS)

ABSTRACT Background This study analyzed the bleeding adverse events (AEs) resulting from the treatment of B-cell lymphoma with Bruton tyrosine kinase (BTK) inhibitors, according to reports in the US Food and Drug Administration’s Adverse Event Reporting System (FAERS) Methods Bleeding AEs associated with BTK inhibitors (including ibrutinib, zanubrutinib, and acalabrutinib) from the first quarter of 2013 to the third quarter of 2023 were extracted. Reporting odds ratio (ROR) and proportional reporting ratio (PRR) were reported. Preferred Terms (PTs) of Medical Dictionary for Regulatory Activities (MedDRA) terms were mapped to System Organ Class terms (SOC) terms and analyzed bleeding AEs associated with three BTK inhibitors. Results A total of 463 cases of bleeding AEs were included. Contusion, subcutaneous hemorrhage, hematuria, and cerebral hemorrhage were included in PTs. Blood urine was present and subdural hematoma were also reported. The incidence of bleeding AEs was higher with ibrutinib (Case number = 10,696) than with zanubrutinib (Case number = 213) and acalabrutinib (Case number = 314). Conclusion Our findings indicate that bleeding AEs linked to BTK inhibitors in various conditions underscore the need for cautious clinical decision-making, particularly in nervous system disorders, injuries, poisoning, surgical complications, vascular disorders, and others.

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

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. 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. 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. 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.

A disproportionality analysis of FDA adverse event reporting system (FAERS) events for ticagrelor.

Ticagrelor is a commonly used antiplatelet agent, but due to the stringent criteria for trial population inclusion and the limited sample size, its safety profile has not been fully elucidated. We utilized OpenVigil 2.1 to query the FDA Adverse Event Reporting System database and retrieved reports by the generic name "ticagrelor" published between 1 October 2010 and 31 March 2023. Adverse drug events (ADEs) were classified and described according to the preferred terms and system organ classes in the Medical Dictionary of Regulatory Activity. Proportional reporting ratio (PRR), reporting odds ratio (ROR) and Bayesian Confidence Propagation Neural Network (BCPNN) were used to detect signals. The number of ADE reports with ticagrelor as the primary suspect drug was 12,909. The top three ADEs were dyspnea [1824 reports, ROR 7.34, PRR 6.45, information component (IC) 2.68], chest pain (458 reports, ROR 5.43, PRR 5.27, IC 2.39), and vascular stent thrombosis (406 reports, ROR 409.53, PRR 396.68, IC 8.02). The highest ROR, 630.24, was found for "vascular stent occlusion". Cardiac arrest (137 reports, ROR 3.41, PRR 3.39, IC 1.75), atrial fibrillation (99 reports, ROR 2.05, PRR 2.04, IC 1.03), asphyxia (101 reports, ROR 23.60, PRR 23.43, IC 4.51), and rhabdomyolysis (57 reports, ROR 2.75, PRR 2.75, IC 1.45) were suspected new adverse events of ticagrelor. The FAERS database produced potential signals associated with ticagrelor that have not been recorded in the package inserts, such as cardiac arrest, atrial fibrillation, asphyxia, and rhabdomyolysis. Further clinical surveillance is needed to quantify and validate potential hazards associated with ticagrelor-related adverse events.

Inverse Association Between Weight-loss Inducing Anti-hyperglycemic Agents and Multiple Sclerosis: Data Mining of the FDA Adverse Event Reporting System Database (P11-6.001)

Inverse Association Between Weight-loss Inducing Anti-hyperglycemic Agents and Multiple Sclerosis: Data Mining of the FDA Adverse Event Reporting System Database (P11-6.001)

Gender Differences in Adverse Events of Ketamine Drugs: A Real-World Study Based on FAERS

