Olmesartan-Induced Enteropathy: Case Reports and Insights From FDA Adverse Event Reporting System (FAERS).

Olmesartan, an angiotensin II receptor blocker (ARB) approved in 2002, is used to treat hypertension, either alone or with other antihypertensive drugs. It has been frequently associated with sprue-like enteropathy, while few cases of colitis have been reported. Differentiating between sprue-like enteropathy and colitis is of clinical concern, since just the first condition is a well-documented adverse event of olmesartan.

Artificial intelligence in pharmacovigilance signal management: a review of tools, implementations, research, and regulatory landscape.

Introduction: The integration of artificial intelligence (AI) into pharmacovigilance (PV) has advanced rapidly in recent years. AI tools have the potential to transform signal management by enabling faster and more accurate signal management and decision-making. However, the regulatory landscape governing these technologies remains complex.

NAT2 Acetylation Status Predicts Hepatotoxicity During Antituberculosis Therapy: Cumulative Risk Analysis of a Multiethnic Cohort.

Antituberculosis drug-induced hepatotoxicity (ATDH) is a common adverse drug reaction often requiring treatment interruption, complicating tuberculosis management. The slow acetylator phenotype, characterized by reduced N-acetyltransferase 2 (NAT2) enzyme activity, is associated with increased hepatotoxicity risk, while rapid acetylators are associated with a higher risk of therapeutic failure. This study investigates the association between the NAT2 acetylation phenotype and ATDH occurrence, with an emphasis on its predictive value in regard to a multiethnic population and its impact on the timing of ATDH onset.

Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infarction.

Background: Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data from sources such as electronic health records (EHRs) and administrative databases are leveraged for signal detection. Although network analysis has shown promise in mapping relationships between clinical attributes for signal detection in spontaneous reporting system databases, its application in real-world data from EHRs and administrative databases remains limited.

NAT2 Slow Acetylator Phenotype as a Significant Risk Factor for Hepatotoxicity Caused by Antituberculosis Drugs: Results From a Multiethnic Nested Case-Control Study.

Background: Under standard therapies, the incidence of drug-induced liver injury (DILI) in patients with tuberculosis ranges from 2% to 28%. Numerous studies have identified the risk factors for antituberculosis DILI; however, none have been conducted in a multiethnic real-world setting. The primary outcome of the current study was to identify the risk factors that could be used as the best predictors of DILI in a multiethnic cohort.

Timing Matters: A Machine Learning Method for the Prioritization of Drug-Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure.

Introduction: Current drug-drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases.

Objective: This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis.

Methods: The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).

Exploring the impact of co-exposure timing on drug-drug interactions in signal detection through spontaneous reporting system databases: a scoping review.

Introduction: Drug-drug interactions (DDIs) are defined as the pharmacological effects produced by the concomitant administration of two or more drugs. To minimize false positive signals and ensure their validity when analyzing Spontaneous Reporting System (SRS) databases, it has been suggested to incorporate key pharmacological principles, such as temporal plausibility.

Areas Covered: The scoping review of the literature was completed using MEDLINE from inception to March 2023.

Comparing major and mild cognitive impairment risks in older type-2 diabetic patients: a Danish register-based study on dipeptidyl peptidase-4 inhibitors vs. glucagon-like peptide-1 analogues.

Introduction: The prevalence of major and mild cognitive impairment (CI) in type-2 diabetes older patients is 15-25% and 30-60%, respectively, thus affecting quality of life and health outcomes. There is, therefore, the need of head-to-head studies aiming at identifying the optimal treatment for individuals with type-2 diabetes at increased risk of mild and major CI. This study focuses on the risk of developing mild and major CI in Danish patients treated with dipeptidyl peptidase-4 inhibitors (DPP-4i) and glucagon-like peptide-1 analogues (GLP-1a) using administrative and healthcare registers.

Evaluating the 6-month formulation of paliperidone palmitate: a twice-yearly injectable treatment for schizophrenia in adults.

Introduction: Paliperidone Palmitate is the only antipsychotic that has been developed in three different intramuscular long-acting injectable (LAI) dosing regimen: monthly (PP1M), quarterly (PP3M), and from 2020 also twice-yearly (PP6M). The latter was approved for the maintenance treatment of adults with schizophrenia and clinically stabilized with PP1M or PP3M.

Areas Covered: Data from studies evaluating efficacy in the maintenance treatment of schizophrenia with PP6M are reviewed.

Artificial intelligence for the optimal management of community-acquired pneumonia.

Purpose Of Review: This timely review explores the integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management, emphasizing its relevance in predicting the risk of hospitalization. With CAP remaining a global public health concern, the review highlights the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes.

Recent Findings: Challenges in CAP management delve into the application of AI in predicting CAP-related hospitalization risks, and complications, and mortality.

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.

Barriers to the use of three-month Paliperidone Palmitate formulation: a study from an Italian real-world setting.

