Safety analysis of Oseltamivir and Baloxavir Marboxil after market approval: a pharmacovigilance study based on the FDA adverse event reporting system.

BACKGROUND AND OBJECTIVES: Amidst limited influenza treatment options, evaluating the safety of Oseltamivir and Baloxavir Marboxil is crucial, particularly given their comparable efficacy. This study investigates post-market safety profiles, exploring adverse events (AEs) and their drug associations to provide essential clinical references. METHODS: A meticulous analysis of FDA Adverse Event Reporting System (FAERS) data spanning the first quarter of 2004 to the fourth quarter of 2022 was conducted. Using data mining techniques like reporting odds ratio (ROR), proportional reporting ratio, Bayesian Confidence Propagation Neural Network, and Multiple Gamma Poisson Shrinkage, AEs related to Oseltamivir and Baloxavir Marboxil were examined. Venn analysis compared and selected specific AEs associated with each drug. RESULTS: Incorporating 15,104 Oseltamivir cases and 1,594 Baloxavir Marboxil cases, Wain analysis unveiled 21 common AEs across neurological, psychiatric, gastrointestinal, dermatological, respiratory, and infectious domains. Oseltamivir exhibited 221 significantly specific AEs, including appendicolith [ROR (95% CI), 459.53 (340.88 ∼ 619.47)], acne infantile [ROR (95% CI, 368.65 (118.89 ∼ 1143.09)], acute macular neuroretinopathy [ROR (95% CI), 294.92 (97.88 ∼ 888.64)], proctitis [ROR (95% CI), 245.74 (101.47 ∼ 595.31)], and Purpura senile [ROR (95% CI), 154.02 (81.96 ∼ 289.43)]. designated adverse events (DMEs) associated with Oseltamivir included fulminant hepatitis [ROR (95% CI), 12.12 (8.30-17.72), n=27], ventricular fibrillation [ROR (95% CI), 7.68 (6.01-9.83), n=64], toxic epidermal necrolysis [ROR (95% CI), 7.21 (5.74-9.05), n=75]. Baloxavir Marboxil exhibited 34 specific AEs, including Melaena [ROR (95% CI), 21.34 (14.15-32.18), n = 23], cystitis haemorrhagic [ROR (95% CI), 20.22 (7.57-54.00), n = 4], ileus paralytic [ROR (95% CI), 18.57 (5.98-57.71), n = 3], and haemorrhagic diathesis [ROR (95% CI), 16.86 (5.43-52.40)), n = 3]. DMEs associated with Baloxavir Marboxil included rhabdomyolysis [ROR (95% CI), 15.50 (10.53 ∼ 22.80), n = 26]. CONCLUSION: Monitoring fulminant hepatitis during Oseltamivir treatment, especially in patients with liver-related diseases, is crucial. Oseltamivir's potential to induce abnormal behavior, especially in adolescents, necessitates special attention. Baloxavir Marboxil, with lower hepatic toxicity, emerges as a potential alternative for patients with liver diseases. During Baloxavir Marboxil treatment, focused attention on the occurrence of rhabdomyolysis is advised, necessitating timely monitoring of relevant indicators for those with clinical manifestations. The comprehensive data aims to provide valuable insights for clinicians and healthcare practitioners, facilitating an understanding of the safety profiles of these influenza treatments in real-world scenarios.

Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance.

This new editorial discusses the promise and challenges of successful integration of natural language processing methods into electronic health records for timely, robust, and fair oncology pharmacovigilance.

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.

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Introduction: Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Method: Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. Result: The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. Discussion: This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.

BACKGROUND: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. OBJECTIVE: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. METHODS: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. RESULTS: The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. CONCLUSIONS: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

The Association between PDE5 Inhibitors and Aneurysm/Arterial Dissection:A Pharmacovigilance Study Using WHO Safety Database.

