Drug Safety最新文献

Introduction: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents.

Objective: Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes.

Results: Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs.

Conclusion: The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.

Background and objective: Missing data and unmeasured confounding are key challenges for external comparator studies. This work evaluates bias and other performance characteristics depending on missingness and unmeasured confounding by means of two case studies and simulations.

Methods: Two case studies were constructed by taking the treatment arms from two randomised controlled trials and an external real-world data source that exhibited substantial missingness. The indications of the randomised controlled trials were multiple myeloma and metastatic hormone-sensitive prostate cancer. Overall survival was taken as the main endpoint. The effects of missing data and unmeasured confounding were assessed for the case studies by reporting estimated external comparator versus randomised controlled trial treatment effects. Based on the two case studies, simulations were performed broadening the settings by varying the underlying hazard ratio, the sample size, the sample size ratio between the experimental arm and the external comparator, the number of missing covariates and the percentage of missingness. Thereby, bias and other performance metrics could be quantified dependent on these factors.

Results: For the multiple myeloma external comparator study, results were in line with the randomised controlled trial, despite missingness and potential unmeasured confounding, while for the metastatic hormone-sensitive prostate cancer case study missing data led to a low sample size, leading overall to inconclusive results. Furthermore, for the metastatic hormone-sensitive prostate cancer study, missing data in important eligibility criteria led to further limitations. Simulations were successfully applied to gain a quantitative understanding of the effects of missing data and unmeasured confounding.

Conclusions: This exploratory study confirmed external comparator strengths and limitations by quantifying the impact of missing data and unmeasured confounding using case studies and simulations. In particular, missing data in key eligibility criteria were seen to limit the ability to derive the external comparator target analysis population accurately, while simulations demonstrated the magnitude of bias to expect for various settings.

Introduction: The safety of antiviral agents in real-world clinical settings is crucial, as pre-marketing studies often do not capture all adverse events (AE). Active pharmacovigilance strategies are essential for detecting and characterising these AE comprehensively.

Objective: The aim of this study was to identify and characterise active pharmacovigilance strategies used in real-world clinical settings for patients under systemic antiviral agents, focusing on the frequency of AE and the clinical data sources used.

Methods: We conducted a systematic review by searching three electronic bibliographic databases targeting observational prospective active pharmacovigilance studies, phase IV clinical trials for post-marketing safety surveillance, and interventional studies assessing active pharmacovigilance strategies, focusing on individuals exposed to systemic antiviral agents.

Results: We included 36 primary studies, predominantly using Drug Event Monitoring (DEM), with a minority employing sentinel sites and registries. Human immunodeficiency virus (HIV) was the most common condition, with the majority using DEM. Within the DEM, there was a wide range of incidences of patients experiencing at least one AE, and most of these studies used one or two data sources. Sentinel site studies were less common, with two on hepatitis C virus (HCV) and one on HIV, each relying on one or two data sources. The single study using a registry focusing on HIV therapy reported using just one data source. Patient interviews were the most common data source, followed by medical records and laboratory tests. The quality of the studies was considered 'good' in 18/36, 'fair' in 1/36, and 'poor' in 17/36 studies.

Conclusion: DEM was the predominant pharmacovigilance strategy, employing multiple data sources, and appears to increase the likelihood of detecting higher AE incidence. Establishing such a framework would facilitate a more detailed and consistent approach across different studies and settings.

Introduction: Impulsivity induced by dopaminergic agents, like pramipexole and aripiprazole, can lead to behavioral addictions that impact on social functioning and quality of life of patients and families (e.g., resulting in unemployment, marital problems, anxiety). These secondary effects, interconnected in networks of signs and symptoms, are usually overlooked by clinical trials, not reported in package inserts, and neglected in clinical practice.

Objective: This study explores the syndromic burden of impulsivity induced by pramipexole and aripiprazole, pinpointing key symptoms for targeted mitigation.