Background: Long-acting injectable paliperidone can improve adherence in psychotic patients and reduce relapses and healthcare resource utilization (HRU). This study compares the effectiveness of the three-monthly paliperidone palmitate (PP3M) with the one-monthly formulation (PP1M) and investigates reasons that hinder the use of PP3M in real-world settings.

Methods: The authors conducted a three-phase observational study.

The potential effect of metformin on cognitive and other symptom dimensions in patients with schizophrenia and antipsychotic-induced weight gain: a systematic review, meta-analysis, and meta-regression.

Introduction: Metformin has shown good efficacy in the management of antipsychotic-induced metabolic syndrome (MetS) in patients with schizophrenia or schizoaffective disorders. Its ability to induce antidepressant behavioural effects and improve cognitive functions has also been investigated: yet information has not been systematized. The aim of this study was therefore to investigate the effects of metformin on cognitive and other symptom dimension in schizophrenic patients treated with antipsychotics through a systematic review and meta-analysis.

Umbrella Review: Association Between Antipsychotic Drugs and Metabolic Syndrome Hallmarks in Children and Adolescents.

Objective: To summarize the available evidence on metabolic parameters indicating metabolic adverse effects and risk of metabolic syndrome in children and adolescents treated with antipsychotics, following a pre-specified protocol (PROSPERO ID 252336).

Method: We searched PubMed, Embase and PsycINFO until May 14, 2021, to identify systematic reviews (SR), meta-analyses (MA) and network meta-analyses (NMA) examining symptoms associated to metabolic syndrome in patients <18 years of age who required treatment with oral antipsychotic drugs. Evidence from quantitative analyses for all outcomes related to anthropometric, glyco-metabolic, and blood pressure parameters (measured from baseline to intervention-end and/or follow-up, in subjects exposed to antipsychotics and placebo) was reported on the basis of their metrics (median difference [medianD], mean difference [MD], standardized mean difference [SMD], odds ratio [OR], risk ratio ([RR]).

Ubrogepant and rimegepant: signal detection using spontaneous reports of adverse events from the Food and Drug Administration Adverse Event Reporting System.

Background: In this study, we fill this gap in knowledge by updating the safety profile of ubrogepant and rimegepant via disproportionality analysis in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), a US-based database registering spontaneous reports.

Research Design And Methods: ASCII files of quarterly extraction of FAERS data were downloaded from the FDA website up to the 3 quarter (Q3) of 2021 (last accessed 03/02/2022). Disproportionality analysis was done using the Reporting Odds Ratio (ROR) as a disproportionality measure.

A novel approach for pharmacological substantiation of safety signals using plasma concentrations of medication and administrative/healthcare databases: A case study using Danish registries for an FDA warning on lamotrigine.

PHARMACOM-EPI is a novel framework to predict plasma concentrations of drugs at the time of occurrence of clinical outcomes. In early 2021, the U.S.

Psychiatric and non-psychiatric drugs causing false-positive amphetamines urine test in psychiatric patients: a pharmacovigilance analysis using FAERS.

Introduction: Immunoassay urine drug screen (UDS) is frequently used in clinical practice for initial screening process, being generally available, fast, and inexpensive. Exposure to widely prescribed drugs might determine false-positive UDS amphetamines, leading to diagnostic issues, wrong therapeutic choices, impairment of physician-patient relationship, and legal implications.

Areas Covered: To summarize and comment on a comprehensive list of compounds responsible for UDS false positives for amphetamines, we conducted a literature review on PubMed along with a comparison with Real-World Data from the Food and Drug Administration Adverse Event Reporting System (FAERS) database analysis between 2010 and 2022.

On the potential of drug repurposing in dysphagia treatment: New insights from a real-world pharmacovigilance study and a systematic review.

Polypharmacy is common in patients with dysphagia. Routinely used drugs may influence swallowing function either improving or worsening it. We aimed to explore the potential effects of three commonly used drug classes on dysphagia and aspiration pneumonia through a systematic review and a real-world data analysis to probe the possibility of drug repurposing for dysphagia treatment.

The potential antidepressant effect of antidiabetic agents: New insights from a pharmacovigilance study based on data from the reporting system databases FAERS and VigiBase.

Growing evidence supports a bidirectional association between diabetes and depression; promising but limited and conflicting data from human studies support the intriguing possibility that antidiabetic agents may be used to relieve effectively depressive symptoms in diabetic patients. We investigated the potential antidepressant effects of antidiabetic drugs in a high-scale population data from the two most important pharmacovigilance databases, , the FDA Adverse Event Reporting System (FAERS) and the VigiBase. From the two primary cohorts of patients treated with antidepressants retrieved from FDA Adverse Event Reporting System and VigiBase we identified (depressed patients experiencing therapy failure) and (depressed patients experiencing any other adverse event).

Exploring the underlying mechanisms of drug-induced impulse control disorders: a pharmacovigilance-pharmacodynamic study.

Introduction: Impulse control disorders (e.g. pathological gambling, hypersexuality) may develop as adverse reactions to drugs.