Therapeutic Advances in Drug Safety最新文献

Background: In tissue regenerative trials, investigators operate in the intersection of routine clinical care and research. Often, clinical trials are the only option to introduce innovative treatments to disadvantaged populations in safety-net hospitals (SNHs). It is necessary to maintain a balance between efficient study conduct and patient safety. Social determinants play a role in medication adherence in clinical trials and chronic disease management that may increase risks if not managed correctly.

Objectives: We aimed to assess the safety of patients in tissue regenerative clinical trials while examining underlying causes to develop proactive risk mitigation strategies for high-risk patients.

Design: A single-center, retrospective study was conducted for clinical trials with tissue regenerative products for chronic wounds at an SNH.

Methods: Data obtained from 186 subjects were analyzed retrospectively for correlation between social determinants of health and adverse events by using Spearman correlation and Kruskal-Wallis tests.

Results: Wound healing was achieved in 41.94% of patients who received investigational products. Overall, the diabetic foot ulcer group was noted to have a higher prevalence of serious adverse events (SAEs; 22.8% of enrolled subjects) and adverse events (78.3% of enrolled subjects) as compared to the venous stasis ulcer group, with 12.4% of SAEs and 71.0% adverse events observed in the study population. Kruskal-Wallis test demonstrated statistically significant correlation between polypharmacy (⩾5 drugs) and a higher number of adverse events (p = 0.0016). A Spearman correlation test showed that a higher number of comorbidities was associated with a higher number of adverse events (p = 0.0007).

Conclusion: These findings in polypharmacy and comorbidities being associated with a higher number of adverse events highlighted the importance of safety monitoring of patients with high disease burden in clinical trials. Understanding the frequency/types of adverse events can provide important insights for those conducting trials in a particular indication. In addition, monitoring can help to address social determinants that contribute to higher numbers of adverse events, and proactively address disease burden with appropriate medical management to minimize risks in tissue regenerative clinical trials.

Background: Potentially inappropriate medications (PIMs) are common among older adults with chronic diseases and are linked to adverse outcomes, including hospitalization. Evidence on PIM prevalence and its clinical impact in Ethiopia is limited. This study assessed the prevalence of PIM use and its association with hospitalization among older adults in the Amhara Region, Ethiopia.

Objectives: To determine the prevalence of PIM use and identify factors associated with PIM exposure and hospitalization in older adults with chronic diseases.

Design: Multicenter prospective cohort study.

Methods: Between May 1 and November 30, 2024, 1700 adults aged ⩾60 years were enrolled from five comprehensive specialized hospitals in the Amhara region. PIM use was assessed using the 2023 American Geriatrics Society Beers criteria. Sociodemographic, clinical, medication, and hospitalization data were collected via structured interviews and medical chart reviews. Multivariable logistic regression identified factors independently associated with PIM use and hospitalization.

Results: PIM use was identified in 41.1% of participants. Exposure to PIMs significantly increased the risk of hospitalization (adjusted odds ratio (AOR) = 3.70, 95% confidence interval (CI): 2.25-4.95, p = 0.023). Independent predictors of PIM use included khat chewing (AOR = 1.95), cor pulmonale (AOR = 2.28), degenerative diseases (AOR = 3.20), Charlson Comorbidity Index >4 (AOR = 4.50), prolonged chronic illness (AOR = 3.07), benzodiazepine use (AOR = 1.80), and concurrent benzodiazepine-opioid use (AOR = 4.02). Regular medication reviews were protective, reducing the risk of PIM use (AOR = 0.55).

Conclusion: PIM use is highly prevalent among older adults with chronic diseases in the Amhara Region and is associated with increased hospitalization risk. Systematic medication reviews and improved prescribing practices are essential to enhance medication safety and reduce preventable hospital admissions.

Background: Appropriate deprescribing of psychotropic, sedative, and anticholinergic potentially inappropriate medication (PSA-PIM) in older adults with polypharmacy can reduce the risk of adverse drug reactions, but is inconsistently implemented in primary care. The PARTNER intervention was designed to address challenges in PSA-PIM deprescribing at both provider and patient levels.

Objectives: To evaluate the effectiveness and cost-effectiveness of the PARTNER intervention, and to understand the mechanisms of its effects.

Design: Multicenter, two-arm cluster-randomized controlled trial.