Methods: An event-event Information Component (IC) on the FDA Adverse Event Reporting System (FAERS) (January 2004 to March 2022) identified the syndrome of events disproportionally co-reported with impulsivity, separately for pramipexole and aripiprazole. A greedy-modularity clustering on composite network analyses (positive pointwise mutual information [PPMI], Ising, Φ) identified sub-syndromes. Bayesian network modeling highlighted possible precipitating events.

Results: Suspected drug-induced impulsivity was documented in 7.49% pramipexole and 4.50% aripiprazole recipients. The highest IC concerned obsessive-compulsive disorder (reporting rate = 26.77%; IC median = 3.47, 95% confidence interval [CI] = 3.33-3.57) and emotional distress (21.35%; 3.42, 3.26-3.54) for pramipexole, bankruptcy (10.58%; 4.43, 4.26-4.55) and divorce (7.59%; 4.38, 4.19-4.53) for aripiprazole. The network analysis identified delusional jealousy and dopamine dysregulation sub-syndromes for pramipexole, obesity-hypoventilation and social issues for aripiprazole. The Bayesian network highlighted anxiety and economic problems as potentially precipitating events.

Conclusion: The under-explored consequences of drug-induced impulsivity significantly burden patients and families. Network analyses, exploring syndromic reactions and potential precipitating events, complement traditional techniques and clinical judgment. Characterizing the secondary impact of reactions will support informed patient-centered decision making.

Introduction: Paracetamol dosing errors can cause acute liver injury, with potentially toxic doses only slightly above the therapeutic range. This study aimed to characterise unintentional paracetamol overdose reported to an Australian poisons centre, including time trends, demographics, types of dosing errors, and outcomes.

Methods: Records regarding paracetamol dosing errors for individuals aged ≥12 years were extracted from the New South Wales Poisons Information Centre database, January 2017 to June 2023. Data from 2021 underwent an in-depth screening of free text case notes to examine: dose, duration, products involved, reasons for ingestion and outcomes including hospitalisation, treatment, liver transplantations and deaths. Where possible, complete outcome data were obtained from medical records of New South Wales hospitalised cases in 2021.

Results: There were 14,380 exposures due to paracetamol dosing errors (predominantly self-administered, median age 43 years, 62.6% female), with an average yearly increase of 2.5% (95% CI 1.6-3.8%; p < 0.0001). The in-depth analysis of exposures recorded during 2021 revealed 1899 exposures (median age 46 years, 63.4% female) with 26.8% requiring hospitalisation. Immediate- and modified-release formulations were highly implicated. Multiple paracetamol-containing products were ingested in approximately 20% of exposures. Hospitalised exposures were associated with paracetamol use for dental pain and ingested higher doses for longer durations. Over half of those hospitalised (52%) were treated with the antidote (N-acetylcysteine), and 6% of exposures developed hepatotoxicity.

Conclusion: Paracetamol dosing errors continue to occur, with relatively high rates of hospitalisation and liver injury. Many hospitalisations involved use for dental pain. Possible preventative measures include ingredient name prominence and increased education on appropriate dosing.

Introduction: A risk factor for a potentially fatal ventricular arrhythmia Torsade de Pointes is a prolongation in the heart rate-corrected QT interval (QTc) ≥ 500 milliseconds (ms) or an increase of ≥ 60 ms from a patient's baseline value, which can cause sudden cardiac death. The Tisdale risk score calculator uses clinical variables to predict which hospitalized patients are at the highest risk for QTc prolongation.

Objective: To determine the rate of overridden QTc drug-drug interaction (DDI)-related clinical decision support (CDS) alerts per patient admission and the prevalence by Tisdale risk score category of these overridden alerts. Secondary outcome was to determine the rate of drug-induced QTc prolongation (diQTP) associated with overrides.

Methods: Our organization's enterprise data warehouse was used to retrospectively access QTc DDI alerts presented for patients aged ≥ 18 years who were admitted to Brigham and Women's Hospital during 2022. The QTc DDI CDS alerts were included if shown to a physician, fellow, resident, physician assistant, or nurse practitioner when entering the order in inpatient areas for patients with a length of stay of at least 2 days. Variables collected for the Tisdale calculator included age, sex, whether patient was on a loop diuretic, potassium level, admission QTc value, admitting diagnosis of acute myocardial infarction, sepsis, or heart failure, and number of QTc-prolonging drugs given to the patient.