Objective. To identify gender differences in the adverse events (AEs) of ketamine, reduce the AEs among patients, and contribute to the advancement of personalized medicine. Methods. A normalized dataset from 2004 Q1 to the 2022 Q4 in the US Food and Drug Administration Adverse Event Reporting System (FAERS) was analysed. The reporting odds ratio (ROR), proportional reporting ratio (PRR), and P value were used to detect the risk signals from the data in the FAERS database and quantify the presence and extent of gender differences in ketamine AEs. Results. Totally, 5,477 ketamine (female/male (2507/1795)) AE reports were analysed, and sedation (ROR 1.30 (1.07, 1.58)), suicidal ideation (ROR 1.30 (1.03, 1.64)), nausea (ROR 1.37 (1.05, 1.78)), depression (ROR 1.22 (1.13, 1.61)), dizziness (ROR 2.25 (1.78, 2.90)), anxiety (ROR 1.48 (1.09, 1.99)), and other adverse events were found to be significantly more frequent in male patients than in female patients. Conclusion. Using FAERS, we identified gender as factors associated with ketamine-related AEs. With the limitations inherent to this open data source, our data need prospective validation but elucidate potential factors for a personalized side effect profiling.

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.

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

ObjectiveAlthough 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. MethodsWe 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. ResultsOut 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. ConclusionThe 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.

CDK4/6 inhibitors in drug-induced liver injury: a pharmacovigilance study of the FAERS database and analysis of the drug-gene interaction network.

The aim of this study was to investigate the potential risk of drug-induced liver injury (DILI) caused by the CDK4/6 inhibitors (CDK4/6is abemaciclib, ribociclib, and palbociclib by comprehensively analyzing the FDA Adverse Event Reporting System (FAERS) database. Moreover, potential toxicological mechanisms of CDK4/6is-related liver injury were explored via drug-gene network analysis. In this retrospective observational study, we collected reports of DILI associated with CDK4/6i use from the FAERS dated January 2014 to March 2023. We conducted disproportionality analyses using the reporting odds ratio (ROR) with a 95% confidence interval (CI). Pathway enrichment analysis and drug-gene network analyses were subsequently performed to determine the potential mechanisms underlying CDK4/6i-induced liver injury. We found positive signals for DILI with ribociclib (ROR = 2.60) and abemaciclib (ROR = 2.37). DILIs associated with liver-related investigations, signs, and symptoms were confirmed in all three reports of CDK4/6is. Moreover, ascites was identified as an unlisted hepatic adverse effect of palbociclib. We isolated 189 interactive target genes linking CDK4/6 inhibitors to hepatic injury. Several key genes, such as STAT3, HSP90AA1, and EP300, were revealed via protein-protein analysis, emphasizing their central roles within the network. KEGG pathway enrichment of these genes highlighted multiple pathways. Our study revealed variations in hepatobiliary toxicity among the different CDK4/6 inhibitors, with ribociclib showing the highest risk of liver injury, followed by abemaciclib, while palbociclib appeared relatively safe. Our findings emphasize the need for cautious use of CDK4/6 inhibitors, and regular liver function monitoring is recommended for long-term CDK4/6 inhibitor use.

Adverse events with pemigatinib in the real world: a pharmacovigilance study based on the FDA Adverse Event Reporting System

ABSTRACT Background To data, there is insufficient large-scale data on the adverse events (AEs) of pemigatinib. Consequently, we conducted a pharmacovigilance study utilizing the Food and Drug Administration Adverse Event Reporting System (FAERS) database to investigate these AEs. Research design and methods The OpenVigil 2.1 was used to extract AE data from the FAERS database. Proportional reporting ratio (PRR), reporting odds ratios (ROR), and bayesian analysis confidence propagation neural network (BCPNN) were used to assess the association between pemigatinib and AEs. The clinical importance of AE signals were prioritized using a rating scale. Results A total of 848 AE reports were retrieved from the FAERS database, and 421 AE reports were identified after the data cleaning process. After accounting for indication bias and removal of medication errors, 59 positive signals were finally included. The 59 positive signals emerged in 11 system organ classes (SOCs). Besides, 19 positive AEs were classified as moderate clinical priority, while 40 were deemed weak in terms of priority. 9 positive AEs were not included in the drug label. Conclusions This study provided valuable evidence for clinicians to mitigate the risk of pemigatinib-related toxicities in the real world.