Aneurysm and arterial dissection have been reported as adverse drug events, associated with angiogenesis inhibitors and fluoroquinolones. Specifically, several cases of severe arterial disease following cGMP-specific phosphodiesterase type 5 (PDE5) inhibitors usage have recently been reported. It is necessary to ascertain the risks of serious adverse events caused by PDE5 inhibitors. We aimed to evaluate the association of aneurysm and artery dissection with PDE5 inhibitors using VigiBase, which is a World Health Organization database of spontaneously reported adverse events, for explorative hypothesis-generating analysis. We performed disproportionality analysis using a dataset from inception in 1967 to December 2022 and calculated reporting odds ratios (ROR) between PDE5 inhibitors and arterial diseases. We extracted 195,839 reports on PDE5 inhibitors with 254 reports of arterial disease as adverse events from VigiBase. Disproportionality analysis showed disproportional signals for PDE5 inhibitors (ROR, 2.30;95% confidence intervals, 2.04-2.61);disproportional signals were detected in analyses restricting the lesion site to the aorta or cerebral arteries. From stratified analysis, disproportional signals were noted in females, as well as males, generally recognized as a risk factor for artery diseases. This real-world data analysis suggests that PDE5 inhibitors may play a role in the development of lethal arterial disease. J. Med. Invest. 71 : 134-140, February, 2024.

Navigating duplication in pharmacovigilance databases: a scoping review.

OBJECTIVES: Pharmacovigilance databases play a critical role in monitoring drug safety. The duplication of reports in pharmacovigilance databases, however, undermines their data integrity. This scoping review sought to provide a comprehensive understanding of duplication in pharmacovigilance databases worldwide. DESIGN: A scoping review. DATA SOURCES: Reviewers comprehensively searched the literature in PubMed, Web of Science, Wiley Online Library, EBSCOhost, Google Scholar and other relevant websites. ELIGIBILITY CRITERIA: Peer-reviewed publications and grey literature, without language restriction, describing duplication and/or methods relevant to duplication in pharmacovigilance databases from inception to 1 September 2023. DATA EXTRACTION AND SYNTHESIS: We used the Joanna Briggs Institute guidelines for scoping reviews and conformed with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Two reviewers independently screened titles, abstracts and full texts. One reviewer extracted the data and performed descriptive analysis, which the second reviewer assessed. Disagreements were resolved by discussion and consensus or in consultation with a third reviewer. RESULTS: We screened 22 745 unique titles and 156 were eligible for full-text review. Of the 156 titles, 58 (47 peer-reviewed; 11 grey literature) fulfilled the inclusion criteria for the scoping review. Included titles addressed the extent (5 papers), prevention strategies (15 papers), causes (32 papers), detection methods (25 papers), management strategies (24 papers) and implications (14 papers) of duplication in pharmacovigilance databases. The papers overlapped, discussing more than one field. Advances in artificial intelligence, particularly natural language processing, hold promise in enhancing the efficiency and precision of deduplication of large and complex pharmacovigilance databases. CONCLUSION: Duplication in pharmacovigilance databases compromises risk assessment and decision-making, potentially threatening patient safety. Therefore, efficient duplicate prevention, detection and management are essential for more reliable pharmacovigilance data. To minimise duplication, consistent use of worldwide unique identifiers as the key case identifiers is recommended alongside recent advances in artificial intelligence.

A real­world pharmacovigilance study of FDA adverse event reporting system events for daratumumab.

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.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

BACKGROUND: The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. METHODS: We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. RESULTS: Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. CONCLUSION: The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.

Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate manual annotations significantly hinders the performance of machine learning algorithms. In this study, a new DDI prediction framework, Subgraph Enhance model, was developed for DDI (SubGE-DDI) to improve the performance of machine learning algorithms. This model uses drug pairs knowledge subgraph information to achieve large-scale plain text prediction without many annotations. This model treats DDI prediction as a multi-class classification problem and predicts the specific DDI type for each drug pair (e.g. Mechanism, Effect, Advise, Interact and Negative). The drug pairs knowledge subgraph was derived from a huge drug knowledge graph containing various public datasets, such as DrugBank, TwoSIDES, OffSIDES, DrugCentral, EntrezeGene, SMPDB (The Small Molecule Pathway Database), CTD (The Comparative Toxicogenomics Database) and SIDER. The SubGE-DDI was evaluated from the public dataset (SemEval-2013 Task 9 dataset) and then compared with other state-of-the-art baselines. SubGE-DDI achieves 83.91% micro F1 score and 84.75% macro F1 score in the test dataset, outperforming the other state-of-the-art baselines. These findings show that the proposed drug pairs knowledge subgraph-assisted model can effectively improve the prediction performance of DDIs from biomedical texts.