Methods and analysis: The study aims to recruit at least 44 clusters and 352 patients (⩾65 years old with polypharmacy (⩾5 drugs) and use of ⩾1 PSA-PIM for ⩾6 months) across three study sites in Germany. Clusters consist of one general practice and one or more community pharmacies, randomly allocated to either the PARTNER intervention or control group. The PARTNER intervention includes: (A) education for general practitioners (GPs) and pharmacists on PSA-PIM deprescribing, (B) an interprofessional workshop, (C) drug-specific empowerment brochures for patients, (D) a patient-pharmacist consultation to enhance patient empowerment, and (E) a GP-patient consultation focusing on shared decision-making. The control group receives enhanced usual care, comprising a one-off patient-pharmacist consultation for medication safety checks without a specific focus on PSA-PIM deprescribing. The intervention's focus on PSA-PIM deprescribing is blinded to control group clusters throughout the study. The primary endpoint is a reduction in PSA-PIM exposure at 6 months (⩾0.15-point decrease in the Drug Burden Index). Secondary endpoints include falls, quality of life, healthcare utilization, and costs. The primary analysis will use a generalized linear mixed model to estimate the odds ratio for achieving the primary endpoint, adjusting for study center, age, sex, and pre-randomization PSA-PIM type and count. The process evaluation will explore the understanding of how and why the intervention succeeded or failed.

Discussion: The PARTNER trial will provide evidence on the intervention's effectiveness, efficiency, and appropriateness, informing its potential for broader implementation.

Trial registration: The trial has been registered with ClinicalTrials.gov (NCT05842928) on May 6, 2023; https://clinicaltrials.gov/search?term=NCT05842928.

Background: The Food and Drug Administration (FDA) authorized three COVID-19 antivirals, including remdesivir, nirmatrelvir/ritonavir, and molnupiravir. Although medication errors involving these treatments have been reported, national-level evidence on their frequency and clinical impact remains limited.

Objectives: This study aimed to evaluate medication errors and associated serious clinical outcomes linked to FDA-approved COVID-19 antivirals using national postmarketing safety data.

Design: A retrospective pharmacovigilance study using postmarketing adverse event reports.

Methods: We analyzed data from the FDA Adverse Event Reporting System (FAERS) from January 2020 to December 2024. COVID-19 antivirals were identified using both their generic and brand names. Medication errors were identified using MedDRA preferred terms categorized under "medication errors and other product use errors." Serious outcomes included death, hospitalization, life-threatening conditions, disability, required medical intervention to prevent permanent harm, and other clinically significant events. Descriptive analyses summarized the report characteristics. Disproportionality analysis was performed using reporting odds ratios (RORs) with 95% confidence intervals (CIs) to evaluate associations between antivirals and medication errors, as well as between medication errors and serious outcomes.

Results: Among 10,768 medication error reports involving COVID-19 antivirals, nirmatrelvir/ritonavir accounted for the highest number of reports, while molnupiravir had the highest proportion of medication errors relative to total reports. Remdesivir (ROR = 0.50, 95% CI: 0.48-0.53) and nirmatrelvir/ritonavir (ROR = 0.86, 95% CI: 0.84-0.88) were associated with lower odds of medication errors, whereas molnupiravir showed significantly increased odds (ROR = 3.98, 95% CI: 3.77-4.21). Medication errors were significantly associated with serious outcomes, including death (ROR = 1.31, 95% CI: 1.19-1.46), life-threatening events (ROR = 1.38, 95% CI: 1.21-1.57), and required interventions to prevent permanent harm (ROR = 3.84, 95% CI: 2.58-5.72).

Conclusion: Medication errors involving COVID-19 antivirals remain a safety concern, particularly with molnupiravir. Errors were associated with serious outcomes, highlighting the need for targeted safety interventions in prescribing and dispensing practices to reduce preventable harm.