Results: A total of 2649 patients with 3033 patient admissions had 18,432 QTc DDI alerts presented that were overridden. An average of 3 unique QTc DDI alerts were presented per patient admission and the alerts were overridden an average of 6 times per patient admission. Overall, 6% of patient admissions were low risk (score ≤ 6), 64% moderate risk (score 7-10), and 30% high risk (score ≥ 11) of QTc prolongation. The most common QTc DDI alerts overridden resulting in an diQTP were quetiapine and propofol (11%) and amiodarone and haloperidol (7%). The diQTP occurred in 883 of patient admissions (29%) and was more frequent in those with higher risk score, with 46% of patient admissions with diQTP in high risk, 23% in moderate risk, and 8% in low risk.

Conclusion: Use of the Tisdale calculator to assess patient-specific risk of QT prolongation combined with CDS may improve overall alert quality and acceptance rate, which may decrease the diQTP rate.

Continuous professional development among stakeholders involved in drug safety monitoring and surveillance is imperative in strengthening pharmacovigilance (PV) systems. The "Pharmacovigilance infrastructure and post-marketing surveillance system capacity building for regional medicine regulatory harmonization in East Africa" (PROFORMA) project aims to enhance the national PV infrastructure, post-marketing surveillance systems and clinical trial regulatory capabilities in Ethiopia, Tanzania, Kenya and Rwanda. To achieve this, training, including short-term training (STT) activities, at various levels is required. This article aims to describe the experiences of the authors during the development and implementation of STT in an attempt to improve the PV training landscape of these countries. To identify gaps, a baseline assessment of PV teaching and practices at the national medicines regulatory authorities (NMRAs) and medical universities was conducted. Five successive training sessions, tailored to each country's specific needs and regulatory environments, were conducted; three focusing on fundamental concepts in PV and two dedicated to training-of-trainers courses. The training targeted staff from PV units of the NMRAs and medical universities. Enabling participation from all four countries in the same training fostered cross-country learning and collaboration. The contribution of STT to university education and the operational methodologies within NMRAs are explored, showcasing the impact on knowledge transfer and skill development in each country. In conclusion, by investing strategically in STT activities and fostering partnerships with academic institutions and NMRAs, we demonstrated a sustainable approach to PV capacity strengthening in resource-limited settings. The success of this model underscores its potential for adoption and replication across the African continent, offering a valuable framework for strengthening drug safety regulation and ultimately protecting public health.

Introduction: Preventing prenatal exposure to teratogenic medications is an important goal of regulatory risk mitigation efforts. In the USA, as of March 2024, 11 teratogenic medications have a required Risk Evaluation and Mitigation Strategy (REMS) program. It is unclear whether these programs target those medications with the most significant impact on public health and adverse pregnancy outcomes.

Objectives: This study aims to develop an innovative decision support tool that uses explicit, quantifiable criteria to facilitate prioritization of teratogenic medications for risk mitigation strategies.

Methods: The Teratogenic Risk Impact and Mitigation (TRIM) decision support tool will be developed by a national panel via a modified Delphi approach to define measurable criteria, and a multi-criteria decision analysis to estimate criteria weights within a discrete choice experiment. The TRIM scores will then be calculated for 12 teratogenic drugs with active or eliminated REMS programs and for 12 teratogenic drugs without REMS. These drugs will be identified based on highest prenatal exposure prevalence in claims data of privately and publicly insured individuals. Data for the TRIM criteria levels for these 24 drugs will be identified from evidence searches and ad hoc analyses of the same claims data.

Conclusions: Teratogenic Risk Impact and Mitigation is intended to inform regulatory decision making about the need for risk mitigation programs for teratogenic medications by providing explicit, quantifiable, evidence-based criteria. The TRIM scores of 24 teratogenic drugs may provide benchmarks for considering REMS for marketed and new teratogenic medications.