A real-world pharmacovigilance study of amivantamab-related cardiovascular adverse events based on the FDA adverse event reporting system (FAERS) database.

Amivantamab is the first dual-specificity antibody targeting EGFR and MET, which is approved for the treatment of locally advanced or metastatic non-small cell lung cancer (NSCLC) with EGFR exon 20 insertion mutations. Cardiovascular toxicities related to amivantamab have not been reported in the CHRYSALIS study. However, the occurrence of cardiovascular events in the real world is unknown. To comprehensively investigate the clinical characteristics, onset times, and outcomes of cardiovascular toxicities associated with amivantamab. The Food and Drug Administration Adverse Event Reporting System (FAERS) database from 1st quarter of 2019 to the 2nd quarter of 2023 was retrospectively queried to extract reports of cardiovascular adverse events (AEs) associated with amivantamab. To perform disproportionality analysis, the reporting odds ratios (RORs) and information components (ICs) were calculated with statistical shrinkage trans-formation formulas and a lower limit of the 95% confidence interval (CI) for ROR (ROR025) > 1 or IC (IC025) > 0 with at least 3 reports was considered statistically significant. A total of 20,270,918 eligible records were identified, among which 98 records were related to cardiovascular events associated with amivantamab. 4 categories of cardiovascular events exhibited positive signals: venous thrombotic diseases, abnormal blood pressure, arrhythmia, and pericardial effusion. Venous thrombotic diseases and abnormal blood pressure were the two most common signals. The median time to onset (TTO) for cardiovascular AEs was 33 days. The cumulative incidence within 90 days was 100% for cardiac failure, 75% for stroke, 63.16% for arrhythmia, 50% for sudden death, and 44.18% for venous thrombotic diseases. Death accounted for 16.3% of all cardiovascular AEs associated with amivantamab. The mortality rates for Major Adverse Cardiovascular Events (MACE) were up to 60%. This pharmacovigilance study systematically explored the cardiovascular adverse events of amivantamab and provided new safety signals based on past safety information. Early and intensified monitoring is crucial, and attention should be directed towards high-risk signals.

Global burden of vaccine-associated multiple sclerosis, 1967-2022: A comprehensive analysis of the international pharmacovigilance database.

Vaccine-associated multiple sclerosis (MS) is rare, with insufficient evidence from case reports. Given the scarcity of large-scale data investigating the association between vaccine administration and adverse events, we investigated the global burden of vaccine-associated MS and potential related vaccines from 1967 to 2022. Reports on vaccine-associated MS between 1967 and 2022 were obtained from the World Health Organization International Pharmacovigilance Database (total number of reports = 120 715 116). We evaluated global reports, reporting odds ratio (ROR), and information components (IC) to investigate associations between 19 vaccines and vaccine-associated MS across 156 countries and territories. We identified 8288 reports of vaccine-associated MS among 132 980 cases of all-cause MS. The cumulative number of reports on vaccine-associated MS gradually increased over time, with a substantial increase after 2020, owing to COVID-19 mRNA vaccine-associated MS. Vaccine-associated MS develops more frequently in males and adolescents. Nine vaccines were significantly associated with higher MS reporting, and the highest disproportional associations were observed for hepatitis B vaccines (ROR 19.82; IC025 4.18), followed by encephalitis (ROR 7.42; IC025 2.59), hepatitis A (ROR 4.46; IC025 1.95), and papillomavirus vaccines (ROR 4.45; IC025 2.01). Additionally, MS showed a significantly disproportionate signal for COVID-19 mRNA vaccines (ROR 1.55; IC025 0.52). Fatal clinical outcomes were reported in only 0.3% (21/8288) of all cases of vaccine-associated MS. Although various vaccines are potentially associated with increased risk of MS, we should be cautious about the increased risk of MS following vaccination, particularly hepatitis B and COVID-19 mRNA vaccines, and should consider the risk factors associated with vaccine-associated MS.

