Pharmacovigilance

BackgroundGlucagon-like peptide-1 receptor agonists (GLP-1RAs) are widely used due to their profound efficacy in glycemic control and weight management. Real-world observations have revealed potential neuropsychiatric adverse events (AEs) associated with GLP-1RAs. This study aimed to comprehensively investigate and characterize these neuropsychiatric AEs with GLP-1RAs.MethodsWe analyzed GLP-1RA adverse reaction reports using the FDA Adverse Event Reporting System database. Disproportionality analysis using reporting odds ratio (ROR) identified eight categories of neuropsychiatric AEs associated with GLP-1RAs. We conducted descriptive and time-to-onset (TTO) analyses and explored neuropsychiatric AE signals among individual GLP-1RAs for weight loss and diabetes mellitus (DM) indications.ResultsWe identified 25,110 cases of GLP-1RA-related neuropsychiatric AEs. GLP-1RAs showed an association with headache (ROR 1.74, 95% confidence interval [CI] 1.65-1.84), migraine (ROR 1.28, 95%CI 1.06-1.55), and olfactory and sensory nerve abnormalities (ROR 2.44, 95%CI 1.83-3.25; ROR 1.69, 95%CI 1.54-1.85). Semaglutide showed a moderate suicide-related AEs signal in the weight loss population (ROR 2.55, 95%CI 1.97-3.31). The median TTO was 16 days (interquartile range: 3-66 days).ConclusionsIn this study, we identified eight potential neuropsychiatric adverse events (AEs) associated with GLP-1RAs and, for the first time, detected positive signals for migraine, olfactory abnormalities, and sensory abnormalities. We also observed positive suicide-related signals of semaglutide, in weight loss population. This study provides a reliable basis for further investigation of GLP-1RA-related neuropsychiatric AEs. However, as an exploratory study, our findings require confirmation through large-scale prospective studies.

BackgroundOne hundred years after the American Medical Association's report on death from local anaesthetics, their contemporary lethality is not well characterised.MethodsWe investigated local anaesthetic-associated deaths reported to the US Food and Drug Administration Adverse Event Reporting System from 1968 to 2023, comparing adverse events from local anaesthetics with adverse events from other drugs using the reporting odds ratio (ROR). We assessed the impact of professional society practice advisories about systemic toxicity on reporting of mortality from long-acting local anaesthetics and lidocaine. Finally, we extracted individual citations and conducted an exploratory analysis on cases of death.ResultsThere were 22 050 total adverse events from local anaesthetics and 1473 reports of death. Compared with other agents, reports of death were lower for amide (ROR, 0.4; 95% confidence interval [CI], 0.37-0.42) and ester (ROR, 0.12; 95% CI, 0.09-0.16) local anaesthetics. Death from lidocaine was reported most frequently. After practice advisories, reporting of death from long-acting local anaesthetics decreased (ROR, 0.61; 95% CI, 0.46-0.83) compared with the previous 10 yr, whereas reporting of death from lidocaine remained unchanged (ROR, 0.95; 95% CI, 0.78-1.16). Reporting of death from lidocaine was similar to other agents over the past 5 yr (ROR, 0.95; 95% CI, 0.83-1.1). Citations identified high-dose lidocaine, nerve catheters, and high neuraxial anaesthesia as the primary contributors to death since 2010.ConclusionsReporting of mortality from long-acting local anaesthetics decreased in the past decade but remained unchanged for lidocaine. The potential lethality of this ubiquitous local anaesthetic deserves further study.

BackgroundPharmacovigilance is vital for monitoring adverse drug reactions (ADRs) and ensuring drug safety. Traditional methods are slow and inconsistent, but artificial intelligence (AI), through automation and advanced analytics, improves efficiency and accuracy in managing increasing data complexity.AimTo explore AI's practical applications in pharmacovigilance, focusing on efficiency, process acceleration, and task automation. It also examines the use of an expert-defined Bayesian network for causality assessment in a Pharmacovigilance Centre, demonstrating its impact on decision-making.MethodA comprehensive literature narrative review was conducted in MEDLINE (via PubMed), Scopus, and Web of Science using a set of targeted keywords, including but not limited to "pharmacovigilance", "artificial intelligence", "adverse drug reactions" and "drug safety". Relevant studies were analysed without restrictions on publication year or language. The search was carried out in January 2025.ResultsAI has greatly improved pharmacovigilance by streamlining signal detection, surveillance, and ADR reporting automation. Techniques like data mining and automated signal detection have expedited safety signal identification, while duplicate detection has enhanced data precision in safety evaluations. AI has also refined real-world evidence analysis, deepening drug safety and efficacy insights. Predictive models now anticipate ADRs and drug-drug interactions, enabling proactive patient care. At a regional pharmacovigilance center, the implementation of an expert-defined Bayesian network has optimized causality assessment, reducing processing times from days to hours, minimizing subjectivity, and improving the reliability of drug safety evaluations.ConclusionAI holds significant promise for enhancing pharmacovigilance practices, yet its practical application remains primarily confined to academic research, with integration hindered by data quality issues, regulatory barriers, and the need for more transparent algorithms.

ObjectiveCyclophosphamide(CHOP) is a widely used chemotherapeutic agent belonging to the class of alkylating agents. It primarily inhibits the proliferation of tumor cells by interfering with DNA replication and transcription. CHOP has been approved for the treatment of various types of malignant tumors. The aim of this research is to assess adverse events linked to CHOP using real-world data.MethodsThis research collected and analyzed adverse event reports associated with CHOP from the FAERS(US Food and Drug Administration Adverse Event Reporting System) database spanning from the first quarter of 2004 to the third quarter of 2024. This study leveraged the FAERS database, extracting reports submitted exclusively by healthcare professionals where CHOP was designated as the primary suspect (PS). Four risk signal detection methods were employed: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multiitem Gamma Poisson Shrinker (MGPS), to conduct a comprehensive analysis of CHOP-related ADRs.ResultsA total of 3,625 adverse event reports with CHOP as the primary suspect drug were obtained. Across 25 system organ classes (SOCs), four positive Preferred Terms(PTs) signals were detected. The most significant signal in SOCs was "blood and lymphatic system disorders" (ROR = 6.64, 95% CI 6.49-6.79), while the most significant PTs signal was "high grade B-cell lymphoma Burkitt-like lymphoma recurrent" (ROR = 613.63, 95% CI 123.85-3040.40).ConclusionThis study provides compelling evidence of the presence of CHOP seven unexpected off-label AEs were also observed, such as mucoepidermoid carcinoma, hypotelorism of orbit, breakthrough COVID-19, airway remodeling, meningitis enteroviral, acute graft versus host disease, and gastrosplenic fistula. Additionally, the combination of CHOP and radiotherapy increased the risk of several severe AEs, such as hemorrhagic cystitis. These findings highlight the importance of caution when prescribing CHOP to high-risk individuals with a history of meningitis, cystitis or airway obstruction.