Association of Antipsychotic Drugs with Venous Thromboembolism: Data Mining of Food and Drug Administration Adverse Event Reporting System and Mendelian Randomization Analysis.

Past observational studies have reported on the association between antipsychotic drugs and venous thromboembolism (VTE); however, the conclusions remain controversial, and its mechanisms are yet to be fully understood. Thus, in this study, we aim to determine the associations of antipsychotic drugs with VTE, including deep vein thrombosis (DVT) and pulmonary embolism (PE), and their potential mechanisms. We first mined the adverse event signals of VTE, DVT, and PE caused by antipsychotic drugs in Food and Drug Administration Adverse Event Reporting System (FAERS). Next, we used two-sample Mendelian randomization (MR) method to investigate the association of antipsychotic drug target gene expression with VTE, DVT, and PE, using single-nucleotide polymorphisms as genetic instruments. We not only used the expression of all antipsychotic drug target genes as exposure to perform MR analyses but also analyzed the effect of single target gene expression on the outcomes. In the FAERS, 1694 cases of VTE events were reported by 16 drugs. However, using the MR approach, no significant association was determined between the expression of all antipsychotic target genes and VTE, DVT, or PE, either in blood or brain tissue. Although the analysis of single gene expression data showed that the expression of nine genes was associated with VTE events, these targets lacked significant pharmacological action. Adverse event mining results have supported the claim that antipsychotic drugs can increase the risk of VTE. However, we failed to find any genetic evidence for this causal association and potential mechanisms. Thus, vigilance is still needed for antipsychotic drug-related VTE despite the limited supporting evidence.

Relationship Between Clozapine and Non-Hematological Malignant Tumors: A Pharmacovigilance Analysis Using the FDA Adverse Event Reporting System Database.

Clozapine shows higher efficacy against treatment-resistant schizophrenia than other antipsychotics. This study aimed to investigate whether clozapine is associated with the risk of non-hematological malignant tumors, utilizing the US Food and Drug Administration (FDA) Adverse Event Report System (FAERS) database. The records from the first quarter of 2004 to the third quarter of 2012 were used for disproportionality analysis, and patients who developed non-hematological malignant tumors were identified by the Standardized Medical Dictionary for Regulatory Activities Queries (SMQ). Of the 3,641,281 patients with 12,401,586 reports of adverse drug events, 151,904 reports belonged to non-hematological malignant tumors (SMQ). We identified 1668 reports of non-hematological malignant tumors (SMQ) in clozapine users, and the reporting odds ratio (ROR) was calculated to be 1.28 (95% confidence interval (CI): 1.22-1.34). ROR (95% CI) for the relationship between clozapine and the risk of testis cancer was calculated as 10.94 (6.99-17.12), 9.87 (7.42-13.15) for gastrointestinal carcinoma, 7.48 (5.57-10.05) for metastatic lung cancer, 6.71 (4.52-9.97) for throat cancer, 6.12 (4.56-8.21) for metastases to the spine, 5.97 (5.30-6.72) for lung malignant neoplasm, 5.07 (3.69-6.95) for esophageal carcinoma, 1.88 (1.43-2.47) for colon cancer, and 1.65 (1.24-2.21) for metastases to the liver. Colon cancer, esophageal carcinoma, and throat cancer were predominantly reported in males, and metastases to the spine and liver were in females. This study detected signals indicating a relationship between clozapine and certain non-hematological malignant tumors, utilizing the FAERS database. Despite the database relying on spontaneous reporting, the current results justify further investigation.

The association of hypophysitis with immune checkpoint inhibitors use: Gaining insight through the FDA pharmacovigilance database.