Rising cases of drug-induced pulmonary fibrosis: Analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS) database, 2000-2022.

PURPOSE: Pulmonary fibrosis (PF) is a severe, progressive disease, which may be caused by exposure to certain medications. METHODS: We queried the U.S. FDA Adverse Event Reporting System (FAERS) from 2000 to 2022, using the search terms "pulmonary fibrosis" and "idiopathic pulmonary fibrosis" and excluded reports with patients under the age of 18 years, and patients with unknown sex or age. Reports were sorted by generic drug names, counted, and plotted over time using a best-fit trendline based on an exponential function. RESULTS: From 2000 to 2022, there were 24 095 935 adverse drug events reported in FAERS, of which 17 520 (0.07%) were reported as PF. After excluding reports containing patients with unknown age (5255, 30%), sex (122, 0.7%), and age below 18 years old (155, 0.9%), our study included 11 988 reports. The mean age of the study sample was 66.5 ± 13.1 years, and 6248 patients (52.1%) were male. Plotting the 11 988 reports by year revealed an exponential best fit line (R2 = 0.88) with a positive slope over time. The top five drug classes associated with PF were disease modifying antirheumatic drugs (DMARDs, 39.4%), antineoplastic agents (26.4%), cardiovascular agents (12.6%), corticosteroids (4.6%), and immunosuppressive agents (4.0%). CONCLUSION: A 23-year analysis of the FAERS database revealed exponentially increasing adverse event reports of PF. Significant annual increases in reporting of PF suspected with DMARDs and antineoplastic agents were identified. Our study highlights important trends, which should be used to guide PF research related to drugs of potential importance.

Hemorrhage profile associated with immune checkpoint inhibitors: a systematic review and a real-world study based on the FAERS database.

OBJECTIVES: To investigate the risk of hemorrhage associated with Immune Checkpoint Inhibitors (ICIs) and characterize its clinical features. METHODS: We systematically reviewed randomized clinical trials (RCTs) of hemorrhage related to ICIs and calculated odds ratios (ORs) with 95% confidence intervals (CIs). Pharmacovigilance studies were conducted by collecting ICIs-related hemorrhage cases from the FAERS database and assessing disproportionalities by reporting odds ratios (RORs) and information components (ICs). RESULTS: A total of 79 RCTs involving 45,100 patients were finally included in the systematic review, with four published RCTs (n = 1965) and 75 unpublished RCTs (n = 43135). The primary analysis showed no significant difference in ICIs compared to the control group (OR 1.18 [95% CI 1.00-1.38], p = 0.05). In subgroup analyses, anti-PD-L1 combined with anti-CTLA-4 increased the risk of hemorrhage (OR 1.95, p = 0.03), and anti-CTLA-4 increased the risk of hemorrhage in the gastrointestinal system (OR 2.23, p = 0.04). 3555 cases of hemorrhage from the FAERS database were included in the disproportionate analysis, and the result suggested that ICIs increased the risk of hemorrhage (IC025 = 0.23). CONCLUSION: Our study suggests that ICIs increase the risk of hemorrhage, and in particular, anti-CTLA-4 significantly increases the risk of hemorrhage in the gastrointestinal system.

Pharmacovigilance Concept Knowledge, Perspectives and Attitudes: A Cross-Sectional Study Among Community Pharmacists.