The COVID-19 pandemic has renewed attention to complex chronic health conditions that challenge conventional biomedical paradigms. Syndromes such as postural orthostatic tachycardia syndrome and myalgic encephalomyelitis/chronic fatigue syndrome have gained broader visibility through the lens of Long COVID. As global vaccination campaigns expanded, a subset of individuals began reporting similarly persistent, multisystem symptoms following COVID-19 immunization-informally referred to as post-COVID-19 vaccination syndrome. These presentations, which include dysautonomia, neuropathic pain, post-exertional malaise, and cognitive dysfunction, resemble post-infectious syndromes and may involve shared immune-related mechanisms. Although no causal relationship to vaccination has been established, these cases-together with comparable reports following other vaccines-highlight limitations in current vaccine safety systems for detecting and evaluating complex chronic outcomes. This article introduces the concept of complex chronic adverse events following immunization (CC-AEFIs) as a pragmatic, surveillance-oriented framework to support the systematic identification and investigation of such cases. CC-AEFIs are not syndromic diagnoses but a higher-order category encompassing persistent, multifactorial conditions that may follow immunization yet challenge existing pharmacovigilance definitions and tools. These conditions often involve multiple organ systems, delayed onset, fluctuating trajectories, diagnostic ambiguity, and symptom heterogeneity. Drawing on the author's lived experience as an affected patient and integrating clinical, regulatory, and experiential evidence, the analysis examines structural and epistemic limitations across the pharmacovigilance continuum-from underrecognition in clinical settings to analytic exclusion and constrained governance. It concludes by proposing reforms to strengthen safety-system responsiveness, including enhanced diagnostic training, longitudinal surveillance, patient-reported outcome integration, and analytic transparency. Addressing these limitations is essential to sustain public trust, ensure equitable care, and uphold the scientific integrity of immunization programs.

The advent of generative artificial intelligence (GenAI) has introduced both remarkable opportunities and significant challenges in the field of pharmacovigilance (PV). This perspective review reflects on emerging trends, practical use cases, and conceptual frameworks shaping the integration of GenAI in high-risk domains such as drug and vaccine safety monitoring. We draw on current experiments and early real-world applications to examine the potential benefits, inherent risks, and propose a framework for integrating GenAI into PV systems, emphasizing the necessity of rigorous testing, human oversight, and ethical considerations. Our goal is to support PV professionals and stakeholders in navigating this rapidly evolving landscape by identifying promising strategies and implementation pathways.

Background: Adverse drug reactions (ADRs) are harmful side effects of medications. Social media provides real-time, patient-generated data, though its unstructured format presents challenges. Natural language processing and transfer learning offer promising solutions.

Objective: This study aimed to evaluate whether transformer-based models fine-tuned on a general ADR dataset can effectively classify ADRs from tweets related to glucagon-like peptide-1 (GLP-1) receptor agonists and to benchmark their performance against state-of-the-art large language models (LLMs).

Design: This study employed a machine learning approach using transformer-based language models to classify ADRs in social media.

Methods: BERT (bidirectional encoder representations from transformers)-base, BERTweet-base, and GPT-2 (Generative Pre-Trained Transformer-2) models were fine-tuned using Sarker and SIDER (Side Effect Resource) datasets for ADR classification. The test dataset comprised 396 tweets mentioning GLP-1 receptor agonists that were categorized as personal experiences. Model performance was primarily evaluated using the F1 score, which was used to select the optimal model. In addition, the fine-tuned transformer models were benchmarked against state-of-the-art LLMs, including ChatGPT 4o, ChatGPT 4o-mini, and Gemini 2.5 Flash.

Results: Among 396 tweets, 116 (29.3%) were classified as ADRs and 280 (70.7%) as non-ADRs. Among the transformer-based models, BERTweet-base achieved the highest performance (accuracy: 0.835, F1: 0.729), outperforming both BERT-base (accuracy: 0.826, F1: 0.679) and GPT-2 (accuracy: 0.766, F1: 0.628). Among the LLMs, ChatGPT 4o-mini demonstrated the best results (accuracy: 0.970, F1: 0.948), followed by Gemini 2.5 Flash (accuracy: 0.954, F1: 0.919) and ChatGPT 4o (accuracy: 0.936, F1: 0.895). Overall, LLMs substantially outperformed the fine-tuned transformer-based models.

Conclusion: Fine-tuned transformer-based models demonstrated reasonable performance in ADR detection from GLP-1 receptor agonist tweets, with BERTweet-base performing best. However, state-of-the-art LLMs, particularly ChatGPT 4o-mini, substantially outperformed these models, highlighting their potential for pharmacovigilance tasks.

Background: Baricitinib is widely used for immune-mediated diseases, yet real-world safety in underrepresented age groups and the temporal dynamics of adverse drug reactions (ADRs) remain insufficiently characterized.

Objective: To identify age-stratified ADR signals of baricitinib and to examine potential causal roles of Janus kinase (JAK) 1/2 inhibition in key ADRs.

Design: A retrospective pharmacovigilance study integrating disproportionality analysis, Mendelian randomization (MR), and time-to-onset (TTO) assessment.