The use of immune checkpoint inhibitor (ICI) marked a revolutionary change in cancer treatment and opened new avenues for cancer therapy, but ICI can also trigger immune-related adverse events (irAEs). Here, we investigated the publicly available US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database to gain insight into the possible association between immune checkpoint inhibitors and hypophysitis. Data on adverse events (AEs) due to hypophysitisfor nivolumab, pembrolizumab, ipilimumab, and atezolizumab were collected from the US FDA Adverse Event Reporting System from the first quarter of 2004 to the second quarter of 2021, and the signals for hypophysitis associated with the four drugs were examined using the reporting odds ratio (ROR) method. The number of reported hypophysitis events ≥ 3 and the lower limit of the 95% confidence interval (CI) of the ROR > 1 were considered positive for hypophysitis signals. A total of 1252 AE reports of hypophysitis associated with nivolumab, pembrolizumab, ipilimumab, and atezolizumab were collected, including 419, 149, 643, and 41 cases, respectively. The RORs of hypophysitis were 289.58 (95% CI 258.49-324.40), 171.74 (95% CI 144.91-203.54), 2248.57 (95% CI 2025.31-2496.45), and 97.29 (95% CI 71.28-132.79), respectively. All four drugs were statistically correlated with the target AE, with the correlation being, in descending order, ipilimumab, nivolumab, pembrolizumab, and atezolizumab. Nivolumab, pembrolizumab, ipilimumab, and atezolizumab have all been associated with hypophysitis, which can negatively impact quality of life, and early recognition and management of immune checkpoint inhibitor-related hypophysitis is critical.

Gastrointestinal infections and gastrointestinal haemorrhage are underestimated but serious adverse events in chimeric antigen receptor T-cell recipients: A real-world study.

Chimeric antigen receptor T-cell (CAR-T) therapy has achieved durable response in patients with hematological malignancies, however, therapy-associated multisystem toxicities are commonly observed. Here, we systematically analyzed CAR-T-related gastrointestinal adverse events (GAEs) using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) between January 2017 and December 2021. Disproportionality analyses were performed using reporting odds ratios (ROR) and information component (IC). Among 105,087,611 reports in FAERS, 1518 CAR-T-related GAEs reports were identified. 23 GAEs (n = 281, 18.51%) were significantly overreported following CAR-T therapy compared with the full database, of which 11 GAEs (n = 156, 10.28%) were associated with gastrointestinal infections (GI), such as clostridium difficile colitis (n = 44 [2.90%], ROR = 5.55), enterovirus infection (n = 23 [1.52%], ROR = 20.02), and mucormycosis (n = 15 [0.99%], ROR = 3.09). Overall, the fatality rate of 11 GI-related AEs was 29.49%, especially mucormycosis causing substantial mortality with 60%. In addition, 4 of 23 overreported GAEs were related to haemorrhage and the mortality of gastrointestinal haemorrhage was 73.17%. Lastly, 29 death-related GAEs were identified. These findings could help clinicians early alert those rarely reported but lethal GAEs, thus reducing the risk of severe toxicities.

Abstract 6380: Exploring immune checkpoint inhibitor-related adverse events in pediatric cancer patients: A pharmacovigilance analysis of VigiBase and the FDA adverse event reporting system (FAERS) database