The concept of pharmacovigilance (PV) is currently highlighted after emergency authorization and worldwide distribution of the urgently launched COVID-19 novel vaccinations. As they typically serve as the initial point of patient contact for medication-related issues, understanding the knowledge, perspectives, and attitudes of community pharmacists in PV and reporting adverse drug reactions (ADRs) is crucial to improving the healthcare system and public health policies. However, previous studies in Jordan have not focused entirely on community pharmacists. This study aimed to assess community pharmacists' knowledge, perspectives, and attitudes on PV and ADRs reporting in Jordan. The applied methodology in this study was based on a cross-sectional study design using a validated questionnaire distributed to a convenient sample of Jordanian community pharmacists. Seventeen questions were designed from different pieces of literature relating to knowledge, perspectives, and attitudes of PV among community pharmacists. Descriptive statistics (frequencies and percentages) were used to report the results data. The study questionnaire was completed by 180 of 325 community pharmacists willing to participate (a response rate of 55.4%). Of them (n = 132, 73%) were aware of the concept of PV. Additionally (n = 84, 47%) of the community pharmacists would use the concept and policy of PV in their everyday work. Nevertheless, only (n = 36, 20.0%) of the community pharmacists thought an ADR should be reported if seen, and approximately 120 pharmacists (67.0%) believed it was essential to report ADRs as patient health matters. Although community pharmacists in Jordan showed a considerable awareness level of PV, they demonstrated a low level of its application. Thus, ADR reporting is not considered a mainstay among them, and the implementation of PV is not yet addressed. The results from this study shed light on community pharmacists' perceptions and attitudes regarding ADR reporting and PV.

Time-to-onset Analysis of Rhabdomyolysis due to Different Proton Pump Inhibitors Using a Pharmacovigilance Database.

BACKGROUND/AIM: Recent research has increasingly demonstrated an association between proton pump inhibitors (PPIs) and serious adverse events. This study aimed to evaluate the association between PPI and rhabdomyolysis (RM), examining its time-to-onset profiles using the Japanese Adverse Drug Event Report (JADER) database. PATIENTS AND METHODS: Data spanning from April 2004 to March 2022 were used. The association between PPIs and RM was evaluated using the reporting odds ratio (ROR), adjusted for sex and age. Subsequent analyses were conducted after excluding cases involving concomitant use of statins or fibrates. Furthermore, the onset time of RM and Weibull distribution parameters were calculated to evaluate the expression profile of RM, and the outcomes were examined. RESULTS: RM was associated with the use of esomeprazole, omeprazole, and rabeprazole, even in the absence of concomitant statin or fibrate use. The median time to RM onset varied among PPIs, ranging from 6.5 to 127 d. The Weibull distribution parameters indicated that the hazard types of nearly all orally administered PPIs were classified as early failure or close to random failure. Regarding outcomes, cases of death were reported for all PPIs except vonoprazan. CONCLUSION: The findings suggest the need for vigilant monitoring of RM during PPI administration, particularly in the early stages, considering the varying onset times.

Risk and Time-to-Onset of Acute Kidney Injury With Vancomycin Plus Piperacillin-tazobactam Combination: Analysis Using JADER.

BACKGROUND/AIM: Pharmacovigilance data and clinical studies have indicated a risk of acute kidney injury (AKI) associated with concomitant administration of vancomycin and piperacillin-tazobactam. However, no pharmacovigilance studies have evaluated time-to-onset and outcomes of AKI related to this combination. Therefore, this study used a pharmacovigilance database to investigate the incidence, time-to-onset, and outcomes of AKI in patients treated with intravenous vancomycin plus piperacillin-tazobactam or other antipseudomonal antibiotics. PATIENTS AND METHODS: From data in the Japanese Adverse Drug Event Report (JADER) database, we calculated the reporting odds ratios (RORs) and 95% confidence intervals (CIs), time-to-onset, and outcomes of AKI following intravenous administration of vancomycin plus piperacillin-tazobactam or other antipseudomonal antibiotics and with other vancomycin regimens, including monotherapy. RESULTS: The JADER database contained 4,471 reports of intravenous vancomycin treatment, including 517 reports of AKI. The adjusted RORs (95%CIs) of AKI in cases with co-administration of intravenous vancomycin and piperacillin-tazobactam was 2.58 (2.06-3.24). The median time-to-onset for AKI in vancomycin plus piperacillin-tazobactam was 6.0 (interquartile range=3.0-10.3). Weibull shape parameter analysis showed that the pattern of onset of AKI in vancomycin plus piperacillin-tazobactam represented a wear out failure, predicting an increasing hazard with time. For the outcome of AKI, there was no significant difference between all vancomycin regimen and the piperacillin-tazobactam combination groups. CONCLUSION: Concomitant use of intravenous vancomycin and piperacillin-tazobactam may increase the incidence of AKI but may not affect the outcome. This combination does not necessarily have to be avoided, but long-term use is not advisable.