Methods: Baricitinib-associated ADRs reported to the FDA Adverse Event Reporting System (FAERS; Q3-2018 to Q1-2024) were analyzed using Reporting Odds Ratio and Bayesian Confidence Propagation Neural Network, stratified by age (18-65 vs ≥66 years). TTO was modeled to characterize temporal patterns. Two-sample MR using eQTL-based instruments of JAK1/2 expression evaluated causal links with thrombosis and atrial fibrillation (AF).

Results: Among 5354 reports, infections were most frequent (28.3%). Thrombotic events (deep vein thrombosis, pulmonary embolism) were more prominent in the elderly (≥66 years), whereas hepatic enzyme elevation and malignancies were more frequent in adults aged 18-65 years. MR suggested that higher JAK2 expression was protective against thrombosis (OR = 0.998, p = 0.028), whereas higher JAK1 expression conferred increased risk of AF (OR = 1.255, p = 0.043). TTO analysis showed that thrombotic ADRs tended to occur early after baricitinib initiation, whereas certain malignancies emerged later.

Conclusion: This study highlights distinct age-dependent vulnerabilities to baricitinib-associated ADRs, with genetic evidence suggesting target-specific mechanisms underlying cardiovascular risk. These findings underscore the importance of age-tailored monitoring strategies and proactive pharmacovigilance in clinical practice.

Background: Patients with type 2 diabetes (T2DM) are at risk of developing urinary tract infections (UTIs). Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are a common medication associated with UTIs in these patients. However, emerging data show that other medications may be more frequently prescribed prior to UTI diagnosis.

Objectives: Explore the correlation of newly prescribed medications in patients with the diagnosis of T2DM prior to an incidence of UTI and compare it to those without a UTI.

Design: This observational case-control study aimed to explore the correlation between the incidence of UTIs in patients with T2DM and new prescription medication fills.

Methods: Data were retrieved from national prescription and medical claims database IQVIA PharMetric® Plus for Academics between 2018 to 2021. The exposed cohort included patients with T2DM and an encounter for UTI. The comparator cohort was developed using propensity score matching and consisted of patients with T2DM and a health care encounter, but without a diagnosis of UTI.

Results: A total of 31,746 patients met study criteria, with 15,873 in both the exposed and matched comparator cohorts. The medications with the largest percentage point difference were opioids at 3.70 (p-value <0.001), statins at 3.42 (p-value <0.001), amoxicillin at 2.48 (p-value <0.001), metformin at 2.45 (p-value <0.001), and PPIs at 2.19 (p-value <0.001). SGLT2i were the 19th most prescribed medication class.

Conclusion: Opioids, statins, amoxicillin, metformin, and PPIs were the top 5 medications prescribed prior to the UTI event based on percentage point difference. SGLT2i were not in the top 10 medications initiated prior to UTI. This adds to existing literature that other new start medications may be correlated with a higher risk of developing a UTI such as opioids and PPIs than SGLT2 inhibitors in patients with T2DM.

Background: There is a rising incidence of neurological adverse events (AEs), such as seizures, associated with novel anticancer agents, warranting investigation. Large-scale studies assessing seizure risk across diverse anticancer drug classes, particularly in breast cancer (BC), remain limited.

Objective: This study aimed to systematically evaluate the association between seizures and 14 novel anticancer agents used in BC treatment, compared with traditional chemotherapy, utilizing international pharmacovigilance databases.

Design: A large-scale, real-world pharmacovigilance study using data from the US FDA Adverse Event Reporting System (FAERS) and the Canada Vigilance Database (from Q1 2004 to Q1 2025).

Methods: Disproportionality analysis was employed to calculate reporting odds ratios (RORs) for identifying significant seizure AE signals. Signals were assessed at both the Standardised MedDRA Query and Preferred Term levels. Pan-cancer transcriptomic data from The Cancer Genome Atlas were integrated to explore biological pathways correlated with drug-induced seizures.

Results: Significant and consistent seizure signals were identified for five agents-Lapatinib, Tucatinib, Trastuzumab, Trastuzumab Emtansine (T-DM1), and Atezolizumab-across both databases. In FAERS, over 50% of seizures occurred after 100 days of treatment (median: 68 days); however, fatal cases exhibited a significantly shorter median onset time. Novel agents demonstrated disproportionately higher seizure reporting signals compared to traditional chemotherapy. Pan-cancer analysis revealed negative correlations between seizure RORs and pathways, including asthma and the pentose phosphate pathway.

Conclusion: This dual-database pharmacovigilance study identifies potential associations between seizures and five novel BC therapies, underscoring the need for vigilant monitoring during their clinical use.