Abstract Background: With the widespread use of immune checkpoint inhibitors (ICIs) in the treatment of childhood cancers, their associated ICIs-related adverse events (irAEs) have become an important challenge. Our study was conducted to explore the pediatric high-risk ICIs-related AEs (pediatric irAEs) and explore the underlying mechanisms of pediatric irAEs. Methods: We analyzed ICIs-related adverse reaction reports in VigiBase from 1967 to 2023 and the FDA Adverse Event Reporting System (FAERS) database from 2013 to 2022. Using three methods, the reporting odds ratio (ROR), the proportional reporting ratio (PRR), and the information component (IC), we conducted a disproportionality analysis between pediatric and adult cancer patients using ICIs to discover pediatric irAEs. Furthermore, we explored the impact of clinical factors on the occurrence of pediatric irAEs and their subsequent prognosis through univariate and multivariate logistic regression analyses. Finally, combining these data with pan-cancer transcriptome data from the Gene Expression Omnibus (GEO) database, we explored the potential biological mechanisms related to pediatric irAEs. Results: We identified 4 categories of pediatric irAEs in the VigiBase and FAERS databases, including cytokine release syndrome (RORVigiBase = 17.05; RORFAERS = 14.17), acute respiratory distress syndrome (RORVigiBase = 10.26; RORFAERS = 12.39), seizure (RORVigiBase = 7.18; RORFAERS = 10.63) and febrile neutropenia (FN) (RORVigiBase = 3.01; RORFAERS = 4.84). The occurrence of pediatric irAEs may be related to mechanisms such as activation of immune cells, imbalance of inflammatory factors, activation of pathogen recognition, downregulation of inhibitory synapses, excitation/inhibition imbalance and central nervous system dysfunction. Conclusion: This study systematically analyzed the high-risk irAEs that may occur after using ICIs in pediatric cancer patients. Next, we explored the possible biological mechanisms of these pediatric irAEs to provide certain theoretical guidance for the prevention and treatment of adverse reactions in pediatric cancer patients after treatment with ICIs. Citation Format: Junyi Shen, Anqi Lin, Quan Cheng, Jian Zhang, Peng Luo. Exploring immune checkpoint inhibitor-related adverse events in pediatric cancer patients: A pharmacovigilance analysis of VigiBase and the FDA adverse event reporting system (FAERS) database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6380.

Abstract 7169: A real-world toxicity-atlas shows that adverse events of combination-therapies commonly result in additive interactions

Abstract Purpose: Combination-therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in an real-world setting. Experimental Design: We provide here a proof-of-concept study using 15 million patient records from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Complex adverse-event frequencies of drugs or their combinations were visualized as heatmaps onto a 2D UMAP 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 heatmaps. Additionally, statistical synergy analyses were performed based on BLISS independence or Chi-square 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. Conclusion: Our study provides a framework for analyzing adverse events and suggest 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. Citation Format: Asli Küçükosmanoglu, Silvia I. Scoarta, Bart Westerman. A real-world toxicity-atlas shows that adverse events of combination-therapies commonly result in additive interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7169.

Real-world safety of Levetiracetam: Mining and analysis of its adverse drug reactions based on FAERS database

Levetiracetam is a relatively new and widely utilized anti-seizure medication; however, limited information is available regarding its adverse effects. This study aims to thoroughly investigate, evaluate, and present evidence on the safety profile of Levetiracetam, relying on data from the FDA Adverse Event Reporting System (FAERS) database to facilitate informed clinical decision-making. We employed various statistical measures, including Reporting Odds Ratio (ROR), Proportionate Reporting Ratio (PRR), and analysis by the Medicines and Healthcare Products Regulatory Agency (MHRA), to identify signals of adverse reactions associated with Levetiracetam. Positive signals consistent with Designated Medical Event (DME) were singled out for focused comparison and discussion. The analysis of 26,182 adverse events linked to Levetiracetam as the primary suspected drug revealed 692 positive signals spanning 22 System Organ Classes (SOCs). Nervous system disorders were the most frequently reported, followed by psychiatric disorders, and general disorders and administration site conditions. 11 positive signals consistent with Preferred Terms (PTs) in DME were identified, predominantly concentrated in 6 SOCs. Among these, rhabdomyolysis, Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), drug reaction with eosinophilia and systemic symptoms (DRESS) exhibited relatively large values of A, ROR, and Chi-squared. Additionally, PTs related to spontaneous abortion, drug interaction, urethral atresia, ventricular septal defect, and atrial septal defect showed significant strength. The study indicates that Levetiracetam carries a potential risk of causing rhabdomyolysis, SJS, TEN, DRESS as well as spontaneous abortion. Signals related to drug interaction, urethral atresia, ventricular septal defect, and atrial septal defect warrant heightened attention in clinical use.