Comparative analysis of adverse drug reactions associated with new antiseizure medications from the Korea Adverse Event Reporting System database.

OBJECTIVE: This study aimed to compare and characterize the safety profiles of new antiseizure medications (ASMs) using a nationwide pharmacovigilance database from a long-term perspective in Korea. METHODS: We reviewed adverse event reports from the Korea Adverse Event Reporting System database between January 2013 and December 2022 for descriptive analysis of six new ASMs (lacosamide, levetiracetam, lamotrigine, oxcarbazepine, topiramate, and zonisamide). We investigated the frequency and characteristics of adverse drug reactions (ADRs) based on the MedDRA terminology, system organ classes, and modified WHO classification. RESULTS: We identified 5,733 reported cases of ADRs. The commonly reported ADRs associated with total ASMs were rash/urticaria (1,822, 31.8 %), dizziness (409, 7.1 %), somnolence/drowsiness (311, 5.4 %), and hepatotoxic effects (273, 4.8 %). Type B (idiosyncratic) effects (2,932; 51.1 %) were more commonly reported than Type A (related to known drug mechanisms) effects (2,613; 45.6 %). Skin and subcutaneous tissue disorders and type B effects were most commonly reported for lamotrigine and oxcarbazepine, whereas nervous system disorders and type A effects were most commonly reported for lacosamide, topiramate, and zonisamide. The pediatric group (<18 years) exhibited skin and subcutaneous tissue disorders and type B effects relatively more frequently than the adult and older adult groups. CONCLUSION: Hypersensitivity skin reactions and type B effects remained significant ADRs in the new ASMs; however, type A effects were more commonly reported in some ASMs. The pediatric group showed a higher rate of type B effects. Overall, new ASMs should also be used with caution.

Cross-sectional study of pharmacovigilance knowledge, attitudes, and practices based on structural equation modeling and network analysis: a case study of healthcare personnel and the public in Yunnan Province.

Background: This study focuses on understanding pharmacovigilance knowledge, attitudes, and practices (KAP) in Yunnan Province, employing Structural Equation Modeling (SEM) and network analysis. It aims to evaluate the interplay of these factors among healthcare personnel and the public, assessing the impact of demographic characteristics to inform policy and educational initiatives. Methods: A cross-sectional survey was conducted in Yunnan, targeting healthcare personnel and the public. Data collection was through questionnaires, with subsequent analysis involving correlation matrices, network visualization, and SEM. The data analysis utilized SPSS 27.0, AMOS 26.0, and Gephi software for network analysis. Results: This study evaluated pharmacovigilance KAP among 209 public participants and 823 healthcare personnel, uncovering significant differences. Public respondents scored averages of 4.62 ± 2.70 in knowledge, 31.99 ± 4.72 in attitudes, and 12.07 ± 4.96 in practices, while healthcare personnel scored 4.38 ± 3.06, 27.95 ± 3.34, and 7.75 ± 2.77, respectively. Statistically significant correlations across KAP elements were observed in both groups, highlighting the interconnectedness of these factors. Demographic influences were more pronounced among healthcare personnel, emphasizing the role of professional background in pharmacovigilance competency. Network analysis identified knowledge as a key influencer within the pharmacovigilance KAP network, suggesting targeted education as a vital strategy for enhancing pharmacovigilance engagement. Conclusion: The research reveals a less-than-ideal state of pharmacovigilance KAP among both healthcare personnel and the public in Yunnan, with significant differences between the two groups. SEM and network analysis confirmed a strong positive link among KAP components, moderated by demographics like age, occupation, and education level. These insights emphasize the need to enhance pharmacovigilance education and awareness, thereby promoting safer drug use.